Journal articles on the topic 'Training Posture'
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Su, Wei, and Jian Feng. "Research on Methods of Physical Aided Education Based on Deep Learning." Scientific Programming 2022 (May 9, 2022): 1–13. http://dx.doi.org/10.1155/2022/6447471.
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In order to better meet the training needs of sports and improve the standardization of sports training, an openpose-based sports posture estimation method and assisted training system are proposed, combining the basic structure and principle of openpose network. Firstly, the human posture estimation algorithm is constructed by combining with the openpose network; secondly, the overall framework, specific operation process, image acquisition, posture estimation, and other modules of the sports assistance system are designed in detail; finally, the openpose posture estimation method constructed above is validated. The results show that the value of the loss function obtained by the algorithm gradually stabilizes after 250 iterations. By using the COCO dataset as the training base and comparing it with the standard posture, it is found that the algorithm can correctly identify different badminton action postures, and the recognition rate can reach up to 94%. This shows that the algorithm is feasible and can be used for posture estimation and training of badminton sports movements.
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Bourahmoune, Katia, Karlos Ishac, and Toshiyuki Amagasa. "Intelligent Posture Training: Machine-Learning-Powered Human Sitting Posture Recognition Based on a Pressure-Sensing IoT Cushion." Sensors 22, no. 14 (July 17, 2022): 5337. http://dx.doi.org/10.3390/s22145337.
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We present a solution for intelligent posture training based on accurate, real-time sitting posture monitoring using the LifeChair IoT cushion and supervised machine learning from pressure sensing and user body data. We demonstrate our system’s performance in sitting posture and seated stretch recognition tasks with over 98.82% accuracy in recognizing 15 different sitting postures and 97.94% in recognizing six seated stretches. We also show that user BMI divergence significantly affects posture recognition accuracy using machine learning. We validate our method’s performance in five different real-world workplace environments and discuss training strategies for the machine learning models. Finally, we propose the first smart posture data-driven stretch recommendation system in alignment with physiotherapy standards.
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Jeng, Po-Yuan, Li-Chun Wang, Chaur-Jong Hu, and Dean Wu. "A Wrist Sensor Sleep Posture Monitoring System: An Automatic Labeling Approach." Sensors 21, no. 1 (January 2, 2021): 258. http://dx.doi.org/10.3390/s21010258.
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In the hospital, a sleep postures monitoring system is usually adopted to transform sensing signals into sleep behaviors. However, a home-care sleep posture monitoring system needs to be user friendly. In this paper, we present iSleePost—a user-friendly home-care intelligent sleep posture monitoring system. We address the labor-intensive labeling issue of traditional machine learning approaches in the training phase. Our proposed mobile health (mHealth) system leverages the communications and computation capabilities of mobile phones for provisioning a continuous sleep posture monitoring service. Our experiments show that iSleePost can achieve up to 85 percent accuracy in recognizing sleep postures. More importantly, iSleePost demonstrates that an easy-to-wear wrist sensor can accurately quantify sleep postures after our designed training phase. It is our hope that the design concept of iSleePost can shed some lights on quantifying human sleep postures in the future.
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Takahira, Naonobu, Sho Kudo, Mako Ofusa, Kenta Sakai, Kouji Tsuda, Kiyoshi Tozaki, Yoshiki Takahashi, and Hiroaki Kaneda. "Effect of Devised Simultaneous Physical Function Improvement Training and Posture Learning Exercises on Posture." Healthcare 11, no. 9 (April 30, 2023): 1287. http://dx.doi.org/10.3390/healthcare11091287.
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Poor posture in young adults and middle-aged people is associated with neck and back pain which are among the leading causes of disability worldwide. Training posture maintenance muscles and learning about ideal posture are important for improving poor posture. However, the effect of using both approaches simultaneously has not been verified, and it is unclear how long the effects persist after the intervention. Forty female university students were randomly and evenly assigned to four groups: physical function improvement training, posture learning, combination, and control groups. Four weeks of intervention training was conducted. Postural alignment parameters were obtained, including trunk anteroposterior inclination, pelvic anteroposterior inclination, and vertebral kyphosis angle. Physical function improvement training for improving crossed syndrome included two types of exercises: “wall-side squatting” and “wall-side stretching”. The posture learning intervention consisted of two types of interventions: “standing upright with their back against the wall” and “rolled towel”. A multiple comparison test was performed after analysis of covariance to evaluate the effect of each group’s postural change intervention on postural alignment. Only the combination group showed an effective improvement in all posture alignments. However, it was found that a week after the 4-week intervention, the subjects’ postures returned to their original state.
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Kaplan, Defne Öcal. "Evaluating the Effect of 12 Weeks Football Training on the Posture of Young Male Basketball Players." Journal of Education and Training Studies 6, no. 10 (August 3, 2018): 47. http://dx.doi.org/10.11114/jets.v6i10.3423.
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Posture is the most healthy and correct placement of each region in the body when compared to the whole body. The predominantly use of one side of the body disrupts the posture. A bad posture changes the center of gravity of the body and causes chronic skeletal and muscle soreness. It is aimed to determine whether there is a rehabilitative effect of football training that does not require the use of dominant arms on posture asymmetries which may occur in the basketball which requires the use of dominant arm and leg in the study.15 male basketball players who played basketball for an average of 8 years with a mean age of 21.7 ± 1.5 years participated as volunteers. Postures of the subjects were measured with PostureScreen Mobile® before and after 12 week football trainings with lateral and anteriorly taken photographs. SPSS 22 was used program for statistical analysis. While analyzing of data Wilcoxon Test method was used and it is determined as significance level was p<0.05.After 12 weeks of football training, statistically significant differences were found in measurements taken from the anterior and lateral positions on the head, shoulder, ribcage, hip, and knee measured tilts and shifts. Estimated average head weight decreased depending on the posture of the cervical vertebrae and a significant difference was detected. Postures of subjects came close to the correct posture.Basketball is an acycle sport branch that requires the use of muscles on the dominant side. It creates an asymmetric position on the athlete due to this feature; causes shifting and tilts. In order to eliminate postural disorders that occur, the effectiveness of football training that does not require to use of the dominant side of the body has been demonstrated.
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Jeon, Chanhui, Haram Kim, and Dongsoo Kim. "A Deep-Learning-Based System for Pig Posture Classification: Enhancing Sustainable Smart Pigsty Management." Sustainability 16, no. 7 (March 29, 2024): 2888. http://dx.doi.org/10.3390/su16072888.
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This paper presents a deep-learning-based system for classifying pig postures, aiming to improve the management of sustainable smart pigsties. The classification of pig postures is a crucial concern for researchers investigating pigsty environments and for on-site pigsty managers. To address this issue, we developed a comprehensive system framework for pig posture classification within a pigsty. We collected image datasets from an open data sharing site operated by a public organization and systematically conducted the following steps: object detection, data labeling, image preprocessing, model development, and training. These processes were carried out using the acquired datasets to ensure comprehensive and effective training for our pig posture classification system. Subsequently, we analyzed and discussed the classification results using techniques such as Grad-CAM. As a result of visual analysis through Grad-CAM, it is possible to identify image features when posture is correctly classified or misclassified in a pig image. By referring to these results, it is expected that the accuracy of pig posture classification can be further improved. Through this analysis and discussion, we can identify which features of pig postures in images need to be emphasized to improve the accuracy of pig posture classification. The findings of this study are anticipated to significantly improve the accuracy of pig posture classification. In practical applications, the proposed pig posture classification system holds the potential to promptly detect abnormal situations in pigsties, leading to prompt responses. Ultimately, this can greatly contribute to increased productivity in pigsty operations, fostering efficiency enhancements in pigsty management.
