Journal articles: 'Human gait model'

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Otoda, Yuji, Hiroshi Kimura, and Kunikatsu Takase. "Construction of Gait Adaptation Model in Human Splitbelt Treadmill Walking." Applied Bionics and Biomechanics 6, no. 3-4 (2009): 269–84. http://dx.doi.org/10.1155/2009/305061.

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There are a huge number of studies that measure kinematics, dynamics, the oxygen uptake and so on in human walking on the treadmill. Especially in walking on the splitbelt treadmill where the speed of the right and left belt is different, remarkable differences in kinematics are seen between normal and cerebellar disease subjects. In order to construct the gait adaptation model of such human splitbelt treadmill walking, we proposed a simple control model and made a newly developed 2D biped robot walk on the splitbelt treadmill. We combined the conventional limit-cycle based control consisting of joint PD-control, cyclic motion trajectory planning and a stepping reflex with a newly proposed adjustment of P-gain at the hip joint of the stance leg. We showed that the data of robot (normal subject model and cerebellum disease subject model) experiments had high similarities with the data of normal subjects and cerebellum disease subjects experiments carried out by Reisman et al. (2005) and Morton and Bastian (2006) in ratios and patterns. We also showed that P-gain at the hip joint of the stance leg was the control parameter of adaptation for symmetric gaits in splitbelt walking and P-gain adjustment corresponded to muscle stiffness adjustment by the cerebellum. Consequently, we successfully proposed the gait adaptation model in human splitbelt treadmill walking and confirmed the validity of our hypotheses and the proposed model using the biped robot.

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Bhangale, Ashish. "Human Gait Model for Automatic Extraction and Description for Gait Recognition." International Journal on Bioinformatics & Biosciences 2, no. 2 (June 30, 2012): 15–28. http://dx.doi.org/10.5121/ijbb.2012.2202.

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Duan, X. H., R. H. Allen, and J. Q. Sun. "A stiffness-varying model of human gait." Medical Engineering & Physics 19, no. 6 (September 1997): 518–24. http://dx.doi.org/10.1016/s1350-4533(97)00022-2.

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Ashkenazy, Yosef, Jeffrey M. Hausdorff, Plamen Ch. Ivanov, and H. Eugene Stanley. "A stochastic model of human gait dynamics." Physica A: Statistical Mechanics and its Applications 316, no. 1-4 (December 2002): 662–70. http://dx.doi.org/10.1016/s0378-4371(02)01453-x.

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Abdolvahab, Mohammad. "A synergetic model for human gait transitions." Physica A: Statistical Mechanics and its Applications 433 (September 2015): 74–83. http://dx.doi.org/10.1016/j.physa.2015.03.049.

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Lacker, HM, TH Choi, S. Schenk, B. Gupta, RP Narcessian, SA Sisto, S. Massood, et al. "21 A mathematical model of human gait dynamics." Gait & Posture 5, no. 2 (April 1997): 176. http://dx.doi.org/10.1016/s0966-6362(97)83418-2.

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Zeng, Wei, Cong Wang, and Yuanqing Li. "Model-Based Human Gait Recognition Via Deterministic Learning." Cognitive Computation 6, no. 2 (June 7, 2013): 218–29. http://dx.doi.org/10.1007/s12559-013-9221-4.

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Alsaif, Omar Ibrahim, Saba Qasim Hasan, and Abdulrafa Hussain Maray. "Using skeleton model to recognize human gait gender." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 974. http://dx.doi.org/10.11591/ijai.v12.i2.pp974-983.

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Biometrics became fairly important to help people identifications persons by their individualities or features. In this paper, gait recognition has been based on a skeleton model as an important indicator in prevalent activities. Using the reliable dataset for the Chinese Academy of Sciences (CASIA) of silhouettes class C database. Each video has been discredited to 75 frames for each (20 persons (10 males and 10 females)) as (1.0), the result will be 1,500 frames. After Pre-processing the images, many features are extracted from human silhouette images. For gender classification, the human walking skeleton used in this study. The model proposed is based on morphological processes on the silhouette images. The common angle has been computed for the two legs. Later, principal components analysis (PCA) was applied to reduce data using feature selection technology to get the most useful information in gait analysis. Applying two classifiers artificial neural network (ANN) and Gaussian Bayes to distinguish male or female for each classifier. The experimental results for the suggested method provided significant accomplishing about (95.5%), and accuracy of (75%). Gender classification using ANN is more efficient from the Gaussian Bayes technique by (20%), where ANN technique has given a superior performance in recognition.

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Yang, Fan, Jun Wang, and Jin Ping Sun. "Human Gaits Differentiation Based on Micro-Doppler Features." Advanced Materials Research 846-847 (November 2013): 203–6. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.203.

