Статті в журналах: "Human Motion Data Analysis"

1

Dong, Ran, Dongsheng Cai, and Soichiro Ikuno. "Motion Capture Data Analysis in the Instantaneous Frequency-Domain Using Hilbert-Huang Transform." Sensors 20, no. 22 (November 16, 2020): 6534. http://dx.doi.org/10.3390/s20226534.

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Motion capture data are widely used in different research fields such as medical, entertainment, and industry. However, most motion researches using motion capture data are carried out in the time-domain. To understand human motion complexities, it is necessary to analyze motion data in the frequency-domain. In this paper, to analyze human motions, we present a framework to transform motions into the instantaneous frequency-domain using the Hilbert-Huang transform (HHT). The empirical mode decomposition (EMD) that is a part of HHT decomposes nonstationary and nonlinear signals captured from the real-world experiments into pseudo monochromatic signals, so-called intrinsic mode function (IMF). Our research reveals that the multivariate EMD can decompose complicated human motions into a finite number of nonlinear modes (IMFs) corresponding to distinct motion primitives. Analyzing these decomposed motions in Hilbert spectrum, motion characteristics can be extracted and visualized in instantaneous frequency-domain. For example, we apply our framework to (1) a jump motion, (2) a foot-injured gait, and (3) a golf swing motion.

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Huang, Zhenzhen, Qiang Niu, and Shuo Xiao. "Human Behavior Recognition Based on Motion Data Analysis." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (December 2, 2019): 2056005. http://dx.doi.org/10.1142/s0218001420560054.

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The development of sensor technologies and smart devices has made it possible to realize real-time data acquisition of human beings. Human behavior monitoring is the process of obtaining activity information with wearables and computer technology. In this paper, we design a data preprocessing method based on the data collected by a single three-axis accelerometer. We first use Butterworth filter as low-pass filtering to remove the noise. Then, we propose a KGA algorithm to remove abnormal data and smooth them at the same time. This method uses genetic algorithm to optimize the parameters of Kalman filter. After that, we use a threshold-based method to identify falls that are harmful to the elderly. The key point of this method is to distinguish falls from people’s daily activities. According to the characteristics of human falls, we extract eigenvalues that can effectively distinguish daily activities from falls. In addition, we use cross-validation to determine the threshold of the method. The results show that in the analysis of 11 kinds of human daily activities and 15 types of falls, our method can distinguish 15 types of falls. The recognition recall rate in our method reaches 99.1%.

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3

LI, Chun-Peng, Zhao-Qi WANG, and Shi-Hong XIA. "Motion Synthesis for Virtual Human Using Functional Data Analysis." Journal of Software 20, no. 6 (July 14, 2009): 1664–72. http://dx.doi.org/10.3724/sp.j.1001.2009.03332.

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4

Barker, T. M., and P. McCombe. "Discriminant analysis of human kinematic data: Application to human lumbar spinal motion." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 213, no. 6 (June 1999): 447–53. http://dx.doi.org/10.1243/0954411991535059.

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5

Yu, Jian, Jun Yi Cao, and Cheng Guang Li. "Dynamic Modeling and Complexity Analysis of Human Lower Limb under Various Speeds." Applied Mechanics and Materials 868 (July 2017): 212–17. http://dx.doi.org/10.4028/www.scientific.net/amm.868.212.

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Human lower limbs are the most important parts of human body due to their supporting the whole body in the process of human motions. There are many pathological joint diseases and accidental damage, such as traffic accident and falling off from high place, influencing the human daily life seriously. Therefore, dynamic model of human lower limb has received considerable interest from multi-disciplines including flexible mechanisms, smart structures, biomechanics and nonlinear dynamics. This paper establishes the simplified simulation model of human lower limb based on the acquired realistic data from human motions under different speeds. The model can not only describe dynamic characteristics of real lower limb but also can be simulated by realistic human lower limb motion excitation acquired by tri-axial accelerometers and inclinometers in different conditions. Consequently, the detailed dynamic information of human lower limb from the proposed model can be obtained. In order to analyze the variability of human motions, multiscale entropy (MSE) is employed to investigate the complexity of human motion signals for different speeds of motion. Motion transition characteristics under different speeds are exhibited for understanding adaptation mechanism of human motion. The results will be helpful for exoskeleton and lower limb rehabilitation robot.