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Silanon, Kittasil. "Thai Finger-Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/9026375.
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Hand posture recognition is an essential module in applications such as human-computer interaction (HCI), games, and sign language systems, in which performance and robustness are the primary requirements. In this paper, we proposed automatic classification to recognize 21 hand postures that represent letters in Thai finger-spelling based on Histogram of Orientation Gradient (HOG) feature (which is applied with more focus on the information within certain region of the image rather than each single pixel) and Adaptive Boost (i.e., AdaBoost) learning technique to select the best weak classifier and to construct a strong classifier that consists of several weak classifiers to be cascaded in detection architecture. We collected 21 static hand posture images from 10 subjects for testing and training in Thai letters finger-spelling. The parameters for the training process have been adjusted in three experiments, false positive rates (FPR), true positive rates (TPR), and number of training stages (N), to achieve the most suitable training model for each hand posture. All cascaded classifiers are loaded into the system simultaneously to classify different hand postures. A correlation coefficient is computed to distinguish the hand postures that are similar. The system achieves approximately 78% accuracy on average on all classifier experiments.
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Hossam, Asmaa, Fatma S. Amin, and Enas E. Abutaleb. "Effect of whole-body vibration on craniovertebral angle and balance control in forward head posture: Single-Blinded randomized controlled trial." Fizjoterapia Polska 21, no. 1 (March 30, 2021): 98–104. http://dx.doi.org/10.56984/8zg208142.
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Background. A fault posture of head can disturb the body balance. Forward head posture (FHP) is one of common faulty postures seen among university students. Whole Body Vibration (WBV) is a quick method of neuromuscular training used to increase muscle strength, improve dynamic balance control and eventually correct posture. Purpose. A randomized controlled trial was designed to investigate the effect of WBV training on craniovertebral angle and dynamic balance control in subjects with forward head posture. Methods. Forty-five participants (11 males and 34 females, 18-23 years old) were randomly allocated into 3 equal groups: group (A) received traditional treatment (stretching and strengthening exercises) + postural advices, group (B) received whole body vibration training + postural advices, group (C) received traditional treatment + whole body vibration training + postural advices, 3 sessions /week for 4 weeks. Outcome measures included craniovertebral angle (CVA), overall stability index (OSI), anteroposterior stability index (APSI) and mediolateral stability index (MLSI) that were assessed at baseline and 4 weeks post-intervention. Results. Comparing all groups post training revealed that there were statistically significant increases (p < 0.05) in all measured variables (CVA, OSI, APSI and MLSI) in favour of group (C), while there were statistically non-significant differences between group A & B (p > 0.05). Conclusion. The conjugation of WBV training with traditional treatment of FHP improved craniovertebral angle and dynamic balance control in subjects with forward head posture.
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Kim, Jongman, Bummo Koo, Yejin Nam, and Youngho Kim. "sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups." Sensors 21, no. 22 (November 18, 2021): 7681. http://dx.doi.org/10.3390/s21227681.
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Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly.
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Lu, Chi-Heng, Chiu-Ching Tuan, Yi-Chao Wu, Chi-Chuan Wu, Mei-Chuan Chen, Chin-Shiuh Shieh, and Tsair-Fwu Lee. "Evaluate the Medial Muscle Strength by Kick Training between the Standing and Sitting Postures." Applied Sciences 9, no. 4 (February 19, 2019): 718. http://dx.doi.org/10.3390/app9040718.
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In order to ameliorate the anterior knee pain or peripheral pain around the tibia caused by patellar lateral subluxation, we evaluated the kick training effects of standing or sitting postures in strengthening the vastus medialis obliquus (VMO) on the quadriceps femoris muscle. A total of 83 subjects (45 male; 38 female) in both sitting and standing positions performed 10° to 90° leg lift and kick training. Among the male group, the effect of the sitting posture was better than that of the standing posture, 74.31% of the former achieved the training goal. In the female group, the effect of a standing posture was better than that of a sitting posture, for which only 37.71% of the latter achieved the training purpose. However, a ratio of 84.34% in the female group showed that the strength of VMO on the quadriceps femoris muscle generated by leg kicking was greater than the strength generated by walking. While it was impossible to immediately achieve a greater effect of VMO on the quadriceps femoris muscle than vastus lateralis on the quadriceps femoris muscle, leg kicking did achieve the objective of enhancing the strength of VMO on the quadriceps femoris muscle.
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Guo, Hao, and Qi Hao. "Research on Aided Judgment of Rural Sports Posture Based on Deep Learning." Scientific Programming 2022 (March 4, 2022): 1–11. http://dx.doi.org/10.1155/2022/5916301.
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With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports auxiliary training, to get rid of the state of traditional sports training purely relying on experience. The article proposes a deep learning BP neural network human body posture recognition algorithm and briefly introduces the human body motion posture. The purpose of this research was to use the powerful data processing, mining, and analysis functions of deep learning to train the massive data generated in competitive sports training and apply it to competitive sports training. It is committed to promoting the accuracy and analysis of competitive sports training. Refinement provides technical guidance for athletes’ training, promotes the scientific and informatized development of competitive sports training in China, and provides some reference methods for the research and application of deep learning in competitive sports training. The article’s research results show that (1) taking a rural area as an example, we recorded the exercise postures of rural athletes in five different states: static, upstairs, downstairs, walking, and running. Comparing the recognition rate and training time of The BP neural network algorithm, ABC-BP algorithm, AFS-BP algorithm, and ABC-AFS-BP algorithm, it can be found that in terms of recognition rate, ABC-AFS-BP algorithm, AFS-BP algorithm, and ABC-BP algorithm are better than traditional BP algorithm. Among them, the recognition rate of the ABC-AFS-BP algorithm is higher than that of the ABC-BP algorithm, but it takes slightly more time than the ABC-BP algorithm. In terms of training time, the ABC-BP algorithm takes less time, but the accuracy is lower than the ABC-AFS-BP algorithm; the ABC-AFS-BP algorithm has a greater improvement in time consumption than the AFS-BP model and can guarantee the recognition rate and accuracy, and the error rate curves of the four algorithms show that after 500 iterations of the training part, the iteration error value of the ABC-AFS-BP algorithm is the smallest. (2) We evaluated sports postures of athletes from a certain rural team and concluded that bad postures will have a certain impact on the body. Among them, more than 85% of athletes in football and basketball have pelvic rotation. The problem is that football players have reached 90% of the test sample. 60% of football players and basketball players have the problem of collapsed foot. The main problem of aerobic athletes is flat back and collapsed foot. More than 90% of badminton players have high and low shoulder problems, and more than 80% of them have neck problems, which is a very serious body posture problem. (3) Detecting the flexibility experiment of the BP posture detection algorithm, compared with the traditional motion posture recording method, we tested from the three aspects of recording motion accuracy, missed detection rate, and recording time. The result shows BP posture detection. The missing detection rate of the algorithm is low, basically maintained at about 2.0, the accuracy of recording actions is relatively high, generally maintained above 98%, and the highest is 99.15%, and the recording time is short, maintained at 3–4 minutes; comparing traditional posture detection with the BP attitude detection algorithm, the missed detection rate of the algorithm is relatively high, kept at 4–6, the action accuracy is lower than that of the BP attitude detection algorithm, generally kept at about 95%, and the recording time is kept at 5–6 minutes. The posture detection algorithm is more efficient.
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Kim, Jongman, Sumin Yang, Bummo Koo, Seunghee Lee, Sehoon Park, Seunggi Kim, Kang Hee Cho, and Youngho Kim. "sEMG-Based Hand Posture Recognition and Visual Feedback Training for the Forearm Amputee." Sensors 22, no. 20 (October 19, 2022): 7984. http://dx.doi.org/10.3390/s22207984.
