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Journal articles on the topic 'Human Fall detection'
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1
Zhang, Duo, Xusheng Zhang, Shengjie Li, Yaxiong Xie, Yang Li, Xuanzhi Wang, and Daqing Zhang. "LT-Fall." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (March 27, 2022): 1–24. http://dx.doi.org/10.1145/3580835.
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Falls are the leading cause of fatal injuries to elders in modern society, which has motivated researchers to propose various fall detection technologies. We observe that most of the existing fall detection solutions are diverging from the purpose of fall detection: timely alarming the family members, medical staff or first responders to save the life of the human with severe injury caused by fall. Instead, they focus on detecting the behavior of human falls, which does not necessarily mean a human is in real danger. The real critical situation is when a human cannot get up without assistance and is thus lying on the ground after the fall because of losing consciousness or becoming incapacitated due to severe injury. In this paper, we define a life-threatening fall as a behavior that involves a falling down followed by a long-lie of humans on the ground, and for the first time point out that a fall detection system should focus on detecting life-threatening falls instead of detecting any random falls. Accordingly, we design and implement LT-Fall, a mmWave-based life-threatening fall detection and alarming system. LT-Fall detects and reports both fall and fall-like behaviors in the first stage and then identifies life-threatening falls by continuously monitoring the human status after fall in the second stage. We propose a joint spatio-temporal localization technique to detect and locate the micro-motions of the human, which solves the challenge of mmWave's insufficient spatial resolution when the human is static, i.e., lying on the ground. Extensive evaluation on 15 volunteers demonstrates that compared to the state-of-the-art work (92% precision and 94% recall), LT-Fall achieves zero false alarms as well as a precision of 100% and a recall of 98.8%.
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Sarthak Turki, Et al. "A Machine Learning Classification Paradigm for Automated Human Fall Detection." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 1169–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8638.
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For elderly people, falls are a severe worry since they can result in serious injuries, loss of independence, and deterioration of general health. In fact, among older persons, falls constitute the main reason for injury-related hospitalisations and fatalities. There is an obvious demand for fall detection systems that can help avoid or lessen the negative effects of falls given the enormous impact of falls on the senior population. Systems for detecting falls are created to notify carers or emergency services when a person has fallen, enabling quicker responses and better results. Elderly people who live alone or have mobility or balance impairments that make them more likely to fall may find these systems to be especially helpful.
The difficulty of categorising various actions as part of a system created to meet the demand for a wearable device to collect data for fall and near-fall analysis is addressed in this study. Three common activities—standing, walking, and lying down—four distinct fall trajectories—forward, backward, left, and right—as well as near-fall circumstances are recognised and detected.
Overall, fall detection systems play a significant role in the care of elderly people by lowering the chance of falls and its unfavourable effects. In order to better meet the demands of this vulnerable group, it's expected that as the older population grows, there will be a greater demand for fall detection systems and ongoing technological developments.
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Sahithi, Challa, Chennepalli HimaBindu, Harika C, and Jyothi M C. "Fall Detection." International Research Journal of Computer Science 10, no. 04 (May 31, 2023): 85–87. http://dx.doi.org/10.26562/irjcs.2023.v1004.09.
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Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is walking, squatting, or falling, among other activities. Among these tasks detecting elderly falls is crucial. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. This is the motivation behind the development of an aged assistance system that detects falls and notifies the caretaker. One of the key characteristics is that it sends an email notification along with a call to the caretaker once a fall is detected.
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Zheng, Kun, Bin Li, Yu Li, Peng Chang, Guangmin Sun, Hui Li, and Junjie Zhang. "Fall detection based on dynamic key points incorporating preposed attention." Mathematical Biosciences and Engineering 20, no. 6 (2023): 11238–59. http://dx.doi.org/10.3934/mbe.2023498.
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<abstract> <p>Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.</p> </abstract>
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Shrivastava, Rashmi, and Manju Pandey. "Human Fall Detection Using Efficient Kernel and Eccentric Approach." International Journal of E-Health and Medical Communications 12, no. 1 (January 2021): 62–80. http://dx.doi.org/10.4018/ijehmc.2021010105.
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Unintentional human falls are a very crucial problem in elderly people. If the fall goes unnoticed or undetected, it can lead to severe injuries and can even lead to death. Detecting falls as early as possible is very important to avoid severe physical injurious and mental trauma. The objective of this paper is to design the fall detection model using data of daily living activities only. In the proposed fall detection model, an eccentric approach with SVM based one-class classification is used. For the pre-processing step, fast fourier transformation has been applied to the data and seven features have been calculated using the preprocessed ADL dataset that has been calculated from the dataset of ADL (activities of daily living) activities acquired from the smartphones. An enhancement of the chi-square kernel-based support vector machine has been proposed here for classifying ADL activities from fall activities. Using the proposed algorithm, 98.81% sensitivity and 98.65% specificity have been achieved. This fall detection model achieved 100% accuracy on the FARSEEING dataset.
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Zi, Xing, Kunal Chaturvedi, Ali Braytee, Jun Li, and Mukesh Prasad. "Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety." Electronics 12, no. 5 (March 6, 2023): 1259. http://dx.doi.org/10.3390/electronics12051259.
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Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.
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Ribeiro, Osvaldo, Luis Gomes, and Zita Vale. "IoT-Based Human Fall Detection System." Electronics 11, no. 4 (February 15, 2022): 592. http://dx.doi.org/10.3390/electronics11040592.