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The radar micro-Doppler signatures of human gait have been investigated and applied to many practical use since late 1990s.Human body can be modeled as jointly connected rigid segments or links. And yet the model was confined to the walking gait. In order to apply the model to different circumstances, the paper modified the currently used human walking model into three different models. Firstly, the analysis of human crouch walking with arms was given. Then radar echo of the model was provided, whose correctness and effectiveness were proved through simulation. Secondly the other two gaits,running and crawling, were modeled in the same way. Finall the paper constructed scenes that two men walked in varied walking gaits, through simulation conclusion was drawn that if two people walked in cetain different gaits,those two different gaits can be distinguished.

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HUANG, BUFU, MENG CHEN, KA KEUNG LEE, and YANGSHENG XU. "HUMAN IDENTIFICATION BASED ON GAIT MODELING." International Journal of Information Acquisition 04, no. 01 (March 2007): 27–38. http://dx.doi.org/10.1142/s0219878907001137.

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Human gait is a dynamic biometrical feature which is complex and difficult to imitate. It is unique and more secure than static features such as passwords, fingerprints and facial features. In this paper, we present intelligent shoes for human identification based on human gait modeling and similarity evaluation with hidden Markov models (HMMs). Firstly we describe the intelligent shoe system for collecting human dynamic gait performance. Using the proposed machine learning method hidden Markov models, an individual wearer's gait model is derived and we then demonstrate the procedure for recognizing different wearers by analyzing the corresponding models. Next, we define a hidden-Markov-model-based similarity measure which allows us to evaluate resultant learning models. With the most likely performance criterion, it will help us to derive the similarity of individual behavior and its corresponding model. By utilizing human gait modeling and similarity evaluation based on hidden Markov models, the proposed method has produced satisfactory results for human identification during testing.

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Lee, Kevin, and Wei Tang. "A Fully Wireless Wearable Motion Tracking System with 3D Human Model for Gait Analysis." Sensors 21, no. 12 (June 12, 2021): 4051. http://dx.doi.org/10.3390/s21124051.

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This paper presents a wearable motion tracking system with recording and playback features. This system has been designed for gait analysis and interlimb coordination studies. It can be implemented to help reduce fall risk and to retrain gait in a rehabilitation setting. Our system consists of ten custom wearable straps, a receiver, and a central computer. Comparing with similar existing solutions, the proposed system is affordable and convenient, which can be used in both indoor and outdoor settings. In the experiment, the system calculates five gait parameters and has the potential to identify deviant gait patterns. The system can track upper body parameters such as arm swing, which has potential in the study of pathological gaits and the coordination of the limbs.

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Flux, E., M. M. van der Krogt, P. Cappa, M. Petrarca, K. Desloovere, and J. Harlaar. "The Human Body Model versus conventional gait models for kinematic gait analysis in children with cerebral palsy." Human Movement Science 70 (April 2020): 102585. http://dx.doi.org/10.1016/j.humov.2020.102585.

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Ackermann, Marko, and Antonie J. van den Bogert. "Optimality principles for model-based prediction of human gait." Journal of Biomechanics 43, no. 6 (April 2010): 1055–60. http://dx.doi.org/10.1016/j.jbiomech.2009.12.012.

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Nabila, Mansouri, Aouled Issa Mohammed, and Ben Jemaa Yousra. "Gait‐based human age classification using a silhouette model." IET Biometrics 7, no. 2 (July 27, 2017): 116–24. http://dx.doi.org/10.1049/iet-bmt.2016.0176.

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Adi Izhar, Che Ani, Z. Hussain, M. I. F. Maruzuki, Mohd Suhaimi Sulaiman, and A. A. Abd. Rahim. "Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 752. http://dx.doi.org/10.11591/ijai.v10.i3.pp752-763.

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The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.

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Liu, Long, Huihui Wang, Haorui Li, Jiayi Liu, Sen Qiu, Hongyu Zhao, and Xiangyang Guo. "Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model." Sensors 21, no. 4 (February 14, 2021): 1347. http://dx.doi.org/10.3390/s21041347.

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Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

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Wang, Yingnan, Yueming Yang, and Yan Li. "Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images." Traitement du Signal 37, no. 6 (December 31, 2020): 1085–91. http://dx.doi.org/10.18280/ts.370621.

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Based on the residual network and long short-term memory (LSTM) network, this paper proposes a human walking gait recognition method, which relies on the vector image of human walking features and the dynamic lower limb model with multiple degrees-of-freedom (DOFs). Firstly, a human pose estimation algorithm was designed based on deep convolutional neural network (DCNN), and used to obtain the vector image of human walking features. Then, the movements of human lower limbs were described by a simplified model, and the dynamic eigenvectors of the simplified model were obtained by Lagrange method, revealing the mapping relationship between eigenvectors in gait fitting. To analyze the difference of human walking gaits more accurately, a feature learning and recognition algorithm was developed based on residual network, and proved accurate and robust through experiments on the data collected from a public gait database.