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6

GAO, CHUNMING, CHANGHUI LI, GUANGHUA TAN, SONGRUI GUO, and KE XIAO. "ADAPTIVE SEGMENTATION APPROACH FOR HUMAN ACTION DATA." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 08 (December 2014): 1455012. http://dx.doi.org/10.1142/s021800141455012x.

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Temporal segmentation of human motion data is an essential preparation process for action recognition. Due to the variability in the temporal scale of human action and the complexity of representing articulated motion, the research of it encounters many difficulties. Especially, when the number of behaviors contained in the motion sequences is unknown in advance, traditional algorithms cannot segment sequences successfully. In this paper, we extend previous works on change-points detection by probabilistic principle component analysis (PPCA). Based on it, an algorithm which is an extension of PCA and Maximum Mean Discrepancy between samples is proposed for estimating the cluster number. Finally, we optimize our approach and detect cyclic units of each action by aligned cluster analysis. We evaluate and compare the approach with the state-of-the-art methods on Synthetic data, Motion Capture Dataset and Kinect data. Experimental results demonstrate the effectiveness of our approach.

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7

Zeng, Ming, Zai Xin Yang, Hong Lin Ren, and Qing Hao Meng. "Multichannel Human Motion Similarity Analysis Based on Information Entropy and Dynamic Time Warping." Applied Mechanics and Materials 687-691 (November 2014): 847–51. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.847.

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Evaluating motion similarity between trainer and trainee is a key part in computer-assisted sports teaching system. Our similarity evaluation algorithm mainly contains four steps. Firstly, the multichannel 3D human motion data are captured using the Kinect, a depth sensor of Microsoft. Next, in order to greatly reduce the amount of data analysis, the piecewise extremum method (PEM) is applied to achieve this goal. Then, considering that doing the same motions the rhythms of different people are not synchronized, the Dynamic Time Warping algorithm (DTW) is selected to solve the problem of analyzing one channel unequal length motion sequences. Finally, the similarity between the two sets of multichannel human motion sequences can be evaluated using the combined method of the information entropy and DTW. The experimental results indicate that compared with other traditional methods, the proposed method not only accurately measures similarity degree of different motions, but also requires less computational time and memory storage capacity.

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8

Li, Wanyi, Feifei Zhang, Qiang Chen, and Qian Zhang. "Projection Analysis Optimization for Human Transition Motion Estimation." International Journal of Digital Multimedia Broadcasting 2019 (June 2, 2019): 1–9. http://dx.doi.org/10.1155/2019/6816453.

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It is a difficult task to estimate the human transition motion without the specialized software. The 3-dimensional (3D) human motion animation is widely used in video game, movie, and so on. When making the animation, human transition motion is necessary. If there is a method that can generate the transition motion, the making time will cost less and the working efficiency will be improved. Thus a new method called latent space optimization based on projection analysis (LSOPA) is proposed to estimate the human transition motion. LSOPA is carried out under the assistance of Gaussian process dynamical models (GPDM); it builds the object function to optimize the data in the low dimensional (LD) space, and the optimized data in LD space will be obtained to generate the human transition motion. The LSOPA can make the GPDM learn the high dimensional (HD) data to estimate the needed transition motion. The excellent performance of LSOPA will be tested by the experiments.

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9

Perera, Asanka G., Yee Wei Law, Ali Al-Naji, and Javaan Chahl. "Human motion analysis from UAV video." International Journal of Intelligent Unmanned Systems 6, no. 2 (April 16, 2018): 69–92. http://dx.doi.org/10.1108/ijius-10-2017-0012.