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sEMG-based gesture recognition is useful for human–computer interactions, especially for technology supporting rehabilitation training and the control of electric prostheses. However, high variability in the sEMG signals of untrained users degrades the performance of gesture recognition algorithms. In this study, the hand posture recognition algorithm and radar plot-based visual feedback training were developed using multichannel sEMG sensors. Ten healthy adults and one bilateral forearm amputee participated by repeating twelve hand postures ten times. The visual feedback training was performed for two days and five days in healthy adults and a forearm amputee, respectively. Artificial neural network classifiers were trained with two types of feature vectors: a single feature vector and a combination of feature vectors. The classification accuracy of the forearm amputee increased significantly after three days of hand posture training. These results indicate that the visual feedback training efficiently improved the performance of sEMG-based hand posture recognition by reducing variability in the sEMG signal. Furthermore, a bilateral forearm amputee was able to participate in the rehabilitation training by using a radar plot, and the radar plot-based visual feedback training would help the amputees to control various electric prostheses.
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İŞSEVER, İlker. "Posture management in vocal training." Journal for the Interdisciplinary Art and Education 1, no. 2 (2020): 63–84. http://dx.doi.org/10.29228/jiae.6.
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Leonard, Myer. "POSTURE TRAINING FOR TMD PATIENTS." Journal of the American Dental Association 131, no. 5 (May 2000): 559–60. http://dx.doi.org/10.14219/jada.archive.2000.0219.
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Babydov, Eugenii, Victoria Zaborova, Svetlana Tkachenko, Sergey Kotovskiy, Igor Erokhin, Anatoliy Fesyun, Dmitry Shestakov, Dilyana Vicheva, and Vladimir Stavrev. "STRENGTH TRAINING AND POSTURE ALIGHMENT." Journal of IMAB - Annual Proceeding (Scientific Papers) 29, no. 2 (June 14, 2023): 4974–79. http://dx.doi.org/10.5272/jimab.2023292.4974.
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Purposes: The aim of the study is to design and test a comprehensive technique based on strength training for posture correction. Materials and methods: This study was carried cohort of 37 men, aged 27,7±3,4 years old with kypholordotic posture. The program consisted of a set of exercises and exercises on a bicycle ergometer. The duration of the program was 16 weeks. Results were controlled by computer optical topography, motor tests, tests for strength endurance of abdominal muscles. For posture screening was used Posture Screen Mobile, motor tests, tests for strength endurance of abdominal muscles. Results: According to PSM, the indices of deviation of the lumbar spine decreased from 4,3 ± 0.5 to 2.0 ± 1,1 (p <0.01) mm, and indices of deviation of the shoulder decreased from 4,7 ± 0,7 to 1,8 ± 1,3 mm (p <0.01), and indices of deviation of the head decreased from 3,5 ± 0,9 to 2,9 ± 1,1 mm (p <0.05) The pressure force of the chest muscles after a study showed an increase in the strength capabilities of the muscles by 21,8 mm Hg. at p <0.01, of the flexor muscles of the shoulder joint in the abduction, showed an increase in strength capabilities by 16 mm Hg. at p <0.01, of the flexor muscles of the shoulder joint in adduction, showed an increase in strength capabilities by 17,9 mm Hg. at p <0.01, strength endurance of the abdominal muscles showed an increase of 32,4 sec at p <0.01. Conclusion: Posture Screen Mobile is a non-invasive and reliable assessment of the parameters of the posture. A 16-week set of weights and stretching exercises is highly effective in correcting kypholordotic posture in young men.
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Liu, Kun, Shuo Ji, Yong Liu, Chi Gao, Jun Fu, Lei Dai, and Shizhong Zhang. "Design and Optimization of Multifunctional Human Motion Rehabilitation Training Robot EEGO." Actuators 12, no. 8 (July 28, 2023): 311. http://dx.doi.org/10.3390/act12080311.
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A multifunctional human motion rehabilitation training robot named EEGO (electric easy go) that could achieve four functions through structural transformation was designed. The four functions achieved by four working modes: the Supporting Posture Mode (SM), the Grasping Posture Mode (GM), the Riding Posture Mode (RM), and the Pet Mode (PM), which are suitable for patients in the middle and late stages of rehabilitation. The size of the equipment under different functions is determined by the height of different postures of the human. During the design process, the equipment was lightweight using size optimization methods, resulting in a 47.3% reduction in mass compared to the original design. Based on the Zero Moment Point (ZMP) stability principle, the stability mechanism of the robot was verified under the three different functions. According to the wanted function of the equipment, the control system of the equipment was designed. Finally, a prototype was prepared based on the analysis and design results for experimental verification, which can effectively assist patients in motion rehabilitation training such as gait, walking, and other movements.
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Di, An. "Posture Correction Technique Based on Visual Analysis." Applied Mechanics and Materials 644-650 (September 2014): 2632–35. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.2632.
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In basketball training, if the posture of basketball player is deviated, great impact will be brought to basketball training. Therefore, this paper presents a posture correction technology based on vision analysis. A lot of computer vision image are collected in basketball training, this images are enhanced to improve definition of image, with the high-quality images to identify wrong posture, and compare with standard posture to achieve posture correction in basketball training. Experimental results show that the proposed algorithm for posture correction in basketball training can improve the accuracy of correction, so as to meet the actual needs of basketball training.
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Silveira Mallmann, André Luiz, Fernanda da Silva Medeiros, Bruna Nichele da Rosa, Kaanda Nabilla Souza Gontijo, and Cláudia Tarragô Candotti. "Effects of TRX Suspensions Training on Functionality, Body Pain and Static Posture of an Elderly Woman: a Case Report." Journal of Health Sciences 21, no. 1 (March 30, 2019): 8. http://dx.doi.org/10.17921/2447-8938.2019v21n1p8-14.
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Considerando que o treinamento suspenso (TRX® ST) remete à ideia de um treinamento completo, trabalhando o corpo como uma unidade, acredita-se que sua prática pode trazer grandes benefícios. Nessa perspectiva, objetivou-se identificar o efeito do treinamento suspenso TRX® ST sobre a funcionalidade, a postura estática da coluna vertebral e as dores corporais em geral em uma mulher idosa, a qual foi submetida à avaliação (1) da funcionalidade por meio do protocolo proposto pelo Senior Fitness Test (SFT); (2) das dores corporais utilizando-se o instrumento Informações sobre Dor nas Costas (IDC); e (3) da postura estática da coluna vertebral utilizando o Flexicurva antes do início do treinamento (1ª avaliação) e após a última sessão do treinamento (2ª avaliação). O treinamento suspenso (TRX® ST), composto de exercícios para força e flexibilidade, foi realizado durante 12 semanas, sendo cada semana composta de duas sessões, com duração de até 50 minutos cada. A participante apresentou: (1) melhora da funcionalidade (aumentando de 12 para 19 o número de repetições de sentar e levantar; diminuindo de 5,9s para 4,5s o tempo de sentar e caminhar; e diminuindo de 6cm para 0cm o resultado de sentar e alcançar, no STF), exceto nos membros superiores; (2) diminuição da dor nas regiões dorsal (de EVA intensidade 1 para intensidade 0), lombar (de EVA intensidade 1 para intensidade 0) e de glúteos (de EVA intensidade 2 para intensidade 1); e (3) mudança da postura da coluna lombar, passando de uma retificação (24°) para uma lordose fisiológica (41°). Em contrapartida, os resultados também demonstraram que a postura da coluna torácica não foi alterada pelo treinamento. Tendo em vista que esses são resultados iniciais, se faz necessária a condução de novos estudos a fim de verificar os efeitos do treinamento com TRX® ST sobre as variáveis dor, postura estática e funcionalidade, bem como sobre a postura dinâmica e a qualidade de vida de seus praticantes.Palavras-chave: Educação Física e Treinamento. Idoso. Postura. Dor.AbstractConsidering that suspended training (TRX® ST) refers to the idea of a full training, it is believed that its practice can bring great benefits. Thus, it was [ aimed to verify the effect of suspended training TRX® ST on the functionality, the static posture of the spine and general body pain in an elderly woman. The following were evaluated :(1) funcionality using Senior Fitness Test (SFT) protocol; (2) body pain using a validated questionnaire; and (3) spine static posture using Flexicurve before the training (1st evaluation) and after the last training session (2nd evaluation). The suspended training TRX® ST, that consists of exercises for strength and flexibility, was performed for twelve weeks, in such ways that each week consisted of two sessions, lasting 50 minutes each. The participant presented: (1) an improvement of the functionality (increasing from 12 to 19 repetitions the number of repetitions of sitting and standing up; decreasing from 5.9s to 4.5s the number of sitting and walking ; and decreasing from 6cm to 0cm the result of sitting and reaching up, in STF), except in upper limbs; (2) a reduction of pain in the dorsal, lumbar and gluteal regions; and (3) postural modifications in the lumbar spine, passing from a correction (24°) to a physiological lordosis. (41°). In contrast, the results also showed that the posture of the thoracic spine was not affected by the suspended training in twelve weeks. Since these are initial results, it is necessary to conduct further studies in order to verify the effects of training with TRX® ST on the variables pain, static posture and functionality, as well as on the dynamic posture and the quality of life of its practitioners.Keywords: Physical Education and Training. Aged. Posture. Pain.