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Arora, Parul, Smriti Srivastava, and Shivank Singhal. "Analysis of Gait Flow Image and Gait Gaussian Image Using Extension Neural Network for Gait Recognition." International Journal of Rough Sets and Data Analysis 3, no. 2 (April 2016): 45–64. http://dx.doi.org/10.4018/ijrsda.2016040104.

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This paper proposes a new technique to recognize human gait by combining model free feature extraction approaches and a classifier. Gait flow image (GFI) and gait Gaussian image (GGI) are the two feature extraction techniques used in combination with ENN. GFI is a gait period based technique, uses optical flow features. So it directly focuses on dynamic part of human gait. GGI is another gait period based technique, computed by applying Gaussian membership function on human silhouettes. Next, ENN has been used as a classifier which combines the extension theory and neural networks. All the study has been done on CASIA-A and OU-ISIR treadmill B databases. The results derived using ENN are compared with SVM (support vector machines) and NN (Nearest neighbor) classifiers. ENN proved to give good accuracy and less iteration as compared to other traditional methods.

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Konz, Latisha, Andrew Hill, and Farnoush Banaei-Kashani. "ST-DeepGait: A Spatiotemporal Deep Learning Model for Human Gait Recognition." Sensors 22, no. 20 (October 21, 2022): 8075. http://dx.doi.org/10.3390/s22208075.

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Human gait analysis presents an opportunity to study complex spatiotemporal data transpiring as co-movement patterns of multiple moving objects (i.e., human joints). Such patterns are acknowledged as movement signatures specific to an individual, offering the possibility to identify each individual based on unique gait patterns. We present a spatiotemporal deep learning model, dubbed ST-DeepGait, to featurize spatiotemporal co-movement patterns of human joints, and accordingly classify such patterns to enable human gait recognition. To this end, the ST-DeepGait model architecture is designed according to the spatiotemporal human skeletal graph in order to impose learning the salient local spatial dynamics of gait as they occur over time. Moreover, we employ a multi-layer RNN architecture to induce a sequential notion of gait cycles in the model. Our experimental results show that ST-DeepGait can achieve recognition accuracy rates over 90%. Furthermore, we qualitatively evaluate the model with the class embeddings to show interpretable separability of the features in geometric latent space. Finally, to evaluate the generalizability of our proposed model, we perform a zero-shot detection on 10 classes of data completely unseen during training and achieve a recognition accuracy rate of 88% overall. With this paper, we also contribute our gait dataset captured with an RGB-D sensor containing approximately 30 video samples of each subject for 100 subjects totaling 3087 samples. While we use human gait analysis as a motivating application to evaluate ST-DeepGait, we believe that this model can be simply adopted and adapted to study co-movement patterns of multiple moving objects in other applications such as in sports analytics and traffic pattern analysis.

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Gupta, Jay Prakash, Nishant Singh, Pushkar Dixit, Vijay Bhaskar Semwal, and Shiv Ram Dubey. "Human Activity Recognition Using Gait Pattern." International Journal of Computer Vision and Image Processing 3, no. 3 (July 2013): 31–53. http://dx.doi.org/10.4018/ijcvip.2013070103.

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Vision-based human activity recognition is the process of labelling image sequences with action labels. Accurate systems for this problem are applied in areas such as visual surveillance, human computer interaction and video retrieval. The challenges are due to variations in motion, recording settings and gait differences. Here the authors propose an approach to recognize the human activities through gait. Activity recognition through Gait is the process of identifying an activity by the manner in which they walk. The identification of human activities in a video, such as a person is walking, running, jumping, jogging etc are important activities in video surveillance. The authors contribute the use of Model based approach for activity recognition with the help of movement of legs only. Experimental results suggest that their method are able to recognize the human activities with a good accuracy rate and robust to shadows present in the videos.

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Tahmoush, Dave, and Jerry Silvious. "Gait Variations in Human Micro-Doppler." International Journal of Electronics and Telecommunications 57, no. 1 (March 1, 2011): 23–28. http://dx.doi.org/10.2478/v10177-011-0003-1.

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Gait Variations in Human Micro-DopplerMeasurement of human gait variation is important for security applications such as the indication of unexpected loading due to concealed weapons. To observe humans safely, unobtrusively, and without privacy issues, radar provides one method to detect abnormal activity without using images. In this paper we focus on modeling the characteristics of human walking parameters in order to determine signature differences that are distinguishable and to determine the variability of normal walking to be compared to armed or loaded walking. We extract micro-Doppler from motion-captured human gait models and verify the models with radar measurements. We then vary the model to determine the extent of normal micro-Doppler variation in multiple dimensions of human gait. We also characterize the ability of radar to determine gender and suggest that alternative views to the frontal view may be more discriminative.