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Human falls are an issue that especially affects elderly people, resulting in permanent disabilities or even in the person’s death. Preventing human falls is a social desire, but it is almost impossible to achieve because it is not possible to ensure full prevention. A possible solution is the detection of human falls in near real-time so that help can quickly be provided. This has the potential to greatly reduce the severity of the fall in long-term health consequences. This work proposes a solution based on the internet of things devices installed in people’s homes. The proposed non-wearable solution is non-intrusive and can be deployed not only in homes but also in hospitals, rehabilitation facilities, and elderly homes. The solution uses a three-layered computation architecture composed of edge, fog, and cloud. A mathematical model using the Morlet wavelet and an artificial intelligence model using artificial neural networks are used for human fall classification; both approaches are compared. The results showed that the combination of both models is possible and brings benefits to the system, achieving an accuracy of 92.5% without false negatives.
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Kan, Xi, Shenghao Zhu, Yonghong Zhang, and Chengshan Qian. "A Lightweight Human Fall Detection Network." Sensors 23, no. 22 (November 9, 2023): 9069. http://dx.doi.org/10.3390/s23229069.
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The rising issue of an aging population has intensified the focus on the health concerns of the elderly. Among these concerns, falls have emerged as a predominant health threat for this demographic. The YOLOv5 family represents the forefront of techniques for human fall detection. However, this algorithm, although advanced, grapples with issues such as computational demands, challenges in hardware integration, and vulnerability to occlusions in the designated target group. To address these limitations, we introduce a pioneering lightweight approach named CGNS-YOLO for human fall detection. Our method incorporates both the GSConv module and the GDCN module to reconfigure the neck network of YOLOv5s. The objective behind this modification is to diminish the model size, curtail floating-point computations during feature channel fusion, and bolster feature extraction efficacy, thereby enhancing hardware adaptability. We also integrate a normalization-based attention module (NAM) into the framework, which concentrates on salient fall-related data and deemphasizes less pertinent information. This strategic refinement augments the algorithm’s precision. By embedding the SCYLLA Intersection over Union (SIoU) loss function, our model benefits from faster convergence and heightened detection precision. We evaluated our model using the Multicam dataset and the Le2i Fall Detection dataset. Our findings indicate a 1.2% enhancement in detection accuracy compared with the conventional YOLOv5s framework. Notably, our model realized a 20.3% decrease in parameter tally and a 29.6% drop in floating-point operations. A comprehensive instance analysis and comparative assessments underscore the method’s superiority and efficacy.
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Martínez-Villaseñor, Lourdes, Hiram Ponce, and Ricardo Abel Espinosa-Loera. "Multimodal Database for Human Activity Recognition and Fall Detection." Proceedings 2, no. 19 (October 22, 2018): 1237. http://dx.doi.org/10.3390/proceedings2191237.
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Fall detection can improve the security and safety of older people and alert when fall occurs. Fall detection systems are mainly based on wearable sensors, ambient sensors, and vision. Each method has commonly known advantages and limitations. Multimodal and data fusion approaches present a combination of data sources in order to better describe falls. Publicly available multimodal datasets are needed to allow comparison between systems, algorithms and modal combinations. To address this issue, we present a publicly available dataset for fall detection considering Inertial Measurement Units (IMUs), ambient infrared presence/absence sensors, and an electroencephalogram Helmet. It will allow human activity recognition researchers to do experiments considering different combination of sensors.
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Abduljabbar Ali, Mohammed, Abir Jaafar Hussain, and Ahmed T. Sadiq. "Human Fall Down Recognition Using Coordinates Key Points Skeleton." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (February 16, 2022): 88–104. http://dx.doi.org/10.3991/ijoe.v18i02.28017.
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Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.
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Kavya G, Sunil Kumar C T, Dhanush C, and Kruthika J. "Human Fall Detection Using Video Surveillance." ACS Journal for Science and Engineering 1, no. 1 (March 12, 2021): 1–10. http://dx.doi.org/10.34293/acsjse.v1i1.1.
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Fall is one of the biggest challenge in elderly people, pregnant and small children’s, who stays alone in home. Sometimes this fall leads to severe injuries and even to death. Detecting the fall is very much important for elderly people. Convolutional Neural Network (CNN) is an deep learning algorithm used for image processing. In this paper, we present a video-based fall detection using CNN, this CNN will perform background subtraction and captures only foreground objects to detect the human movements and detect if fall happens. Firstly, camera will be capturing all the movements of the person. Our proposed model will detect the fall and finally an alarm is raised and email is sent to a given particular caretaker and family member. Our experimental results show the best performance of the proposed model.
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P, Nishanth. "Machine Learning based Human Fall Detection System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2677–82. http://dx.doi.org/10.22214/ijraset.2021.35394.
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Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.
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Yuan, Chunmiao, Pengju Zhang, Qingyong Yang, and Jianming Wang. "Fall Detection and Direction Judgment Based on Posture Estimation." Discrete Dynamics in Nature and Society 2022 (June 15, 2022): 1–12. http://dx.doi.org/10.1155/2022/8372291.
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For the problem of elderly people falling easily, it is very necessary to correctly detect the occurrence of falls and provide early warning, which can greatly reduce the injury caused by falls. Most of the existing fall detection algorithms require the monitored persons to carry wearable devices, which will bring inconvenience to their lives and few algorithms pay attention to the direction of the fall. Therefore, we propose a video-based fall detection and direction judgment method based on human posture estimation for the first time. We predict the joint point coordinates of each human body through the posture estimation network, and then use the SVM classifier to detect falls. Next, we will use the three-dimensional human posture information to judge the direction of the fall. Compared to the existing methods, our method has a great improvement in sensitivity, specificity, and accuracy which reaches 95.86, 99.5, and 97.52 on the Le2i fall dataset, respectively, whereas on the UR fall dataset, they are 95.45, 100, and 97.43, respectively.