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Yuan, He Jin. "Human Action Recognition Algorithm Based on Key Posture." Advanced Materials Research 631-632 (January 2013): 1303–8. http://dx.doi.org/10.4028/www.scientific.net/amr.631-632.1303.
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A novel human action recognition algorithm based on key posture is proposed in this paper. In the method, the mesh features of each image in human action sequences are firstly calculated; then the key postures of the human mesh features are generated through k-medoids clustering algorithm; and the motion sequences are thus represented as vectors of key postures. The component of the vector is the occurrence number of the corresponding posture included in the action. For human action recognition, the observed action is firstly changed into key posture vector; then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can exceed 90%.
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Wang, Shuang Mei, Yi Gao, and Li Luo. "Human Posture Recognition Based on DAG-SVMS." Advanced Materials Research 1042 (October 2014): 117–20. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.117.
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A posture feature extraction and recognition method in monitoring environment is proposed in this paper which can recognize human shapes and analyze human postures. First contours of moving objects are extracted from two frames of a consecutive monitoring video. Then feature parameters are calculated from boundary contours to construct feature vector. In order to classify moving object and human and analyze postures, a DAG-SVMS is constructed by training 100 sample images. Results demonstrate the validity of this method.
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Cheung, Mei-chun, Joanne Yip, and Janelle S. K. Lai. "Biofeedback Posture Training for Adolescents with Mild Scoliosis." BioMed Research International 2022 (January 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/5918698.
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Adolescent idiopathic scoliosis (AIS) is characterized by uneven shoulders, spinal curvature, and uneven hips, and asymmetry in paraspinal muscle activities is common in AIS. This pilot study was aimed at examining the use of a surface electromyography (sEMG) biofeedback posture training program in adolescents with mild scoliosis ( Cob b ’ s angle < 30 ° ) to attenuate asymmetry in paraspinal muscle activities and control the curve progression. Seven female adolescents (age, 12–14 years) with mild scoliosis ( Cob b ’ s angle < 30 ° ) were recruited. The participants received 30 tailor-made sessions of sEMG biofeedback posture training at a rate of one to two sessions per week for approximately 6 months. The activities of the paraspinal muscles (the trapezius, latissimus dorsi, thoracic erector spinae, and lumbar erector spinae) measured by sEMG during habitual sitting postures and spinal deformity evaluated by 3D ultrasound imaging were compared before and after training. The mean values of the root-mean-square sEMG ratio, an index of symmetry in paraspinal muscle activities of the muscle pairs between the concave and convex sides of the spinal curve, revealed significant asymmetry over the trapezius and lumbar erector spinae before the training (p <0.05). After the training, all seven adolescents achieved relatively more symmetrical paraspinal muscle activities over these two muscle pairs ( p < 0.05 ). In two adolescents, the spinal curvature decreased by 5.7° and 5.6°, respectively, whereas the remaining adolescents showed a minimal curve progression with changes in the spinal curvature controlled under 5°. To conclude, sEMG biofeedback posture training can reduce asymmetry in paraspinal muscle activities and control curve progression in adolescents with mild scoliosis and can potentially be considered an alternative early intervention for muscle reeducation in this cohort.
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Hou, Xiangfeng, and Bo Qi. "Basketball Training Posture Monitoring Based on Intelligent Wearable Device." Mobile Information Systems 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/4945534.
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Aiming at the problems of low monitoring accuracy, long time, and poor effect in the current basketball training posture monitoring method, a basketball training posture monitoring method based on intelligent wearable devices is proposed. By analyzing the concept and classification of intelligent wearable devices, the attitude monitoring technology based on intelligent wearable devices is studied. A two-stage Kalman filter is used to correct the error caused by the drift of the gyroscope signal in the intelligent wearable device by constructing an adaptive acceleration error covariance matrix. The time sequence of collecting acceleration and angular velocity signals is segmented, and the characteristics of basketball training posture are extracted from the sensor signals of the intelligent wearable device. The SVM classification algorithm is used to monitor the basketball training posture and realize the basketball training posture monitoring. The experimental results show that the basketball training posture monitoring effect of the proposed method is better, which can effectively improve the monitoring accuracy and shorten the monitoring time.
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Chen, Ken, Gimantha Perera, Li Li, Xu Xu, and Karen B. Chen. "Develop and evaluate an augmented reality posture training tool to promote work safety." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 2051–55. http://dx.doi.org/10.1177/1071181320641496.
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Conventional work posture training tools included pamphlets, one-time training orientation, and/or videos. These tools did not always yield satisfactory training outcomes, and the incident rate of work-related musculoskeletal disorders did not substantially lower. In this research, modern augmented reality (AR) technology was leveraged to deliver interactive, holistic, whole-body visual information to convey safe work postures. The developmental procedure followed DMAIC by first defining specifications of training content, which led to the development of the training tool, including 3D reconstruction of a virtual instructor and building of user interface based on user-centered framework. This AR training tool was measured and analyzed through usability evaluation, and quantitative and qualitative data were obtained for cross-validation and usability issue source identification. Findings revealed the utility of 3D reconstruction of a virtual instructor and practicality of adopting conventional usability evaluation method for AR user interface usability evaluation. Feedback from the usability evaluation via questionnaire, think aloud, and post-task open-ended responses are employed to iteratively design the next version of the AR posture training tool.
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Kajaks, Tara, Christina Ziebart, Vickie Galea, Brenda Vrkljan, and Joy C. MacDermid. "Posture Evaluation of Firefighters During Simulated Fire Suppression Tasks." Workplace Health & Safety 71, no. 12 (November 24, 2023): 606–16. http://dx.doi.org/10.1177/21650799231214275.