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Otoda, Yuji, Hiroshi Kimura, and Kunikatsu Takase. "Construction of gait adaptation model in human splitbelt treadmill walking." Applied Bionics and Biomechanics 6, no. 3-4 (December 2, 2009): 269–84. http://dx.doi.org/10.1080/11762320902944476.

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Vimieiro, Claysson, Emanuel Andrada, Hartmut Witte, and Marcos Pinotti. "A computational model for dynamic analysis of the human gait." Computer Methods in Biomechanics and Biomedical Engineering 18, no. 7 (October 25, 2013): 799–804. http://dx.doi.org/10.1080/10255842.2013.848859.

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Millard, Matthew, Eric Kubica, and John McPhee. "Forward dynamic human gait simulation using a SLIP target model." Procedia IUTAM 2 (2011): 142–57. http://dx.doi.org/10.1016/j.piutam.2011.04.015.

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Santos, A. P., F. Ben Amar, P. Bidaud, and E. Desailly. "Gait synthesis for an anthropomorphic human model with articulated feet." Computer Methods in Biomechanics and Biomedical Engineering 18, sup1 (August 5, 2015): 2056–57. http://dx.doi.org/10.1080/10255842.2015.1069620.

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Tafazzoli, Faezeh, and Reza Safabakhsh. "Model-based human gait recognition using leg and arm movements." Engineering Applications of Artificial Intelligence 23, no. 8 (December 2010): 1237–46. http://dx.doi.org/10.1016/j.engappai.2010.07.004.

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Hase, Kazunori, Kazuo Miyashita, Sooyol Ok, and Yoshiki Arakawa. "Human gait simulation with a neuromusculoskeletal model and evolutionary computation." Journal of Visualization and Computer Animation 14, no. 2 (2003): 73–92. http://dx.doi.org/10.1002/vis.306.

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Olenšek, Andrej, and Zlatko Matjačić. "Human-like control strategy of a bipedal walking model." Robotica 26, no. 3 (May 2008): 295–306. http://dx.doi.org/10.1017/s0263574707004055.

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SUMMARYThis paper presents a two-level control strategy for bipedal walking mechanism that accounts for implicit control of push-off on the between-step control level and tracking of imposed holonomic constraints on kinematic variables via feedback control on within-step control level. The proposed control strategy was tested in a biologically inspired model with minimal set of segments that allows evolution of human-like push-off and power absorption. We investigated controller's stability characteristics by using Poincaré return map analysis in eight simulation cases and further evaluated the performance of the biped walking model in terms of how variations in torso position and gait velocity relate to push-off and power absorption. The results show that the proposed control strategy, with the same set of controller's gains, enables stable walking in a variety of chosen gait parameters and can accommodate to various trunk inclinations and gait velocities in a similar way as seen in humans.

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Gonzalez-Islas, Juan-Carlos, Omar-Arturo Dominguez-Ramirez, Omar Lopez-Ortega, Jonatan Peña-Ramirez, Jesus-Patricio Ordaz-Oliver, and Francisco Marroquin-Gutierrez. "Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics." Applied Sciences 12, no. 20 (October 11, 2022): 10197. http://dx.doi.org/10.3390/app122010197.

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Crouch gait is one of the most common gait abnormalities; it is usually caused by cerebral palsy. There are few works related to the modeling of crouch gait kinematics, crouch gait analysis, and visualization in both the workspace and joint space. In this work, we present a quaternion-based method to solve the forward kinematics of the position of the lower limbs during walking. For this purpose, we propose a modified eight-DoF human skeletal model. Using this model, we present a geometric method to calculate the gait inverse kinematics. Both methods are applied for gait analysis over normal, mild, and severe crouch gaits, respectively. A metric-based comparison of workspace and joint space for the three gaits for a gait cycle is conducted. In addition, gait visualization is performed using Autodesk Maya for the three anatomical planes. The obtained results allow us to determine the capabilities of the proposed methods to assess the performance of crouch gaits, using a normal pattern as a reference. Both forward and inverse kinematic methods could ultimately be applied in rehabilitation settings for the diagnosis and treatment of diseases derived from crouch gaits or other types of gait abnormalities.

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Rahman, Wasifur, Masum Hasan, Md Saiful Islam, Titilayo Olubajo, Jeet Thaker, Abdel-Rahman Abdelkader, Phillip Yang, et al. "Auto-Gait." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 7, no. 1 (March 27, 2022): 1–19. http://dx.doi.org/10.1145/3580845.