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Wang, Zhuo, Vignesh Ramamoorthy, Udi Gal, and Allon Guez. "Possible Life Saver: A Review on Human Fall Detection Technology." Robotics 9, no. 3 (July 19, 2020): 55. http://dx.doi.org/10.3390/robotics9030055.
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Among humans, falls are a serious health problem causing severe injuries and even death for the elderly population. Besides, falls are also a major safety threat to bikers, skiers, construction workers, and others. Fortunately, with the advancements of technologies, the number of proposed fall detection systems and devices has increased dramatically and some of them are already in the market. Fall detection devices/systems can be categorized based on their architectures as wearable devices, ambient systems, image processing-based systems, and hybrid systems, which employ a combination of two or more of these methodologies. In this review paper, a comparison is made among these major fall detection systems, devices, and algorithms in terms of their proposed approaches and measure of performance. Issues with the current systems such as lack of portability and reliability are presented as well. Development trends such as the use of smartphones, machine learning, and EEG are recognized. Challenges with privacy issues, limited real fall data, and ergonomic design deficiency are also discussed.
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Nizam, Yoosuf, Mohd Mohd, and M. Jamil. "Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor." Sensors 18, no. 7 (July 13, 2018): 2260. http://dx.doi.org/10.3390/s18072260.
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Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.
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Chen, Weiming, Zijie Jiang, Hailin Guo, and Xiaoyang Ni. "Fall Detection Based on Key Points of Human-Skeleton Using OpenPose." Symmetry 12, no. 5 (May 5, 2020): 744. http://dx.doi.org/10.3390/sym12050744.
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According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall.The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking, and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by OpenPose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to-height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior.
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Hendi, Ade, Hermanto Hermanto, and Abdur Rozaaq. "Sistem Deteksi Jatuh dan Peringatan Dini Pada Manusia Berbasis Android." Jurnal Sistem Komputer dan Informatika (JSON) 3, no. 3 (March 31, 2022): 350. http://dx.doi.org/10.30865/json.v3i3.3927.
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Falling is an event that has often happened to humans in the surrounding environment, and almost every human being has experienced a fall. The condition of falling in humans can have very serious effects, such as injuries and may even increase the risk of death. The cause of death due to a fall can be caused by several factors, including the weakness of a person's ability to stand back up after a fall, when he falls there is no first aid, someone who falls has difficulty contacting his family or closest people. The purpose of this study is to build a fall detection system in humans. This system utilizes the accelerometer sensor on a smartphone to detect human movement. The fall detection system can run with various conditions of falling motion position with an accuracy level of 87.27% from a total of 110 trials, and a sensitivity of 77.5% so it is properly to use in detecting falling motion.
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Valcourt, L., Y. D. L. Hoz, and M. Labrador. "Smartphone-based Human Fall Detection System." IEEE Latin America Transactions 14, no. 2 (February 2016): 1011–17. http://dx.doi.org/10.1109/tla.2016.7437252.
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Chander, Harish, Reuben F. Burch, Purva Talegaonkar, David Saucier, Tony Luczak, John E. Ball, Alana Turner, et al. "Wearable Stretch Sensors for Human Movement Monitoring and Fall Detection in Ergonomics." International Journal of Environmental Research and Public Health 17, no. 10 (May 19, 2020): 3554. http://dx.doi.org/10.3390/ijerph17103554.
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Wearable sensors are beneficial for continuous health monitoring, movement analysis, rehabilitation, evaluation of human performance, and for fall detection. Wearable stretch sensors are increasingly being used for human movement monitoring. Additionally, falls are one of the leading causes of both fatal and nonfatal injuries in the workplace. The use of wearable technology in the workplace could be a successful solution for human movement monitoring and fall detection, especially for high fall-risk occupations. This paper provides an in-depth review of different wearable stretch sensors and summarizes the need for wearable technology in the field of ergonomics and the current wearable devices used for fall detection. Additionally, the paper proposes the use of soft-robotic-stretch (SRS) sensors for human movement monitoring and fall detection. This paper also recapitulates the findings of a series of five published manuscripts from ongoing research that are published as Parts I to V of “Closing the Wearable Gap” journal articles that discuss the design and development of a foot and ankle wearable device using SRS sensors that can be used for fall detection. The use of SRS sensors in fall detection, its current limitations, and challenges for adoption in human factors and ergonomics are also discussed.
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Cai, Wen-Yu, Jia-Hao Guo, Mei-Yan Zhang, Zhi-Xiang Ruan, Xue-Chen Zheng, and Shuai-Shuai Lv. "GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction." Journal of Healthcare Engineering 2020 (June 25, 2020): 1–15. http://dx.doi.org/10.1155/2020/8887340.
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Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.
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Lim, Myeong Jun, Jin Ho Cho, Young Sun Cho, and Tae Seong Kim. "Directional Human Fall Recognition Using a Pair of Accelerometer and Gyroscope Sensors." Applied Mechanics and Materials 135-136 (October 2011): 449–54. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.449.
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Human fall in the elderly population is one of the major causes of injury or bone fracture: it can be a cause of various injuries (e.g., fracture, concussion, and joint inflammation). It also could be a possible cause of death in a severe case. To detect human fall, various fall detection algorithms have been devised. Most fall detection algorithms rely on signals from a single accelerometer or gyroscope and use a threshold-based method to detect the human fall. However, these algorithms need careful adjustment of a threshold for each subject and cannot detect the direction of falls. In this study, we propose a novel fall recognition algorithm using a pair of a tri-axial accelerometer and a tri-axial gyroscope. Our fall recognition algorithm utilizes a set of augmented features including autoregressive (AR) modeling coefficients of signals, signal magnitude area (SMA), and gradients of angles from the sensors. After Linear Discriminant Analysis (LDA) of the augmented features, an Artificial Neural Nets (ANNs) is utilized to recognize four directional human falls: namely forward fall, backward fall, right-side fall, and left-side fall. Our recognition results show the mean recognition rate of 95.8%. Our proposed fall recognition technique should be useful in the investigation of fall-related injuries and possibly in the prevention of falls for the elderly.