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Purpose The purpose of this paper is to present a preliminary solution to address the problem of estimating human pose and trajectory by an aerial robot with a monocular camera in near real time. Design/methodology/approach The distinguishing feature of the solution is a dynamic classifier selection architecture. Each video frame is corrected for perspective using projective transformation. Then, a silhouette is extracted as a Histogram of Oriented Gradients (HOG). The HOG is then classified using a dynamic classifier. A class is defined as a pose-viewpoint pair, and a total of 64 classes are defined to represent a forward walking and turning gait sequence. The dynamic classifier consists of a Support Vector Machine (SVM) classifier C64 that recognizes all 64 classes, and 64 SVM classifiers that recognize four classes each – these four classes are chosen based on the temporal relationship between them, dictated by the gait sequence. Findings The solution provides three main advantages: first, classification is efficient due to dynamic selection (4-class vs 64-class classification). Second, classification errors are confined to neighbors of the true viewpoints. This means a wrongly estimated viewpoint is at most an adjacent viewpoint of the true viewpoint, enabling fast recovery from incorrect estimations. Third, the robust temporal relationship between poses is used to resolve the left-right ambiguities of human silhouettes. Originality/value Experiments conducted on both fronto-parallel videos and aerial videos confirm that the solution can achieve accurate pose and trajectory estimation for these different kinds of videos. For example, the “walking on an 8-shaped path” data set (1,652 frames) can achieve the following estimation accuracies: 85 percent for viewpoints and 98.14 percent for poses.

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10

XIANG, Jian. "Human motion data analysis and retrieval based on 3D feature extraction." Journal of Computer Applications 28, no. 5 (May 20, 2008): 1344–46. http://dx.doi.org/10.3724/sp.j.1087.2008.01344.

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11

Akiduki, Takuma, Zhong Zhang, Takashi Imamura, and Tetsuo Miyake. "Human Motion Analysis from Inertial Sensor Data Based on Nonlinear Dynamics." IFAC Proceedings Volumes 44, no. 1 (January 2011): 7396–401. http://dx.doi.org/10.3182/20110828-6-it-1002.03781.

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12

Chen, Xiaojing. "A Human Motion Function Rehabilitation Monitoring System Based on Data Mining." Scientific Programming 2022 (August 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/2901812.

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Human motion interaction technologies have evolved to a new level with the development of traditional reality technology as science and technology have developed. Fully interactive and human motion interaction technologies are becoming more common in fields such as medical rehabilitation and military simulations. Human motion is at the heart of all activity, and motion analysis and human motion are critical theoretical disciplines. Identification is based on behavior and motion in human motion, with attributes such as effectiveness, intelligence, potent interaction, and rich expression data. When studying human movement, many researchers now prefer this method. However, this study was conducted with insufficient suggestions for real-time human motion function assessment, rehabilitation, and improvement. The development of an information monitoring system for human motion function rehabilitation can be used to evaluate the efficacy of patient rehabilitation training. A human motion function rehabilitation monitoring system is created using an effective and thorough design methodology. The system is made up of the rehabilitation monitoring terminal, the human motion function monitoring module, and the medical center monitoring system. Therefore, the motion-based data mining technique is better for the human motion function rehabilitation monitoring system. The normalized proportion of motion features will assist in the creation of a database for human motion mining. The nonlinear classification function is used in this paper to scientifically categorize human motion features to implement data mining techniques for monitoring human motion function rehabilitation. The effectiveness of patient rehabilitation is significantly increased by the use of a human motion-based rehabilitation monitoring system.

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13

Mei, Feng, Qian Hu, Changxuan Yang, and Lingfeng Liu. "ARMA-Based Segmentation of Human Limb Motion Sequences." Sensors 21, no. 16 (August 19, 2021): 5577. http://dx.doi.org/10.3390/s21165577.