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Many patients with neurological disorders, such as Ataxia, do not have easy access to neurologists, -especially those living in remote localities and developing/underdeveloped countries. Ataxia is a degenerative disease of the nervous system that surfaces as difficulty with motor control, such as walking imbalance. Previous studies have attempted automatic diagnosis of Ataxia with the help of wearable biomarkers, Kinect, and other sensors. These sensors, while accurate, do not scale efficiently well to naturalistic deployment settings. In this study, we propose a method for identifying ataxic symptoms by analyzing videos of participants walking down a hallway, captured with a standard monocular camera. In a collaboration with 11 medical sites located in 8 different states across the United States, we collected a dataset of 155 videos along with their severity rating from 89 participants (24 controls and 65 diagnosed with or are pre-manifest spinocerebellar ataxias). The participants performed the gait task of the Scale for the Assessment and Rating of Ataxia (SARA). We develop a computer vision pipeline to detect, track, and separate the participants from their surroundings and construct several features from their body pose coordinates to capture gait characteristics such as step width, step length, swing, stability, speed, etc. Our system is able to identify and track a patient in complex scenarios. For example, if there are multiple people present in the video or an interruption from a passerby. Our Ataxia risk-prediction model achieves 83.06% accuracy and an 80.23% F1 score. Similarly, our Ataxia severity-assessment model achieves a mean absolute error (MAE) score of 0.6225 and a Pearson's correlation coefficient score of 0.7268. Our model competitively performed when evaluated on data from medical sites not used during training. Through feature importance analysis, we found that our models associate wider steps, decreased walking speed, and increased instability with greater Ataxia severity, which is consistent with previously established clinical knowledge. Furthermore, we are releasing the models and the body-pose coordinate dataset to the research community - the largest dataset on ataxic gait (to our knowledge). Our models could contribute to improving health access by enabling remote Ataxia assessment in non-clinical settings without requiring any sensors or special cameras. Our dataset will help the computer science community to analyze different characteristics of Ataxia and to develop better algorithms for diagnosing other movement disorders.

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Wang, Yan, Zhikang Li, Xin Wang, Hongnian Yu, Wudai Liao, and Damla Arifoglu. "Human Gait Data Augmentation and Trajectory Prediction for Lower-Limb Rehabilitation Robot Control Using GANs and Attention Mechanism." Machines 9, no. 12 (December 18, 2021): 367. http://dx.doi.org/10.3390/machines9120367.

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To date, several alterations in the gait pattern can be treated through rehabilitative approaches and robot assisted therapy (RAT). Gait data and gait trajectories are essential in specific exoskeleton control strategies. Nevertheless, the scarcity of human gait data due to the high cost of data collection or privacy concerns can hinder the performance of controllers or models. This paper thus first creates a GANs-based (Generative Adversarial Networks) data augmentation method to generate synthetic human gait data while still retaining the dynamics of the real gait data. Then, both the real collected and the synthesized gait data are fed to our constructed two-stage attention model for gait trajectories prediction. The real human gait data are collected with the five healthy subjects recruited from an optical motion capture platform. Experimental results indicate that the created GANs-based data augmentation model can synthesize realistic-looking multi-dimensional human gait data. Also, the two-stage attention model performs better compared with the LSTM model; the attention mechanism shows a higher capacity of learning dependencies between the historical gait data to accurately predict the current values of the hip joint angles and knee joint angles in the gait trajectory. The predicted gait trajectories depending on the historical gait data can be further used for gait trajectory tracking strategies.

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Liu, Ruzhang, Luyin Liu, Guochao Ma, Shanshan Feng, Yuanhui Mu, Dexi Meng, Shuying Wang, and Enlin Cai. "Visual Gait Analysis Based on UE4." Sensors 23, no. 12 (June 9, 2023): 5463. http://dx.doi.org/10.3390/s23125463.

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With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body’s structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body’s motion posture, which has great application prospects in the field of gait analysis.

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Kovač, Jure, and Peter Peer. "Human Skeleton Model Based Dynamic Features for Walking Speed Invariant Gait Recognition." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/484320.

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Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometrics can be captured at public places from a distance without subject's collaboration, awareness, and even consent. Although current approaches give encouraging results, we are still far from effective use in real-life applications. In general, methods set various constraints to circumvent the influence of covariate factors like changes of walking speed, view, clothing, footwear, and object carrying, that have negative impact on recognition performance. In this paper we propose a skeleton model based gait recognition system focusing on modelling gait dynamics and eliminating the influence of subjects appearance on recognition. Furthermore, we tackle the problem of walking speed variation and propose space transformation and feature fusion that mitigates its influence on recognition performance. With the evaluation on OU-ISIR gait dataset, we demonstrate state of the art performance of proposed methods.