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Yan, Jianjun, Xueqiang Wang, Jiangtao Shi, and Shuai Hu. "Skeleton-Based Fall Detection with Multiple Inertial Sensors Using Spatial-Temporal Graph Convolutional Networks." Sensors 23, no. 4 (February 14, 2023): 2153. http://dx.doi.org/10.3390/s23042153.
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The application of wearable devices for fall detection has been the focus of much research over the past few years. One of the most common problems in established fall detection systems is the large number of false positives in the recognition schemes. In this paper, to make full use of the dependence between human joints and improve the accuracy and reliability of fall detection, a fall-recognition method based on the skeleton and spatial-temporal graph convolutional networks (ST-GCN) was proposed, using the human motion data of body joints acquired by inertial measurement units (IMUs). Firstly, the motion data of five inertial sensors were extracted from the UP-Fall dataset and a human skeleton model for fall detection was established through the natural connection relationship of body joints; after that, the ST-GCN-based fall-detection model was established to extract the motion features of human falls and the activities of daily living (ADLs) at the spatial and temporal scales for fall detection; then, the influence of two hyperparameters and window size on the algorithm performance was discussed; finally, the recognition results of ST-GCN were also compared with those of MLP, CNN, RNN, LSTM, TCN, TST, and MiniRocket. The experimental results showed that the ST-GCN fall-detection model outperformed the other seven algorithms in terms of accuracy, precision, recall, and F1-score. This study provides a new method for IMU-based fall detection, which has the reference significance for improving the accuracy and robustness of fall detection.
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Pan, Daohua, Hongwei Liu, Dongming Qu, and Zhan Zhang. "Human Falling Detection Algorithm Based on Multisensor Data Fusion with SVM." Mobile Information Systems 2020 (October 31, 2020): 1–9. http://dx.doi.org/10.1155/2020/8826088.
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Falling is a common phenomenon in the life of the elderly, and it is also one of the 10 main causes of serious health injuries and death of the elderly. In order to prevent falling of the elderly, a real-time fall prediction system is installed on the wearable intelligent device, which can timely trigger the alarm and reduce the accidental injury caused by falls. At present, most algorithms based on single-sensor data cannot accurately describe the fall state, while the fall detection algorithm based on multisensor data integration can improve the sensitivity and specificity of prediction. In this study, we design a fall detection system based on multisensor data fusion and analyze the four stages of falls using the data of 100 volunteers simulating falls and daily activities. In this paper, data fusion method is used to extract three characteristic parameters representing human body acceleration and posture change, and the effectiveness of the multisensor data fusion algorithm is verified. The sensitivity is 96.67%, and the specificity is 97%. It is found that the recognition rate is the highest when the training set contains the largest number of samples in the training set. Therefore, after training the model based on a large amount of effective data, its recognition ability can be improved, and the prevention of fall possibility will gradually increase. In order to compare the applicability of random forest and support vector machine (SVM) in the development of wearable intelligent devices, two fall posture recognition models were established, respectively, and the training time and recognition time of the models are compared. The results show that SVM is more suitable for the development of wearable intelligent devices.
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Bharti Sahu, Shreya Pawar, Mansi Chaudhari, Vaishnavi Kalal. "Real-Time Posture Estimation-Based Human Fall Detection System." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (December 1, 2023): 4798–811. http://dx.doi.org/10.52783/tjjpt.v44.i3.2649.
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Special attention should be given to those in vulnerable age groups, such as children and the elderly, who live in solitary living arrangements and have an increased likelihood of experiencing falls. The primary objective of a fall detector is to minimize the duration of time an elderly person remains on the ground subsequent to experiencing a fall event. The length of time that an individual remains on the floor subsequent to a fall is a determining factor in assessing the severity of the incident. The act of engaging in prolonged deceit has been shown to elevate the likelihood of experiencing adverse health outcomes such as hypothermia, dehydration, and pressure ulcers. The ultimate objective of the detector system is to identify the occurrence of a fall and expeditiously notify a carer. The potential for individuals to inadvertently trigger a fall detector by engaging in rapid movements such as standing up and sitting down underscores the need for a dependable detector that can effectively differentiate between instances of falling and other related occurrences. The primary aim of this research is to enhance the understanding of fall-related mechanisms among medical technologists in the field of public health. The assessment of an individual's physique alteration potential relies on the calculation of the ratio between their height and width. To validate a human fall, it is necessary to measure the height and centre of the rectangle that encloses the individual, and thereafter compare these measurements to a specified threshold. An alert system has been developed to notify persons who are connected to the network in the event of a catastrophe, provided that a state of inactivity is maintained for 100 consecutive frames.
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Alanazi, Thamer, and Ghulam Muhammad. "Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion." Diagnostics 12, no. 12 (December 6, 2022): 3060. http://dx.doi.org/10.3390/diagnostics12123060.