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Background: Posture mechanics during fire suppression tasks are associated with musculoskeletal injuries in firefighters. Methods: This study uses the Ovako Working Posture Analyzing System (OWAS) ergonomics tool to describe and evaluate the postures of 48 firefighters during 3 simulated tasks: (a) hose drag, (b) hose pull, and (c) high-rise pack lift. Ergonomics intervention prioritizations based on the OWAS action classification (AC) scores were identified using Wilcoxon signed-rank tests. Chi-square analyses identified associations between firefighter characteristics and OWAS AC scores. Findings: The initial hose pick-up phase of each task was identified as a high priority for ergonomics intervention (OWAS AC = 4) in 45.8%, 54.2%, and 45.8% of cases for Tasks 1, 2, and 3, respectively. Lower BMI was associated with higher AC scores for the initial hose pick-up during Task 3 (likelihood ratio = 9.20, p value = .01). Conclusion: The results inform ergonomics priorities for firefighter training based on the tasks analyzed. Application to Practice: This study evaluates the posture mechanics of three commonly performed firefighting tasks. The results help inform an ergonomics training intervention focused on posture mechanics during occupational activities for firefighters.
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Xing, Lifu, and Sergey Popik. "A Systematic Review of the Impact of Sports on Body Posture in Adolescents." Journal of Medical Imaging and Health Informatics 10, no. 5 (May 1, 2020): 1159–64. http://dx.doi.org/10.1166/jmihi.2020.3013.
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Purpose: A correct body posture plays an important role in people’s health, especially for children and adolescents who are in intensive development. Exercise regularly can increase their health, but there is also an adverse influence on children and adolescents. Thus this review evaluates the impacts of basketball, volleyball, football, gymnast training on the body posture of adolescents. Methods: The literature collect was complete through databases which included Google Scholar, PubMed, and ScienceDirect. Eight of 480 studies met the inclusion criteria. The collecting articles have assessed the impact of the sport of basketball, volleyball, football, and gymnast on body posture. Result and Conclusion: According to the analysis, the sport of basketball and volleyball play a negative effect on adolescent’s body posture and deviation of body posture increase as training time longer. Further research is required to be done to investigate football training to affect body posture because no studies are confirming the effect of football on body posture. However, gymnast training showed a symmetrical body posture, but the changes of the spine in the sagittal plane in adolescents are worthy of attention. Therefore, it is important to notice that the training program not only aims at the outcome but also promote the harmonious development of the adolescent.
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Zhai, Qiang. "Application of Visual Correction on Physical Training." Advanced Materials Research 989-994 (July 2014): 5461–63. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5461.
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A method is presented for three-dimensional motion posture correction employing Fuzzy kernel estimation and affine transformation. As selecting human moving node by a visual sensor and smoothing single frame image with irregular motion, noise interference is reduced. Based on principles of perspective and affine transformation, adjustment strategy of three-dimensional posture is deduced for irregular single-frame motion picture. In addition to determination of motion picture rotation, accurate correction of irregular single-frame motion picture is proposed. Experimental results show that, under different noise conditions, the algorithm corrects posture of a three-dimensional moving image accurately and presents strong anti-noise performance.
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Dobrzycki, Andrzej D., Ana M. Bernardos, Luca Bergesio, Andrzej Pomirski, and Daniel Sáez-Trigueros. "Exploring the Use of Contrastive Language-Image Pre-Training for Human Posture Classification: Insights from Yoga Pose Analysis." Mathematics 12, no. 1 (December 25, 2023): 76. http://dx.doi.org/10.3390/math12010076.
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Accurate human posture classification in images and videos is crucial for automated applications across various fields, including work safety, physical rehabilitation, sports training, or daily assisted living. Recently, multimodal learning methods, such as Contrastive Language-Image Pretraining (CLIP), have advanced significantly in jointly understanding images and text. This study aims to assess the effectiveness of CLIP in classifying human postures, focusing on its application in yoga. Despite the initial limitations of the zero-shot approach, applying transfer learning on 15,301 images (real and synthetic) with 82 classes has shown promising results. The article describes the full procedure for fine-tuning, including the choice for image description syntax, models and hyperparameters adjustment. The fine-tuned CLIP model, tested on 3826 images, achieves an accuracy of over 85%, surpassing the current state-of-the-art of previous works on the same dataset by approximately 6%, its training time being 3.5 times lower than what is needed to fine-tune a YOLOv8-based model. For more application-oriented scenarios, with smaller datasets of six postures each, containing 1301 and 401 training images, the fine-tuned models attain an accuracy of 98.8% and 99.1%, respectively. Furthermore, our experiments indicate that training with as few as 20 images per pose can yield around 90% accuracy in a six-class dataset. This study demonstrates that this multimodal technique can be effectively used for yoga pose classification, and possibly for human posture classification, in general. Additionally, CLIP inference time (around 7 ms) supports that the model can be integrated into automated systems for posture evaluation, e.g., for developing a real-time personal yoga assistant for performance assessment.
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Weineck, Felicitas, Dana Schultchen, Gernot Hauke, Matthias Messner, and Olga Pollatos. "Using bodily postures to reduce anxiety and improve interoception: A comparison between powerful and neutral poses." PLOS ONE 15, no. 12 (December 9, 2020): e0242578. http://dx.doi.org/10.1371/journal.pone.0242578.
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Background Previous research has shown that anxiety syndromes are highly prevalent among university students. Effective treatments are needed to reduce the burden of anxiety in this population. Powerful postures have been found to impact affective states, as well as interoception (i.e. the ability to perceive inner bodily signals). However, no previous study has compared the effects of powerful- and neutral postures in regards to anxiety and interoceptive ability. Methods The first part of the study measured the single-session effect of adopting powerful- vs. neutral postures on students' (n = 57) interoceptive ability and state anxiety. The second part of the study measured the effect of adopting powerful or neutral postures twice daily for two weeks, on individuals' interoceptive ability and trait anxiety. Results State anxiety decreased in both conditions whereas interoceptive accuracy only increased in the power posing condition after a single session. Interoceptive accuracy increased in both groups after two weeks of training. Limitations The study included no comparison to a condition where individuals adopted their natural (i.e. usual) bodily posture. Conclusions Embodiment interventions that include elements of adopting an open or expansive bodily posture whilst maintaining a self-focus, can help to reduce state anxiety and improve interoceptive accuracy in student populations. Power posing does not seem to be superior to holding a neutral posture to improve interoceptive accuracy or anxiety. One reason therefore could be that both conditions include the manipulation of self-focus and a postural change that diverges from individuals' normal posture.
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Peng, Ying, Chao He, and Hongcheng Xu. "Attachable Inertial Device with Machine Learning toward Head Posture Monitoring in Attention Assessment." Micromachines 13, no. 12 (December 14, 2022): 2212. http://dx.doi.org/10.3390/mi13122212.
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The monitoring of head posture is crucial for interactive learning, in order to build feedback with learners’ attention, especially in the explosion of digital teaching that occurred during the current COVID-19 pandemic. However, conventional monitoring based on computer vision remains a great challenge in the multi-freedom estimation of head posture, owing to low-angle annotation and limited training accuracy. Here, we report a fully integrated attachable inertial device (AID) that comfortably monitors in situ head posture at the neck, and provides a machine learning-based assessment of attention. The device consists of a stretchable inertial sensing unit and a fully integrated circuit-based system, as well as mechanically compliant encapsulation. Due to the mechanical flexibility, the device can be seamlessly attach to a human neck’s epidermis without frequent user interactions, and wirelessly supports six-axial inertial measurements, thereby obtaining multidimensional tracking of individual posture. These head postures (40 types) are then divided into 10 rotation actions which correspond to diverse situations that usually occur in daily activities of teaching. Benefiting from a 2D convolutional neural network (CNN)-based machine learning model, their classification and prediction of head postures can be used to analyze and infer attention behavior. The results show that the proposed 2D CNN-based machine learning method can effectively distinguish the head motion posture, with a high accuracy of 98.00%, and three actual postures were successfully verified and evaluated in a predefined attention model. The inertial monitoring and attention evaluation based on attachable devices and machine learning will have potential in terms of learning feedback and planning for learners.