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Mu, Li Ming. "The Three-Dimensional Visual Gaits Simulation Studies for the Disabled Athletes." Applied Mechanics and Materials 556-562 (May 2014): 4547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4547.

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The paper carries research on the accurate modeling issue for the disabled athletes’ three-dimensional visual gaits. The three-dimensional visual gaits are important gait features when the disabled athletes are walking which have some regularity. However the features present in a mess. The traditional three-dimensional visual gaits model based on visual images don’t consider the messy problems in the gait feature resulting in lower dimensional simulation fidelity. This paper proposes a three-dimensional visual gait modeling method using the human dynamics which can obtain the body features of the three-dimensional visual gaits by analyzing the periodic features of the gaits. The experimental results show that the model can quickly and accurately emulate the three-dimensional visual gaits for the disabled athletes with higher fidelity and lower errors.

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Su, Hai Long, and Da Wei Zhang. "Research on Pre-Slip Gait Mechanical Contributions and Gait Self-Balancing Mechanics during Walking." Applied Mechanics and Materials 164 (April 2012): 383–86. http://dx.doi.org/10.4028/www.scientific.net/amm.164.383.

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Walking is a complex dynamic task that requires the regulation of the whole-body angular momentum to maintain dynamic balance while performing walking subtasks such as propelling the body forward and accelerating the leg into swing. To investigate the characteristic of slips and falls during gait self-balancing, a method was proposed that could better understand the effects of pre-slip gait response biomechanics on the risk for falls. A new segmental model of the human body was developed and this model would be used continuously measured locations from nearly 85 points on the body to produce a dynamic postural record of human movement. The muscles surrounding the hip were found to be most important in maintaining control of the trunk and preventing collapse in response to the forward perturbations (FP).

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Rajagopal, Apoorva, Christopher L. Dembia, Matthew S. DeMers, Denny D. Delp, Jennifer L. Hicks, and Scott L. Delp. "Full-Body Musculoskeletal Model for Muscle-Driven Simulation of Human Gait." IEEE Transactions on Biomedical Engineering 63, no. 10 (October 2016): 2068–79. http://dx.doi.org/10.1109/tbme.2016.2586891.

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S. S. Anupama, C., Rafina Zakieva, Afanasiy Sergin, E. Laxmi Lydia, Seifedine Kadry, Chomyong Kim, and Yunyoung Nam. "Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model." Intelligent Automation & Soft Computing 37, no. 2 (2023): 1453–68. http://dx.doi.org/10.32604/iasc.2023.038321.

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Anderson, Frank C., and Marcus G. Pandy. "Dynamic Optimization of Human Walking." Journal of Biomechanical Engineering 123, no. 5 (May 16, 2001): 381–90. http://dx.doi.org/10.1115/1.1392310.

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A three-dimensional, neuromusculoskeletal model of the body was combined with dynamic optimization theory to simulate normal walking on level ground. The body was modeled as a 23 degree-of-freedom mechanical linkage, actuated by 54 muscles. The dynamic optimization problem was to calculate the muscle excitation histories, muscle forces, and limb motions subject to minimum metabolic energy expenditure per unit distance traveled. Muscle metabolic energy was calculated by summing five terms: the basal or resting heat, activation heat, maintenance heat, shortening heat, and the mechanical work done by all the muscles in the model. The gait cycle was assumed to be symmetric; that is, the muscle excitations for the right and left legs and the initial and terminal states in the model were assumed to be equal. Importantly, a tracking problem was not solved. Rather, only a set of terminal constraints was placed on the states of the model to enforce repeatability of the gait cycle. Quantitative comparisons of the model predictions with patterns of body-segmental displacements, ground-reaction forces, and muscle activations obtained from experiment show that the simulation reproduces the salient features of normal gait. The simulation results suggest that minimum metabolic energy per unit distance traveled is a valid measure of walking performance.

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Gupta, Anand, and Pragya Goel. "ST-Gait: A Framework for Human Identification Using Structural and Transitional Characteristics of Gait." Advanced Materials Research 403-408 (November 2011): 850–57. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.850.

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An interesting problem in Biometrics Research is human identification by reliable extraction of anatomical and behavioral patterns of a person from his manner of walking. Till now the identification through the said characteristics has been addressed by mostly using marker-based methods. These methods have limited applications in the area of security, where a person must be ‘marked’ for correct identification. To mitigate this limitation, we propose a marker-less, model- based approach (ST-Gait) for human identification using gait. The proposed method is simple but effective, and involves low computational complexity. It is validated on a well- known benchmark database (gait database A of CASIA). The encouraging experimental results show that the technique achieves an accuracy of 90% and can be a promising tool for human identification in the area of security.