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Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20–30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem. Timely and accurate fall incident detection could enable the instant delivery of medical services to the injured. New advances in vision-based technologies, including deep learning, have shown significant results in action recognition, where some focus on the detection of fall actions. In this paper, we propose an automatic human fall detection system using multi-stream convolutional neural networks with fusion. The system is based on a multi-level image-fusion approach of every 16 frames of an input video to highlight movement differences within this range. This results of four consecutive preprocessed images are fed to a new proposed and efficient lightweight multi-stream CNN model that is based on a four-branch architecture (4S-3DCNN) that classifies whether there is an incident of a human fall. The evaluation included the use of more than 6392 generated sequences from the Le2i fall detection dataset, which is a publicly available fall video dataset. The proposed method, using three-fold cross-validation to validate generalization and susceptibility to overfitting, achieved a 99.03%, 99.00%, 99.68%, and 99.00% accuracy, sensitivity, specificity, and precision, respectively. The experimental results prove that the proposed model outperforms state-of-the-art models, including GoogleNet, SqueezeNet, ResNet18, and DarkNet19, for fall incident detection.
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Liu, Wei, Xu Liu, Yuan Hu, Jie Shi, Xinqiang Chen, Jiansen Zhao, Shengzheng Wang, and Qingsong Hu. "Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM." Sensors 22, no. 14 (July 21, 2022): 5449. http://dx.doi.org/10.3390/s22145449.
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Aiming to avoid personal injury caused by the failure of timely medical assistance following a fall by seafarer members working on ships, research on the detection of seafarer’s falls and timely warnings to safety officers can reduce the loss and severe consequences of falls to seafarers. To improve the detection accuracy and real-time performance of the seafarer fall detection algorithm, a seafarer fall detection algorithm based on BlazePose–LSTM is proposed. This algorithm can automatically extract the human body key point information from the video image obtained by the vision sensor, analyze its internal data correlation characteristics, and realize the process from RGB camera image processing to seafarer fall detection. This fall detection algorithm extracts the human body key point information through the optimized BlazePose human body key point information extraction network. In this section, a new method for human bounding-box acquisition is proposed. In this study, a head detector based on the Vitruvian theory was used to replace the pre-trained SSD body detector in the BlazePose preheating module. Simultaneously, an offset vector is proposed to update the bounding box obtained. This method can reduce the frequency of repeated use of the head detection module. The algorithm then uses the long short-term memory neural network to detect seafarer falls. After extracting fall and related behavior data from the URFall public data set and FDD public data set to enrich the self-made data set, the experimental results show that the algorithm can achieve 100% accuracy and 98.5% specificity for the seafarer’s falling behavior, indicating that the algorithm has reasonable practicability and strong generalization ability. The detection frame rate can reach 29 fps on a CPU, which can meet the effect of real-time detection. The proposed method can be deployed on common vision sensors.
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WAKITA, Kohei, Jian HUANG, Kosuke SEKIYAMA, and Toshio FUKUDA. "Real-time Fall Detection and Prevention Control Using Intelligent Cane for Human Operator." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 265–70. http://dx.doi.org/10.1299/jsmeicam.2010.5.265.
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Fan, Xinnan, Qian Gong, Rong Fan, Jin Qian, Jie Zhu, Yuanxue Xin, and Pengfei Shi. "Substation Personnel Fall Detection Based on Improved YOLOX." Electronics 12, no. 20 (October 18, 2023): 4328. http://dx.doi.org/10.3390/electronics12204328.
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With the continuous promotion of smart substations, staff fall detection has become a key issue in automatic detection of substations. The injuries and safety hazards caused by falls among substation personnel are numerous. If a timely response can be made in the event of a fall, the injuries caused by falls can be reduced. In order to address the issues of low accuracy and poor real-time performance in detecting human falls in complex substation scenarios, this paper proposes an improved algorithm based on YOLOX. A customized feature extraction module is introduced to the YOLOX feature fusion network to extract diverse multiscale features. A recursive gated convolutional module is added to the head to enhance the expressive power of the features. Meanwhile, the SIoU(Soft Intersection over Union) loss function is utilized to provide more accurate position information for bounding boxes, thereby improving the model accuracy. Experimental results show that the improved algorithm achieves an mAP value of 78.45%, which is a 1.31% improvement over the original YOLOX. Compared to other similar algorithms, the proposed algorithm achieves high accuracy prediction of human falls with fewer parameters, demonstrating its effectiveness.
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Martínez-Villaseñor, Lourdes, Hiram Ponce, Jorge Brieva, Ernesto Moya-Albor, José Núñez-Martínez, and Carlos Peñafort-Asturiano. "UP-Fall Detection Dataset: A Multimodal Approach." Sensors 19, no. 9 (April 28, 2019): 1988. http://dx.doi.org/10.3390/s19091988.
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Falls, especially in elderly persons, are an important health problem worldwide. Reliable fall detection systems can mitigate negative consequences of falls. Among the important challenges and issues reported in literature is the difficulty of fair comparison between fall detection systems and machine learning techniques for detection. In this paper, we present UP-Fall Detection Dataset. The dataset comprises raw and feature sets retrieved from 17 healthy young individuals without any impairment that performed 11 activities and falls, with three attempts each. The dataset also summarizes more than 850 GB of information from wearable sensors, ambient sensors and vision devices. Two experimental use cases were shown. The aim of our dataset is to help human activity recognition and machine learning research communities to fairly compare their fall detection solutions. It also provides many experimental possibilities for the signal recognition, vision, and machine learning community.
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Polepaka, Sanjeeva, Harshini Sangem, Amrutha Varshini Aleti, Akshitha Ajjuri, Myasar Mundher Adnan, Swathi B, Amandeep Nagpal, and Ravi Kalra. "Promoting sustainable safety: Integrating fall detection for person and wheelchair safety." E3S Web of Conferences 507 (2024): 01025. http://dx.doi.org/10.1051/e3sconf/202450701025.