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Wu, Xiaoming, Yu Cao, Yu Wang, Bing Li, Haitao Yang, and S. P. Raja. "Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning." International Journal of Interactive Multimedia and Artificial Intelligence In Press, In Press (2024): 1–9. http://dx.doi.org/10.9781/ijimai.2024.07.001.
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Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training.
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Li, Shisen, Chao Liu, and Guoliang Yuan. "Martial Arts Training Prediction Model Based on Big Data and MEMS Sensors." Scientific Programming 2021 (May 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/9993916.
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In martial arts teaching and sports training, the accurate capturing and analysis of martial arts athletes’ posture is conducive to accurately judge sports postures, as well as correcting sports movements in a targeted manner, further improving martial arts athletes’ performance and reducing physical damage. The manufacturing level of MEMS sensors continues to improve, and status perception of assembly objects is becoming more and more abundant and accurate. The shape is small and can be worn, and data can be collected continuously without obstacles. The price is relatively low, the privacy protection is strong, and the advantages are clear and prominent. A considerable number of technicians choose to use MEMS sensors as the main tool for human behavior detection data collection. Therefore, this article designs multiple MEMS inertial sensors to form a human body lower limb capture device, and its core components are composed of accelerometer, gyroscope, and magnetometer. In order to make the obtained acceleration value, angular velocity value, and magnetometer value accurately reflect the movement state of the lower limb structure, different data fusion algorithms and magnetometer ellipsoid fitting calibration algorithms are studied to realize the calculation of the posture angle of each joint point and obtain martial arts posture big data. In addition, through big data analysis, this article designs a martial arts training performance and injury risk prediction model, which can provide guidance and suggestions for martial arts teaching tasks.
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Pinto, Brendan L., and Clark R. Dickerson. "Vertical and horizontal barbell kinematics indicate differences in mechanical advantage between using an arched or flat back posture in the barbell bench press exercise." International Journal of Sports Science & Coaching 16, no. 3 (January 17, 2021): 756–62. http://dx.doi.org/10.1177/1747954120982954.
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Employing an arched back posture during the bench press exercise is increasingly popular. Vertical displacement of the barbell is commonly believed to be the key difference influencing strength performance between an arched and flat back bench press technique. However, comparisons between these back postures using a free weight barbell are lacking. Directly comparing performance between each posture is confounded by many variables such as proficiency and fatigue. This investigation aimed to investigate whether changing back posture alone can influence barbell kinematics, to indirectly assess potential performance differences. Twenty males performed one repetition of the bench press exercise using either an arched or flat back posture, at 25%, 50% and 75% of their one repetition maximum, in a repeated measures study design. Statistical significance was considered at p < 0.05. Changing back posture alone, reduced vertical displacement (approximately 11% average difference across all load conditions) and barbell to glenohumeral joint moment arm (approximately 20% difference) in the arched posture compared to the flat posture. These changes occurred without any specific cueing of the barbell motion and may increase the potential for lifting higher loads and decrease cumulative joint exposure. Additional cueing and training may be required to maximize the mechanical advantage available with each back posture. The arched posture appears to have an increased potential for further improvements in vertical displacement and moment arm through specific cueing. Future comparisons should consider if each back posture’s potential mechanical advantage has been maximized when assessing differences between techniques.
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Odesola, David Faith, Janusz Kulon, Shiny Verghese, Adam Partlow, and Colin Gibson. "Smart Sensing Chairs for Sitting Posture Detection, Classification, and Monitoring: A Comprehensive Review." Sensors 24, no. 9 (May 5, 2024): 2940. http://dx.doi.org/10.3390/s24092940.
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Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. The prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for the real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart sensing chairs, with a specific focus on the strategies used for posture detection and classification and the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, Google Scholar, Scopus, and PubMed databases yielded 39 pertinent studies that utilized non-invasive methods for posture monitoring. The analysis revealed that Force Sensing Resistors (FSRs) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy due to the constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.
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Satish, Aswin, Shobhalakshmi Sudarshan, and Mukta Pitambare. "Association between Foot Posture and Agility in Amateur Soccer Players." International Journal of Kinesiology and Sports Science 11, no. 4 (October 31, 2023): 10–16. http://dx.doi.org/10.7575/aiac.ijkss.v.11n.4p.10.
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Background: Agility is the ability to accelerate, decelerate and sprint with change in direction. The risk factors for injuries in soccer players are imbalance, decreased agility, and improper foot posture. Understanding the relationship between foot posture and agility can facilitate agility training and rehabilitation to improve their performance in the sport. Objective: To determine an association between foot posture and agility in amateur soccer players. Methodology: The cross sectional study recruited 78 age and gender-matched amateur soccer players. They were then allocated into pronated, supinated and neutral foot postures based on the foot posture index scores. Later the modified Illinois change of direction test (MICODT) was administered to all the players to test agility. With standing as the starting position of the test the players were made to run from the starting point to the finish point without any stop in between. The time to complete the test was noted. Results: Out of 78 subjects, 26 subjects had supinated feet 26 had pronated feet and 26 had normal feet. An association was found between foot posture and agility with a significance of p0.001. Logistic regression analysis revealed that players with supinated a feet demonstrated 3.53 times greater influence on agility than players with normal feet (p0.05). Players with pronated feet showed 0.54 times greater influence on agility than players with normal feet. Conclusion: A significant association between foot posture and agility was detected in amateur soccer players. Supinated feet influenced agility to a greater degree when compared to pronated and normal foot postures in amateur soccer players.
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Ferrone, Andrea, Astrid García Patiño, and Carlo Menon. "Low Back Pain—Behavior Correction by Providing Haptic Feedbacks: A Preliminary Investigation." Sensors 21, no. 21 (October 28, 2021): 7158. http://dx.doi.org/10.3390/s21217158.
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The activities performed by nurses in their daily activities involve frequent forward bending and awkward back postures. These movements contribute to the prevalence and development of low back pain (LBP). In previous studies, it has been shown that modifying their posture by education and training in proper lifting techniques decreases the prevalence of LBP. However, this education and training needs to be implemented daily. Hence, implementing the use of a wearable device to monitor the back posture with haptic feedback would be of importance to prevent LBP. This paper proposes a wearable device to monitor the back posture of the user and provide feedback when the participant is performing a possible hurtful movement. In this study, a group of participants was asked to wear the device while performing three of the most common activities performed by nurses. The study was divided into three sessions: In the first session, the participants performed the activities without feedback (baseline). During the second session, the participants received feedback from the wearable device (training) while performing the three tasks. Finally, for the third session, the participants performed the three tasks again, but the haptic feedback was turned off (validation). We found an improvement in the posture of more than 40% for the pitch (lateral bending) and roll (forward/backward bending) axes and 7% for the yaw (twisting) axis when comparing to the results from session 1 and session 2. The comparison between session 1 and session 3 showed an overall improvement of more than 50% for the pitch (lateral bending) and roll (forward/backward bending) axes and more than 20% for the yaw axis. These results hinted at the impact of the haptic feedback on the participants to correct their posture.
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Li, Chunguang, and Jianbiao Cui. "Intelligent Sports Training System Based on Artificial Intelligence and Big Data." Mobile Information Systems 2021 (May 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/9929650.