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Wang, Xiuhui, and Wei Qi Yan. "Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory." International Journal of Neural Systems 30, no. 01 (November 21, 2019): 1950027. http://dx.doi.org/10.1142/s0129065719500278.

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Human gait recognition is one of the most promising biometric technologies, especially for unobtrusive video surveillance and human identification from a distance. Aiming at improving recognition rate, in this paper we study gait recognition using deep learning and propose a novel method based on convolutional Long Short-Term Memory (Conv-LSTM). First, we present a variation of Gait Energy Images, i.e. frame-by-frame GEI (ff-GEI), to expand the volume of available Gait Energy Images (GEI) data and relax the constraints of gait cycle segmentation required by existing gait recognition methods. Second, we demonstrate the effectiveness of ff-GEI by analyzing the cross-covariance of one person’s gait data. Then, making use of the temporality of our human gait, we design a novel gait recognition model using Conv-LSTM. Finally, the proposed method is evaluated extensively based on the CASIA Dataset B for cross-view gait recognition, furthermore the OU-ISIR Large Population Dataset is employed to verify its generalization ability. Our experimental results show that the proposed method outperforms other algorithms based on these two datasets. The results indicate that the proposed ff-GEI model using Conv-LSTM, coupled with the new gait representation, can effectively solve the problems related to cross-view gait recognition.

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Minh, Vu Trieu, Mart Tamre, Victor Musalimov, Pavel Kovalenko, Irina Rubinshtein, Ivan Ovchinnikov, David Krcmarik, Reza Moezzi, and Jaroslav Hlava. "Model Predictive Control for Modeling Human Gait Motions Assisted by Vicon Technology." Journal Européen des Systèmes Automatisés 53, no. 5 (November 15, 2020): 589–600. http://dx.doi.org/10.18280/jesa.530501.

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Human muscles and the central nervous system (CNS) play the key role to control the human movements and activities. The human CNS determines each human motion following three steps: estimation of the movement trajectory; calculation of required energy for muscles; then perform the motion. In these three step tasks, the human CNS determines the first two steps and the human muscles conduct the third one. This paper efforts the use of model predictive control (MPC) algorithm to simulate the human CNS calculation in the case of gait motion. We first build up the human gait motion mathematical model with 5-link mechanism. This allows us to apply MPC to calculate the optimal torques at each joint and optimal trajectory for muscles. Outcomes of simulations simultaneously are compared with the real human movements captured by the Vicon motion capture technology which is the novelty of this study. Results show that tracking errors are not excessed 7%.

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Ghadi, Yazeed, Israr Akhter, Mohammed Alarfaj, Ahmad Jalal, and Kibum Kim. "Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning." PeerJ Computer Science 7 (November 19, 2021): e764. http://dx.doi.org/10.7717/peerj-cs.764.

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The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.

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Mahmoud, Hadeer, and Ahmed Abdelhafeez. "Computational Intelligence Approach for Biometric Gait Identification." International Journal of Advances in Applied Computational Intelligence 2, no. 1 (2023): 36–43. http://dx.doi.org/10.54216/ijaaci.020105.

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Gait recognition has gained significant attention in recent years due to its potential applications in various fields, including surveillance, security, and healthcare. Biometric gait identification, which involves recognizing individuals based on their walking patterns, is a challenging task due to the inherent variations in gait caused by factors such as clothing, footwear, and walking speed. In this paper, we propose a computational intelligence approach for biometric gait identification. Specifically, we integrate an intelligent convolutional model to identify human gaits based on the inertial sensory data captured from the body movement during the human walk. Extensive experiments on two datasets demonstrated that the efficiency of the proposed approach outperforms the existing methods. Our approach has the potential to be used in real-world applications such as surveillance systems and healthcare monitoring, where accurate and efficient identification of individuals based on their gait is crucial.

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Ju, Ming-Shaung, and J. M. Mansour. "Simulation of the Double Limb Support Phase of Human Gait." Journal of Biomechanical Engineering 110, no. 3 (August 1, 1988): 223–29. http://dx.doi.org/10.1115/1.3108435.

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Dynamic mechanical models of the double limb support phase of human gait were developed for both two-dimensional (sagittal plane) and three-dimensional motion. A “foot” model with a curved plantar surface was also developed such that the model foot motion was kinematically equivalent to that of a walking subject. This foot model was incorporated into the planar model for double limb support. The dynamic formulations were based on Kane’s method and were implemented symbolically using MACSYMA. The development of the formulations for the constrained systems, application of these formulations to the study of normal gait, the sensitivity of the simulation to the frequency content of the input data, the sensitivity of limb displacements to changes in joint moments and the application of a nonlinear feedback controller to correct for perturbations in limb trajectories were investigated.