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Fall detection systems are crucial for ensuring the safety of the elderly, especially those who are wheelchair-bound. A potential remedy involves promptly detecting human falls in near real-time to facilitate rapid assistance. While various methods have been suggested for fall detectors, there remains a necessity to create precise and sturdy architectures, methodologies, and protocols for detecting falls, particularly among elderly individuals, especially those using wheelchairs. The objective is to design an affordable and dependable IoT-based system for detecting falls in wheelchair users, alerting nearby individuals for assistance and promote sustainable safety. The setup includes a MEMS Sensor, GSM module, and Arduino UNO microcontroller for detecting falls, with the goal of securing the well-being and promoting independent living for the elderly.
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Santos, Guto, Patricia Endo, Kayo Monteiro, Elisson Rocha, Ivanovitch Silva, and Theo Lynn. "Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks." Sensors 19, no. 7 (April 6, 2019): 1644. http://dx.doi.org/10.3390/s19071644.
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Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.
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Tong, Chao, Yu Lian, Yang Zhang, Zhongyu Xie, Xiang Long, and Jianwei Niu. "A Novel Real-Time Fall Detection System Based on Real-Time Video and Mobile Phones." Journal of Circuits, Systems and Computers 26, no. 04 (December 6, 2016): 1750056. http://dx.doi.org/10.1142/s0218126617500566.
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In recent years, due to the growing population of the elderly, falls of elderly people have aroused wide public concern. Detecting timely falls of the elderly is significant to their safety. Numerous challenges exist in real-time fall detection systems because some features of normal human activities are greatly similar to the characteristics of falls. To address these problems, we propose a novel fall detection scheme and build a health-care system to detect falls of the elderly based on a real-time video surveillance system and a smart phone. The system contains two major modules. The first module is a feature extraction module. We adopt the Gaussian mixture model, tracking learning detecting algorithm and logpolar histogram to extract the characteristics of falls from the video surveillance system and the sensors embedded in mobile phones. The main purpose of the second module is to detect a fall-based on the features obtained in the first module. The experimental results show that every module is significant. Besides, our system is effective to separate falls from other similar actions such as bend down with an accuracy rate of more than 98% and performs better than other state-of-the-art fall detection systems.
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Bucinskas, Vytautas, Andrius Dzedzickis, Juste Rozene, Jurga Subaciute-Zemaitiene, Igoris Satkauskas, Valentinas Uvarovas, Rokas Bobina, and Inga Morkvenaite-Vilkonciene. "Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis." Sensors 21, no. 15 (August 3, 2021): 5240. http://dx.doi.org/10.3390/s21155240.
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Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person.
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Kolobe, Tsepo Constantinus, Chungling Tu, and Pius Adewale Owolawi. "A Review on Fall Detection in Smart Home for Elderly and Disabled People." Journal of Advanced Computational Intelligence and Intelligent Informatics 26, no. 5 (September 20, 2022): 747–57. http://dx.doi.org/10.20965/jaciii.2022.p0747.
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Falling is a major challenge faced by elderly and disabled people who live alone. They therefore need reliable surveillance so they can be assisted in the event of a fall. An effective fall detection system is needed to provide good care to such people as it will allow for communication with caregivers. Such a system will not only reduce the medical costs related to falls but also lower the death rate among elderly and disabled people due to falls. This review paper presents a survey of different fall detection techniques and algorithms used for fall detection. Various fall detection approaches including wearable, vision, ambience, and multimodal systems are analyzed and compared and recommendations are presented.
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Zhang, Jin, Cheng Wu, and Yiming Wang. "Human Fall Detection Based on Body Posture Spatio-Temporal Evolution." Sensors 20, no. 3 (February 10, 2020): 946. http://dx.doi.org/10.3390/s20030946.
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Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Vision-driven fall event detection has the huge advantage of being non-invasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the “five-point inverted pendulum model”, and uses an improved two-branch multi-stage convolutional neural network (M-CNN) to extract and construct the inverted pendulum structure of human posture in real-world complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatio-temporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatio-temporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior: motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.
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Siong Jun, Sai, Hafiz Rashidi Ramli, Azura Che Soh, Noor Ain Kamsani, Raja Kamil Raja Ahmad, Siti Anom Ahmad, and Asnor Juraiza Ishak. "Development of fall detection and activity recognition using threshold based method and neural network." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1338. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1338-1347.
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Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
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Divya, S. "Developing a Fall Detection Technology for Mobility and System Level." Asian Journal of Computer Science and Technology 8, S2 (March 5, 2019): 13–16. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2034.
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Smartphone’s are programmable and embed various sensors; these phones have the potential to change the way how healthcare is delivered. Fall detection is definitely one of the possibilities. Injuries due to falls are dangerous, especially for elderly people, diminishing the quality of life or even resulting in death. This study presents the implementation of a fall detection prototype for the Android-based platform. The proposed system has three components: sensing the accelerometer data from the mobile embedded sensors, learning the relationship between the fall behavior and the collected data, and alerting preconfigured contacts through message while detecting fall. We adopt different fall detection algorithms and conduct various experiments to evaluate performance. The results show that the proposed system can recognize the fall from human activities, such as sitting, walking and standing, with 72.22% sensitivity and 73.78% specificity. The experiment also investigates the impact of different locations where the phone attached. In addition, this study further analyzes the trade-off between sensitivity and specificity and discusses the additional powers consumption of the devices.
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Fan, Kaibo, Ping Wang, and Shuo Zhuang. "Human fall detection using slow feature analysis." Multimedia Tools and Applications 78, no. 7 (January 24, 2018): 9101–28. http://dx.doi.org/10.1007/s11042-018-5638-9.
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Alanazi, Thamer, Khalid Babutain, and Ghulam Muhammad. "A Robust and Automated Vision-Based Human Fall Detection System Using 3D Multi-Stream CNNs with an Image Fusion Technique." Applied Sciences 13, no. 12 (June 7, 2023): 6916. http://dx.doi.org/10.3390/app13126916.