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All activities in training fields are for the improvement of athletes’ competitive abilities. A sports training system is an organizational system to achieve common goals. Competitive ability is one of the main manifestations of the evolution of the training system. With the rapid development of computer technology, people have begun to combine virtual reality and other technologies to achieve scientific sports-assisted training to eliminate traditional sports training that relied purely on experience. Pose estimation obtains the position, angle, and additional information about the human body in the image in a two-dimensional plane or three-dimensional space by establishing the mapping relationship between the human body features and the human body posture. This article demonstrates a golf-assisted training system to realize the transformation from an experience-based sports training method to a human motion analysis method, using artificial intelligence and big data. The swing posture parameters of the trainer and the coach are obtained using the posture estimation of a human body. Based on this information, an auxiliary training system is built. The two parameters of the joint angle trajectory and the posture similarity are used as auxiliary indicators to compare the trainers. The joint angle trajectory is analyzed, and the coach is guided based on the similarity of the posture.
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Feng, Yongfei, Hongbo Wang, Hao Yan, Xincheng Wang, Zhennan Jin, and Luige Vladareanu. "Research on Safety and Compliance of a New Lower Limb Rehabilitation Robot." Journal of Healthcare Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1523068.
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The lower limb rehabilitation robot is an application of robotic technology for stroke people with lower limb disabilities. A new applicable and effective sitting/lying lower limb rehabilitation robot (LLR-Ro) is proposed, which has the mechanical limit protection, the electrical limit protection, and the software protection to prevent the patient from the secondary damage. Meanwhile, as a new type of the rehabilitation robots, its hip joint rotation ranges are different in the patient sitting training posture and lying training posture. The mechanical leg of the robot has a variable workspace to work in both training postures. So, if the traditional mechanical limit and the electrical limit cannot be used in the hip joint mechanism design, a follow-up limit is first proposed to improve the compatibility of human-machine motion. Besides, to eliminate the accident interaction force between the patient and LLR-Ro in the process of the passive training, an amendment impedance control strategy based on the position control is proposed to improve the compliance of the LLR-Ro. A simulation experiment and an experiment with a participant show that the passive training of LLR-Ro has compliance.
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Cebula, Marzena, Krzysztof Czernicki, and Jacek Durmala. "Posture in youths practising oriented training activity." Scoliosis 4, Suppl 1 (2009): O23. http://dx.doi.org/10.1186/1748-7161-4-s1-o23.
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Wright, Edward. "POSTURE TRAINING FOR TMD PATIENTS: Author's response." Journal of the American Dental Association 131, no. 5 (May 2000): 560–61. http://dx.doi.org/10.14219/jada.archive.2000.0220.
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Harvey, Richard H., Erik Peper, Lauren Mason, and Monica Joy. "Effect of Posture Feedback Training on Health." Applied Psychophysiology and Biofeedback 45, no. 2 (March 30, 2020): 59–65. http://dx.doi.org/10.1007/s10484-020-09457-0.
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Chen, Meixiang, Ruirui Zhang, Meng Han, Tongchuan Yi, Gang Xu, Lili Ren, and Liping Chen. "Algorithm for Extracting the 3D Pose Information of Hyphantria cunea (Drury) with Monocular Vision." Agriculture 12, no. 4 (April 2, 2022): 507. http://dx.doi.org/10.3390/agriculture12040507.
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Currently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep learning models. In this study, an algorithm of the 3D posture information extraction method for Hyphantria cunea (Drury) based on monocular vision is proposed. Four images of every collected sample of H. cunea were taken at 90° intervals. The 3D pose information of the wings was extracted using boundary tracking, edge fitting, precise positioning and matching, and calculation. The 3D posture information of the torso was obtained by edge extraction and curve fitting. Finally, the 3D posture information of the wings and abdomen obtained by this method was compared with that obtained by Metrology-grade 3D scanner measurement. The results showed that the relative error of the wing angle was between 0.32% and 3.03%, the root mean square error was 1.9363, and the average relative error of the torso was 2.77%. The 3D posture information of H. cunea can provide important data support for sample augmentation and species identification of moth pests.
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Ray, C. A., and K. J. Cureton. "Interactive effects of body posture and exercise training on maximal oxygen uptake." Journal of Applied Physiology 71, no. 2 (August 1, 1991): 596–600. http://dx.doi.org/10.1152/jappl.1991.71.2.596.
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To determine the effect of posture on maximal O2 uptake (VO2 max) and other cardiorespiratory adaptations to exercise training, 16 male subjects were trained using high-intensity interval and prolonged continuous cycling in either the supine or upright posture 40 min/day 4 days/wk for 8 wk and 7 male subjects served as non-training controls. VO2 max measured during upright cycling and supine cycling, respectively, increased significantly (P less than 0.05) by 16.1 +/- 3.4 and 22.9 +/- 3.4% in the supine training group (STG) and by 14.6 +/- 2.0 and 6.0 +/- 2.0% in the upright training group (UTG). The increase in VO2 max measured during supine cycling was significantly greater (P less than 0.05) in the STG than in the UTG. The increase in VO2 max in the UTG was significantly greater (P less than 0.05) when measured during upright exercise than during supine exercise. However, there was no significant difference in posture-specific VO2 max adaptations in the STG. A postural specificity was also evident in other maximal cardiorespiratory variables (ventilation, CO2 production, and respiratory exchange ratio). In the UTG, maximal heart rate decreased significantly (P less than 0.05) only during supine cycling; there was no significant difference in maximal heart rate after training in the STG. We conclude that posture affects maximal cardiorespiratory adaptations to cycle training. Additionally, supine training is more effective than upright training in increasing maximal cardiorespiratory responses measured during supine exercise, and the effects of supine training generalize to the upright posture to a greater extent than the effects of upright training generalize to the supine posture.(ABSTRACT TRUNCATED AT 250 WORDS)
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Dang, Zijun, Huan Dong, Tong Li, and Kai Kong. "Design and Optimization of Motion Training System Assisted by Human Posture Estimation Algorithm." Scientific Programming 2022 (May 25, 2022): 1–12. http://dx.doi.org/10.1155/2022/6839768.
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With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect.
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Michaelson, Joana V., Lorrie R. Brilla, David N. Suprak, Wren L. McLaughlin, and Dylan T. Dahlquist. "Effects of Two Different Recovery Postures during High-Intensity Interval Training." Translational Journal of the American College of Sports Medicine 4, no. 4 (February 15, 2019): 23–27. http://dx.doi.org/10.1249/tjx.0000000000000079.
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ABSTRACT The purpose of this study was to examine the effects of two different recovery postures, hands on head (HH) and hands on knees (HK), as a form of immediate recovery from high-intensity interval training (HIIT). Twenty female Division II varsity soccer players (age = 20.3 ± 1.1 yr, body mass index = 22.4 ± 1.80 kg·m−2) completed two experimental trials in a randomized, counterbalanced order. Each trial consisted of four intervals on a motorized treadmill consisting of 4 min of running (4 × 4) at 90%–95% HRmax with 3 min of passive recovery between each interval. HR recovery was collected during the first 60 s of each recovery, where volume of carbon dioxide (V̇CO2) and tidal volume (VT) were recorded each minute during the 3-min recovery period. Results showed an improved HR recovery (P < 0.001), greater VT (P = 0.008), and increased V̇CO2 (P = 0.049), with HK (53 ± 10.9 bpm; 1.44 ± 0.2 L·min−1, 1.13 ± 0.2 L·min−1) compared with HH (31 ± 11.3 bpm; 1.34 ± 0.2 L·min−1, 1.03 ± 0.2 L·min−1). These data indicate that HK posture may be more beneficial than the advocated HH posture as a form of immediate recovery from high-intensity interval training.
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Yuan, Rui, Zhendong Zhang, Yanyan Le, and Enqing Chen. "Adaptive Recognition of Motion Posture in Sports Video Based on Evolution Equation." Advances in Mathematical Physics 2021 (September 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/2148062.