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With the development of human motion capture (MoCap) equipment and motion analysis technologies, MoCap systems have been widely applied in many fields, including biomedicine, computer vision, virtual reality, etc. With the rapid increase in MoCap data collection in different scenarios and applications, effective segmentation of MoCap data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb-bone partition angle body structural representation and autoregressive moving average (ARMA) model fitting. The collected MoCap data were converted into the angle sequence formed by the human limb-bone partition segment and the central spine segment. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements were also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and were labeled by manual segmentation. The proposed algorithm is compared with the principle component analysis (PCA), K-means clustering algorithm (K-means), and back propagation (BP) neural-network-based segmentation algorithms, which shows higher segmentation accuracy due to a more semantic description of human motions by limb-bone partition angles. The results highlight the efficiency and performance of the proposed algorithm, and reveals the potentials of this segmentation model on analyzing inter- and intra-motion sequence distinguishing.

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14

Yong, Ching Yee, Rubita Sudirman, and Kim Mey Chew. "Dark Environment Motion Analysis Using Scalable Model and Vector Angle Technique." Applied Mechanics and Materials 654 (October 2014): 310–14. http://dx.doi.org/10.4028/www.scientific.net/amm.654.310.

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Detecting human existence in video streams is a fundamental task in many video processing applications. In this paper, a novel procedure is produced to model, analyze and recognize human motions (jogging and walking in dark environment) in video streams. There are four major areas that are related in this project for human motion analysis: (1) developing human body structure based on human skeleton model, (2) tracking and data collecting human motion with side view, (3) recognizing human activities from image sequences, and (4) image processing technique using edge detection and vectors angle calculation. All algorithms are developed using MATLAB software. Segmentation is developed to reduce the amount of data and filters out the useless information. Two methods are proposed for angle calculation and activities classification. Results showed that angle between 153.76°-180° for method 1 and 49.64°-92.86° for method 2 is classified as walking while jogging is 95.17°-138.72° for method 1 and 22.62°-56.31° for method 2.

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15

Vadai, Gergely, András Antal, and Zoltán Gingl. "Spectral Analysis of Fluctuations in Humans’ Daily Motion Using Location Data." Fluctuation and Noise Letters 18, no. 02 (May 29, 2019): 1940010. http://dx.doi.org/10.1142/s0219477519400108.

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The interpretation and characterization of universal scaling laws in human mobility and activity are subjects of active research. For the better understanding, instead of the statistical approach we have examined the temporal patterns of human daily motion using location data. The trajectories were measured continuously and with even sampling (1 measurement per minute), using GPS and Wi-Fi/mobile Internet signals of the participant’s smartphone, we have analyzed the few-week-long signals of displacements between two subsequent samplings in frequency domain. Our results had shown that 1/[Formula: see text] type noise is observable over the frequency of the daily rhythm of motion and its harmonics. We point out several technical questions about the measurement and data processing required for further detailed analysis. Furthermore, our new observation could help in the investigation of the underlying dynamics of human motion and opens several theoretical questions about the relationship between the spatial and temporal universality.

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16

Zhang, Zonghua, and Nikolaus F. Troje. "3D Periodic Human Motion Reconstruction from 2D Motion Sequences." Neural Computation 19, no. 5 (May 2007): 1400–1421. http://dx.doi.org/10.1162/neco.2007.19.5.1400.

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We present and evaluate a method of reconstructing three-dimensional (3D) periodic human motion from two-dimensional (2D) motion sequences. Using Fourier decomposition, we construct a compact representation for periodic human motion. A low-dimensional linear motion model is learned from a training set of 3D Fourier representations by means of principal components analysis. Two-dimensional test data are projected onto this model with two approaches: least-square minimization and calculation of a maximum a posteriori probability using the Bayes' rule. We present two different experiments in which both approaches are applied to 2D data obtained from 3D walking sequences projected onto a plane. In the first experiment, we assume the viewpoint is known. In the second experiment, the horizontal viewpoint is unknown and is recovered from the 2D motion data. The results demonstrate that by using the linear model, not only can missing motion data be reconstructed, but unknown view angles for 2D test data can also be retrieved.