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Unintentional human falls, particularly in older adults, can result in severe injuries and death, and negatively impact quality of life. The World Health Organization (WHO) states that falls are a significant public health issue and the primary cause of injury-related fatalities worldwide. Injuries resulting from falls, such as broken bones, trauma, and internal injuries, can have severe consequences and can lead to a loss of mobility and independence. To address this problem, there have been suggestions to develop strategies to reduce the frequency of falls, in order to decrease healthcare costs and productivity loss. Vision-based fall detection approaches have proven their effectiveness in addressing falls on time, which can help to reduce fall injuries. This paper introduces an automated vision-based system for detecting falls and issuing instant alerts upon detection. The proposed system processes live footage from a monitoring surveillance camera by utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying a set of live footage with a 3D multi-stream CNN model (4S-3DCNN). The system alerts when the sequence of the Falling of the monitored human, followed by having Fallen, takes place. The effectiveness of the system was assessed using the publicly available Le2i dataset. System validation revealed an impressive result, achieving an accuracy of 99.44%, sensitivity of 99.12%, specificity of 99.12%, and precision of 99.59%. Based on the reported results, the presented system can be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.
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Xu, He, Leixian Shen, Qingyun Zhang, and Guoxu Cao. "Fall Behavior Recognition Based on Deep Learning and Image Processing." International Journal of Mobile Computing and Multimedia Communications 9, no. 4 (October 2018): 1–15. http://dx.doi.org/10.4018/ijmcmc.2018100101.
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Accidental fall detection for the elderly who live alone can minimize the risk of death and injuries. In this article, we present a new fall detection method based on "deep learning and image, where a human body recognition model-DeeperCut is used. First, a camera is used to get the detection source data, and then the video is split into images which can be input into DeeperCut model. The human key point data in the output map and the label of the pictures are used as training data to input into the fall detection neural network. The output model then judges the fall of the subsequent pictures. In addition, the fall detection system is designed and implemented with using Raspberry Pi hardware in a local network environment. The presented method obtains a 100% fall detection rate in the experimental environment. The false positive rate on the test set is around 1.95% which is very low and can be ignored because this will be checked by using SMS, WeChat and other SNS tools to confirm falls. Experimental results show that the proposed fall behavior recognition is effective and feasible to be deployed in home environment.
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Vishnu, Chalavadi, Rajeshreddy Datla, Debaditya Roy, Sobhan Babu, and C. Krishna Mohan. "Human Fall Detection in Surveillance Videos Using Fall Motion Vector Modeling." IEEE Sensors Journal 21, no. 15 (August 1, 2021): 17162–70. http://dx.doi.org/10.1109/jsen.2021.3082180.
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Hao, Zhanjun, Yu Duan, Xiaochao Dang, and Hongwen Xu. "KS-FALL: Indoor Human Fall Detection Method Under 5GHz Wireless Signals." IOP Conference Series: Materials Science and Engineering 569 (August 9, 2019): 032068. http://dx.doi.org/10.1088/1757-899x/569/3/032068.
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Song, Zhengyun. "Fall Detection Method Based on Convolutional Neural Network." Academic Journal of Science and Technology 7, no. 2 (September 27, 2023): 207–9. http://dx.doi.org/10.54097/ajst.v7i2.12274.
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With the improvement of people's living standards and social medical conditions, the average life expectancy is gradually lengthening, and the proportion of the elderly in the global population is also growing. China is a big country with a large population, and the problem of population aging is becoming increasingly prominent and severe. When elderly people live alone at home without others to care for them, falls become the most common and dangerous phenomenon due to the decline of physical function and the influence of certain diseases. Therefore, rapid, efficient and accurate identification and judgment of human falls are of great significance, which can effectively alleviate the threat to the life and health of the elderly and the social medical burden of falls. This paper mainly carried out a fall detection method based on convolutional neural networks, aiming to improve the accuracy of human fall detection through the natural advantages of convolutional neural networks in image recognition, and provide better solutions for medical monitoring and elderly care.
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Singh, Komal, Akshay Rajput, and Sachin Sharma. "Human Fall Detection Using Machine Learning Methods: A Survey." International Journal of Mathematical, Engineering and Management Sciences 5, no. 1 (November 1, 2019): 161–80. http://dx.doi.org/10.33889/ijmems.2020.5.1.014.
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Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.
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Wu, Falin, Hengyang Zhao, Yan Zhao, and Haibo Zhong. "Development of a Wearable-Sensor-Based Fall Detection System." International Journal of Telemedicine and Applications 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/576364.
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Fall detection is a major challenge in the public healthcare domain, especially for the elderly as the decline of their physical fitness, and timely and reliable surveillance is necessary to mitigate the negative effects of falls. This paper develops a novel fall detection system based on a wearable device. The system monitors the movements of human body, recognizes a fall from normal daily activities by an effective quaternion algorithm, and automatically sends request for help to the caregivers with the patient’s location.
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Qiu, Yuting, James Meng, and Baihua Li*. "Automated Falls Detection Using Visual Anomaly Detection and Pose-based Approaches: Experimental Review and Evaluation." Journal of Biomedical Research & Environmental Sciences 5, no. 1 (January 2024): 055–63. http://dx.doi.org/10.37871/jbres1872.