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In the field of sports, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The application of artificial intelligence technology to the training of athletes to recognize athletes’ posture can help coaches assist in decision-making and greatly enhance athletes’ competitive ability. The human body movements embodied in sports are more complicated, and the accurate recognition of sports postures plays an active and important role in sports competitions and training. In this paper, inertial sensor technology is applied to attitude recognition in motion. First, in order to improve the accuracy of attitude calculation and reduce the noise interference in the preparation process, this article uses differential evolution algorithm to apply attitude calculation to realize multisensor data fusion. Secondly, a two-level neural network intelligent motion gesture recognition algorithm is proposed. The two-level neural network intelligent recognition algorithm effectively recognizes similar actions by splitting the traditional single-level neural network into two-level neural networks. Experiments show that the experimental method designed in this article for the posture in motion can obtain the motion information of the examinee in real time, realize the accurate extraction of individual motion data, and complete the recognition of the motion posture. The average accuracy rate can reach 98.85%. There is a certain practical value in gesture recognition.
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Licciardo, Gian Domenico, Alessandro Russo, Alessandro Naddeo, Nicola Cappetti, Luigi Di Benedetto, Alfredo Rubino, and Rosalba Liguori. "A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems." Applied Sciences 11, no. 11 (May 21, 2021): 4752. http://dx.doi.org/10.3390/app11114752.
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A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 mW and 7 mW for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 MHz and 26.6 MHz, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 kHz and 9.13 kHz, respectively. Furthermore, synthesis results with a CMOS 130 nm technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.
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Fan, Jingjin, Shuoben Bi, Guojie Wang, Li Zhang, and Shilei Sun. "Sensor Fusion Basketball Shooting Posture Recognition System Based on CNN." Journal of Sensors 2021 (March 29, 2021): 1–16. http://dx.doi.org/10.1155/2021/6664776.
Full textAbstract:
In recent years, with the development of wearable sensor devices, research on sports monitoring using inertial measurement units has received increasing attention; however, a specific system for identifying various basketball shooting postures does not exist thus far. In this study, we designed a sensor fusion basketball shooting posture recognition system based on convolutional neural networks. The system, using the sensor fusion framework, collected the basketball shooting posture data of the players’ main force hand and main force foot for sensor fusion and used a deep learning model based on convolutional neural networks for recognition. We collected 12,177 sensor fusion basketball shooting posture data entries of 13 Chinese adult male subjects aged 18–40 years and with at least 2 years of basketball experience without professional training. We then trained and tested the shooting posture data using the classic visual geometry group network 16 deep learning model. The intratest achieved a 98.6% average recall rate, 98.6% average precision rate, and 98.6% accuracy rate. The intertest achieved an average recall rate of 89.8%, an average precision rate of 91.1%, and an accuracy rate of 89.9%.
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Sobrinho, Andressa Crystine da Silva, Mariana Luciano de Almeida, Guilherme da Silva Rodrigues, Larissa Chacon Finzeto, Vagner Ramon Rodrigues Silva, Rodrigo Fenner Bernatti, and Carlos Roberto Bueno Junior. "Effect of Flexibility Training Associated with Multicomponent Training on Posture and Quality of Movement in Physically Inactive Older Women: A Randomized Study." International Journal of Environmental Research and Public Health 18, no. 20 (October 13, 2021): 10709. http://dx.doi.org/10.3390/ijerph182010709.
Full textAbstract:
Background: Multicomponent training has considerable adherence among older populations, but there is a lack of literature on the benefits of this training on older people’s posture. Literature also lacks stretching protocols that work the body in an integrated/unified way and respect the principle of individuality in exercise training. We evaluated the effect of a multicomponent training protocol combined or not with flexibility training in improving the posture and quality of movement in physically inactive older women, according to a score lower than 9.11 in the Modified Baecke Questionnaire for the Elderly (MBQE). Methods: 142 participants were evaluated and randomized in three training groups: multicomponent training (MT = 52), multicomponent and flexibility training (MFT = 43), and a control group (CG = 47). We evaluated joint amplitude using goniometry, flexibility with sit and reach and hands behind the back tests, quality of movement with the functional movement screen, and posture using biophotogammetry. Results: The MFT group had 15 parameters—flexibility and posture—with a very large effect size (ES > 1.30) and nine with average ES (0.50–0.79). MT presented two variables with large ES (0.80–1.25) and seven with average ES. CG presented three variables with high ES and five with average ES. Both interventions improved the quality of movement. Conclusions: These results demonstrate that 14 weeks of multicomponent and flexibility training in a group intervention can improve flexibility and posture levels in physically inactive older women.
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Rahman, Md Mozasser, Rayan Abbas Ahmed Alharazi, and Muhammad Khairul Imban b. Zainal Badri. "Intelligent system for Islamic prayer (salat) posture monitoring." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 1 (March 1, 2023): 220. http://dx.doi.org/10.11591/ijai.v12.i1.pp220-231.
Full textAbstract:
This paper introduced an Intelligent Salat Monitoring and Training System based on machine vision and image processing. In Islam, prayer (<em>i.e. s</em><em>alat</em>) is the second pillar of Islam. It is the most important and fundamental worshipping activity that believers have to perform five times a day. From gestures’ perspective, there are predefined human postures that must be performed in a precise manner. There are lots of materials on the internet and social media for training and correction purposes. However, some people do not perform these postures correctly due to being new to salat or even having learned prayers incorrectly. Furthermore, the time spent in each posture has to be balanced. To address these issues, we propose to develop an assistive intelligence framework that guides worshippers to evaluate the correctness of their prayer’s postures. Image comparison and pattern matching are used to study the system’s effectiveness by using several combining algorithms, such as Euclidean distance, template matching and grey-level correlation, to compare the images of the user and the database. The experiments’ results, both correct and incorrect salat performances, are shown via pictures and graph for each of the postures of salat.
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Azadinia, Fatemeh, Mojtaba Kamyab, Hamid Behtash, Nader Maroufi, and Bagher Larijani. "The effects of two spinal orthoses on balance in elderly people with thoracic kyphosis." Prosthetics and Orthotics International 37, no. 5 (February 11, 2013): 404–10. http://dx.doi.org/10.1177/0309364612474487.
Full textAbstract:
Background:Hyperkyphosis increases the risk of falls for elderly people by reducing postural balance. Spinomed orthosis and the posture-training support are two available options for improving postural balance but have never been compared.Objectives:To compare the effect of the Spinomed orthosis and the posture-training support on balance in elderly people with thoracic hyperkyphosis.Study Design:This study is a clinical trial on an accessible sample of elderly people with thoracic kyphosis.Method:Eighteen participants (16 women and 2 men), aged 60–80 years, with thoracic kyphosis greater than 50°, completed the study procedure. Subjects were randomly allocated to two groups, namely, Spinomed orthosis and the posture-training support groups. Sensory organization test and limits of stability were assessed using the EquiTest system and the Balance Master system, respectively. Balance score, directional control, and reaction time were measured to evaluate balance with and without orthosis in a random order.Results:In the posture-training support group, significant changes were observed in the studied balance parameters: balance score ( p < 0.001), directional control ( p = 0.027), and reaction time ( p = 0.047). There was a significant change in balance score ( p < 0.001) and directional control ( p = 0.032) in the Spinomed group. However, there were no significant differences in the effect of the two orthoses, the Spinomed orthosis and posture-training support, on balance factors.Conclusion:Both Spinomed orthosis and posture-training support may improve balance in the elderly with thoracic hyperkyphosis in a similar manner.Clinical relevanceDespite the importance of falls suffered by elderly people, not much attention has been paid to balance improvement and fall prevention while managing hyperkyphosis. This study evaluates the effect of the Spinomed orthosis and posture-training support on balance in hyperkyphotic elderly people. It provides some new insights into reducing the risk of falls for elderly people.