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17

Su, Hai Long, and Da Wei Zhang. "Study on Error Compensation of Human Motion Analysis System." Applied Mechanics and Materials 48-49 (February 2011): 1149–53. http://dx.doi.org/10.4028/www.scientific.net/amm.48-49.1149.

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Human motion capture system based on a new kind of error compensation technology was developed and used this to assess human motion. On the basis of three-dimensional reconstruction and some essential factors influencing on the measurement accuracy, the measurement error theory and the modified and improved human motion analysis system was established. The experimental data indicate that the measurement precision of the modified and improved system is more precise than the original system on human motion measurement and capturing.

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Li, Jian Wei, Xiao Wen Li, and Hua Lei Wu. "Analysis on Motion Trauma for Human’s Running by Motion Capture." Applied Mechanics and Materials 311 (February 2013): 232–37. http://dx.doi.org/10.4028/www.scientific.net/amm.311.232.

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Running is a kind of high-repetitive limb movements, which can possibly suffer knee-joint and ankle joint badly. In this paper, the author uses advanced instrument of motion capture to gain the gait data of human’s running motion，then create the curve of motion data tracing the knee-joint and ankle joint guided by the theory of biomechanics and kinesiology. Last we get the cause about the suffering of meniscus and ligament of knee-joint and ankle joint during the process of human’s running motion. The result of research can apply to biomechanics of human and the design of exerciser.

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19

HWANG, BON-WOO, SUNGMIN KIM, and SEONG-WHANe LEE. "A FULL-BODY GESTURE DATABASE FOR HUMAN GESTURE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 06 (September 2007): 1069–84. http://dx.doi.org/10.1142/s0218001407005806.

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This paper presents a full-body gesture database which contains 2D video data and 3D motion data of 14 normal gestures, 10 abnormal gestures and 30 command gestures for 20 subjects. We call this database the Korea University Gesture (KUG) database. Using 3D motion cameras and 3 sets of stereo cameras, we captured 3D motion data and 3 pairs of stereo-video data in 3 different directions for normal and abnormal gestures. In case of command gestures, 2 pairs of stereo-video data were obtained by 2 sets of stereo cameras with different focal lengths in order to capture views of whole body and upper body, simultaneously. The 2D silhouette data was synthesized by separating a subject and background in 2D stereo-video data. In this paper, we describe the gesture capture system, the organization of database, the potential usages of the database and the contact point for the KUG database. We expect that this database would be very useful for the study of 2D/3D human gesture and its application.

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20

Yunardi, Riky Tri, Aji Akbar Firdaus, and Eva Inaiyah Agustin. "Robotic Leg Design to Analysis the Human Leg Swing from Motion Capture." Bulletin of Electrical Engineering and Informatics 6, no. 3 (September 1, 2017): 256–64. http://dx.doi.org/10.11591/eei.v6i3.645.

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In this paper presented the prototype of robotic leg has been designed, constructed and controlled. These prototype are designed from a geometric of human leg model with three joints moving in 2D plane. Robot has three degree of freedom using DC servo motor as a joint actuators: hip, knee and ankle. The mechanical leg constructed using aluminum alloy and acrylic material. The control movement of this system is based on motion capture data stored on a personal computer. The motions are recorded with a camera by use of a marker-based to track movement of human leg. Propose of this paper is design of robotic leg to present the analysis of motion of the human leg swing and to testing the system ability to create the movement from motion capture. The results of this study show that the design of robotic leg was capable for practical use of the human leg motion analysis. The accuracy of orientation angles of joints shows the average error on hip is 1.46º, knee is 1.66º, and ankle is 0.46º. In this research suggesting that the construction of mechanic is an important role in the stabilization of the movement sequence.