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Luo, Jian, and Tardi Tjahjadi. "Gait Recognition and Understanding Based on Hierarchical Temporal Memory Using 3D Gait Semantic Folding." Sensors 20, no. 6 (March 16, 2020): 1646. http://dx.doi.org/10.3390/s20061646.

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Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness.

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Luo, Yue, Sai Ouyang, Caroline Lockwood, Maria D. Ferraz, and Boyi Hu. "Publicly Accessible Wearable Motion Databases for Human Gait Studies." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1718–22. http://dx.doi.org/10.1177/1071181320641417.

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As emerging areas of interest in gait analysis research, gait authentication and activity recognition have drawn significant attention recently, facilitated by microelectromechanical systems (MEMS) and smart devices. Machine learning models have been developed to reduce human effort and improve model accuracy in these areas. Sufficient amounts of data become critical for these applications. Unlike other fields such as image processing, in which massive data are easy to collect, human gait data is difficult to collect in large amounts, which makes publicly accessible databases in this area even more valuable. This paper aimed to summarize publicly accessible IMU-based gait databases by surveying the recent literature in human gait analysis. 199 papers were manually evaluated with sixteen data sets included (seven for gait authentication and nine for activity recognition). The detailed technical contents were described and compared in the survey to assist the audience on how to better utilize the data sets.

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Müller, Péter, and Ádám Schiffer. "Human Gait Cycle Analysis Using Kinect V2 Sensor." Pollack Periodica 15, no. 3 (November 7, 2020): 3–14. http://dx.doi.org/10.1556/606.2020.15.3.1.

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Examining a human movement can provide a wealth of information about a patient’s medical condition. The examination process can be used to diagnose abnormal changes (lesions), ability development and monitor the rehabilitation process of people with reduced mobility. There are several approaches to monitor people, among other things with sensors and various imaging and processing devices. In this case a Kinect V2 sensor and a self-developed LabView based application was used, to examine the movement of the lower limbs. The ideal gait pattern was recorded in the RoboGait training machine and the measured data was used to identify the phases of the human gait. During the evaluation, the position of the skeleton model, the associated body joints and angles can be calculated. The pre-recorded ideal and natural gait cycle can be compared.With the self-developed method the pre-recorded ideal and natural gait cycle can be compared and processed for further evaluation. The evaluated measurement data confirm that a reliable and mobile solution for gait analysis has been created.

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Yang, Ning, Jin Tao Li, and Rong Wang. "A Method of Lower Limb Joint Points Extraction Based on Pendulum Model under Arbitrary Gesture Walk." Applied Mechanics and Materials 556-562 (May 2014): 4347–51. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4347.

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The position extraction of lower limb joint points is important for gait recognition because the feature data is always based on the position of lower limb joint points. Since the detection of motion information of human body can affect the gait recognition directly, we propose a position extraction method of lower limb joint points in this paper. Through the study on the human body centroid tracking, and positioning of human lower limb joint point, we can obtain the step cycle information. It has been demonstrated via plenty experiments that the proposed method is feasible and easy for implement, since it can achieve real-time tracking and improve positioning accuracy of the human body joints, and can provide feature data for human gait recognition.

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Ren, Bin, Jianwei Liu, and Jiayu Chen. "Simulating human–machine coupled model for gait trajectory optimization of the lower limb exoskeleton system based on genetic algorithm." International Journal of Advanced Robotic Systems 17, no. 1 (January 1, 2020): 172988141989349. http://dx.doi.org/10.1177/1729881419893493.

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The lower limb exoskeleton robot is capable of providing assisted walking and enhancing exercise ability of humans. The coupling human–machine model has attracted a lot of research efforts to solve the complex dynamics and nonlinearity within the system. This study focuses on an approach of gait trajectory optimization of lower limb exoskeleton coupled with human through genetic algorithm. The human–machine coupling system is studied in this article through multibody virtual simulation environment. Planning of the motion trajectory is carried out by the genetic algorithm, which is iteratively generated under optimization of a set of specially designed fitness functions. Human motion captured data are used to guide the evolution of gait trajectory generation method based on genetic algorithm. Experiments are carried out using the MATLAB/Simulink Multibody physical simulation engine and genetic algorithm-toolbox to generate a more natural gait trajectory, the results show that the proposed gait trajectory generation method can provide an anthropomorphic gait for lower limb exoskeleton device.

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Choi, Jiwoo, Sangil Choi, and Taewon Kang. "Smartphone Authentication System Using Personal Gaits and a Deep Learning Model." Sensors 23, no. 14 (July 14, 2023): 6395. http://dx.doi.org/10.3390/s23146395.

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In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication.

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Journal articles on the topic 'Human gait model'

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