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Falls are a pervasive problem facing elderly populations, associated with significant morbidity and mortality. Prompt recognition of falls, especially in elderly people with cognitive or physical impairments who cannot raise the alarm themselves, is a challenge. To this end, wearable sensors can be used to detect fall behaviour, including smartwatches and wristbands. These devices are limited by their intrusiveness, require user compliance and have issues around endurance and comfort, reducing their effectiveness in elderly populations. They can also only target patients already recognised as falls risks, and cannot apply to non-identified patients. Leveraging state of the art AI deep learning, we introduce two types of automated fall detection techniques using visual information from cameras: 1) self-supervised autoencoder, distinguishing falls from normal behaviour as an anomaly detection problem, 2) supervised human posture-based fall activity recognition. Five models are trained and evaluated based on two publicly available video datasets, composed of activities of daily living and simulated falls in an office-like environment. To test the models for real-world fall detection, we developed two new datasets, including videos of real falls in elderly people, and more complex backgrounds and scenarios. The experimental results show autoencoder detectors are able to predict falls directly from images where the background is pre-learned. While the pose-based approach uses foreground body pose only for AI learning, better targeting complex scenarios and backgrounds. Video-based methods could be a potential for low-cost and non-invasive falls detection, increasing safety in care environments, while also helping elderly people retain independence in their own homes.
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Luo, Jiaqing, Ruiyu Bai, Suining He, and Kang G. Shin. "Pervasive Pose Estimation for Fall Detection." ACM Transactions on Computing for Healthcare 3, no. 3 (July 31, 2022): 1–23. http://dx.doi.org/10.1145/3478027.
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Falls are the second leading cause of accidental or unintentional injuries/deaths worldwide. Accurate pose estimation using commodity mobile devices will help early detection and injury assessment of falls, which are essential for the first aid of elderly falls. By following the definition of fall, we propose a P ervasive P ose Est imation scheme for fall detection ( P \( ^2 \) Est ), which measures changes in tilt angle and height of the human body. For the tilt measurement, P \( ^2 \) Est leverages the pointing of the mobile device, e.g., the smartphone, when unlocking to associate the Device coordinate system with the World coordinate system. For the height measurement, P \( ^2 \) Est exploits the fact that the person’s height remains unchanged while walking to calibrate the pressure difference between the device and the floor. We have prototyped and tested P \( ^2 \) Est in various situations and environments. Our extensive experimental results have demonstrated that P \( ^2 \) Est can track the body orientation irrespective of which pocket the phone is placed in. More importantly, it enables the phone’s barometer to detect falls in various environments with decimeter-level accuracy.
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Srikanth, K. "Alert System for Fall Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1739–47. http://dx.doi.org/10.22214/ijraset.2021.37658.
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Abstract: Healthcare is one of the most important industries, yet new ideas must travel a long way before being fully adopted due to its complexity, scope of duty, and stringent laws. The Internet of Things (IoT) may be the key to resolving healthcare challenges. The Internet of Things (IoT) has a lot of potential in healthcare, but it's still in its early stages. With the advancement of medical IoT, new possibilities for telemedicine, remote monitoring of a patient's status, and much more will emerge. Falling is a significant health danger for the elderly. If the problem is not detected in a timely manner, it can result in the death or impairment of the elderly, lowering their quality of life. Falls are a major public health concern for the elderly around the world. When it comes to old age, we must keep an eye on our loved ones to ensure their health and safety. It is therefore critical to determine if an elderly person has fallen so that help can be provided promptly. Proposing a person fall detection system based on a wearable device for detecting the falls of people in every situation, which takes advantage of lowpower wireless sensor networks, smart devices, and analyses human body motions. The system detects movement using an accelerometer and a gyro sensor. The sensor is wired to a microprocessor, which transmits the acceleration data continuously. Fall detection and sudden movement changes in the person would be monitored by the system. The sensors are getting values from a quick movement shift with shock in the system. When a person falls and becomes unconscious, the system determines whether the person has indeed fallen. If the person has truly fallen, the system will send an alert to the caregivers and sound an alarm to alert anyone nearby. When the system detects that a person has fallen, it immediately sends an alert to the individual's care takers. It is an IoT-based fall detection system that assists people by telling their caregivers about their fall so that quick attention may be drawn to the situation and essential actions can be taken to save the person who has fallen. Keywords: Threshold Based Fall Detection, Arduino, Bi-Axial, Accelerometer, Gyroscope,
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Iazzi, Abderrazak, Mohammed Rziza, and Rachid Oulad Haj Thami. "Fall Detection System-Based Posture-Recognition for Indoor Environments." Journal of Imaging 7, no. 3 (February 26, 2021): 42. http://dx.doi.org/10.3390/jimaging7030042.
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The majority of the senior population lives alone at home. Falls can cause serious injuries, such as fractures or head injuries. These injuries can be an obstacle for a person to move around and normally practice his daily activities. Some of these injuries can lead to a risk of death if not handled urgently. In this paper, we propose a fall detection system for elderly people based on their postures. The postures are recognized from the human silhouette which is an advantage to preserve the privacy of the elderly. The effectiveness of our approach is demonstrated on two well-known datasets for human posture classification and three public datasets for fall detection, using a Support-Vector Machine (SVM) classifier. The experimental results show that our method can not only achieves a high fall detection rate but also a low false detection.
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Hasegawa, Tadahiro, and Hiroki Yokota. "Verification of Fall Detection Sensor." Journal of Robotics and Mechatronics 24, no. 6 (December 20, 2012): 1089–91. http://dx.doi.org/10.20965/jrm.2012.p1089.
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We report about a fall-detection sensor system that detects the beginning of a fall. The fall-detection sensor system monitors the human pulse as a vital sign, acceleration, and angular velocity. The fall-detection algorithm using these signals enables us to detect the beginning of a fall correctly. Especially, the fall detection algorithm using the vital sign was able to distinguish between a case in which persons did not fall down due to keep their balance although the fall began and a case in which persons fell.