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21

Senda, Sadahiro, and Katsuyoshi Tsujita. "2A2-C02 Dynamics simulation analysis of human locomotion on motion capture data(Sense, Motion and Measurement(2))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2012 (2012): _2A2—C02_1—_2A2—C02_2. http://dx.doi.org/10.1299/jsmermd.2012._2a2-c02_1.

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22

Ma, Christina Zong-Hao, Yan To Ling, Queenie Tsung Kwan Shea, Li-Ke Wang, Xiao-Yun Wang, and Yong-Ping Zheng. "Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion." Sensors 19, no. 1 (January 7, 2019): 195. http://dx.doi.org/10.3390/s19010195.

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Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments.

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23

Xu, Cheng, Jie He, Xiaotong Zhang, Haipiao Cai, Shihong Duan, Po-Hsuan Tseng, and Chong Li. "Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition." Computational Intelligence and Neuroscience 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/4160652.

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Motion related human activity recognition using wearable sensors can potentially enable various useful daily applications. So far, most studies view it as a stand-alone mathematical classification problem without considering the physical nature and temporal information of human motions. Consequently, they suffer from data dependencies and encounter the curse of dimension and the overfitting issue. Their models are hard to be intuitively understood. Given a specific motion set, if structured domain knowledge could be manually obtained, it could be used for better recognizing certain motions. In this study, we start from a deep analysis on natural physical properties and temporal recurrent transformation possibilities of human motions and then propose a useful Recurrent Transformation Prior Knowledge-based Decision Tree (RT-PKDT) model for recognition of specific human motions. RT-PKDT utilizes temporal information and hierarchical classification method, making the most of sensor streaming data and human knowledge to compensate the possible data inadequacy. The experiment results indicate that the proposed method performs superior to those adopted in related works, such as SVM, BP neural networks, and Bayesian Network, obtaining an accuracy of 96.68%.

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Bao, Hongshu, and Xiang Yao. "Human Motion Data Retrieval Based on Staged Dynamic Time Deformation Optimization Algorithm." Complexity 2020 (December 14, 2020): 1–11. http://dx.doi.org/10.1155/2020/6650924.

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In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases.

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25

Alnujaim, Ibrahim, and Youngwook Kim. "Augmentation of Doppler Radar Data Using Generative Adversarial Network for Human Motion Analysis." Healthcare Informatics Research 25, no. 4 (2019): 344. http://dx.doi.org/10.4258/hir.2019.25.4.344.

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26

Li, Ying. "Analysis and Synthesis of Human Motion Function Data Based on Decision Tree Classification." Journal of Physics: Conference Series 1982, no. 1 (July 1, 2021): 012117. http://dx.doi.org/10.1088/1742-6596/1982/1/012117.

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27

Zhigailov, Sergei, Artem Kuznetcov, Victor Musalimov, and Gennady Aryassov. "Measurement and Analysis of Human Lower Limbs Movement Parameters during Walking." Solid State Phenomena 220-221 (January 2015): 538–43. http://dx.doi.org/10.4028/www.scientific.net/ssp.220-221.538.

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It is necessary to analyze human gait for treatment and rehabilitation of human with musculoskeletal disorders of the locomotion apparatus (LA). The main goal of this work is evaluation of locomotion apparatus motion parameters captured by inertial measurement units (IMU) during walking. Motion Capture technology is process of getting practical results and data from IMU installed in different parts of human lower limbs. Synchronously, IMU send information about human movements to PC at the same moment of time. Such method gives an opportunity to follow parameters in some points of human leg in real time. The way of devices mounting and instruction for human under monitoring are based on related medical projects. Walking is selected for estimation of the musculoskeletal system as typical action. Experiment results got from several experiments were considered and analyzed.Basically, walking is described as a set of the system “human” discrete states. In the same time, the IMU sensors transmit motion parameters data continuously. It is proposed to present the man as a system with a control signal in the form of the double support period. The length will be measured using data from IMU. Double support period is chosen because its presence distinguishes walking from running.The most attention is given to getting the same practical results and data that can be obtained by placing the devices in different parts of the body. Moreover, a technique of using inertial measurement devices for measuring human motion to get some numerical results is shown. The use of this technique in practice demonstrated that it can be used to obtain an objective parameter describing the motion of the person. Continuation of this work is directed to create a complete model of the lower limbs motion for usage in practice [1].

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28

Alvarez, Juan, Diego Álvarez, and Antonio López. "Accelerometry-Based Distance Estimation for Ambulatory Human Motion Analysis." Sensors 18, no. 12 (December 15, 2018): 4441. http://dx.doi.org/10.3390/s18124441.

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In human motion science, accelerometers are used as linear distance sensors by attaching them to moving body parts, with their measurement axes its measurement axis aligned in the direction of motion. When double integrating the raw sensor data, multiple error sources are also integrated integrated as well, producing inaccuracies in the final position estimation which increases fast with the integration time. In this paper, we make a systematic and experimental comparison of different methods for position estimation, with different sensors and in different motion conditions. The objective is to correlate practical factors that appear in real applications, such as motion mean velocity, path length, calibration method, or accelerometer noise level, with the quality of the estimation. The results confirm that it is possible to use accelerometers to estimate short linear displacements of the body with a typical error of around 4.5% in the general conditions tested in this study. However, they also show that the motion kinematic conditions can be a key factor in the performance of this estimation, as the dynamic response of the accelerometer can affect the final results. The study lays out the basis for a better design of distance estimations, which are useful in a wide range of ambulatory human motion monitoring applications.

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Kloiber, Simon, Volker Settgast, Christoph Schinko, Martin Weinzerl, Johannes Fritz, Tobias Schreck, and Reinhold Preiner. "Immersive analysis of user motion in VR applications." Visual Computer 36, no. 10-12 (August 12, 2020): 1937–49. http://dx.doi.org/10.1007/s00371-020-01942-1.

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Abstract With the rise of virtual reality experiences for applications in entertainment, industry, science and medicine, the evaluation of human motion in immersive environments is becoming more important. By analysing the motion of virtual reality users, design choices and training progress in the virtual environment can be understood and improved. Since the motion is captured in a virtual environment, performing the analysis in the same environment provides a valuable context and guidance for the analysis. We have created a visual analysis system that is designed for immersive visualisation and exploration of human motion data. By combining suitable data mining algorithms with immersive visualisation techniques, we facilitate the reasoning and understanding of the underlying motion. We apply and evaluate this novel approach on a relevant VR application domain to identify and interpret motion patterns in a meaningful way.

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Li, Can. "Analysis of Aerobics Auxiliary Training Based on Deep Learning." Scientific Programming 2022 (March 22, 2022): 1–7. http://dx.doi.org/10.1155/2022/9269988.

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With the in-depth integration of information technology and subject teaching, it is also an inevitable trend to apply modern information technology to aerobics teaching. In this paper, the N-best algorithm is used in the video and real-time camera in aerobics, so that the human posture parameters in a single-frame image can be estimated. By using the relative position and motion direction of each part of the human body to describe the characteristics of aerobics, the Laplace scoring method is used to reduce the dimension of the data, and the discriminant human motion feature vector with a strong local topological structure is obtained. Finally, the iterative self-organizing data analysis technology (ISODATA algorithm) is used to dynamically determine the keyframe. In the aerobics video keyframe extraction experiments, the ST-FMP model improves the recognition accuracy of nondeterministic body parts of the flexible hybrid articulated human model (FMP) by about 15 percentage points and achieves 81% keyframe extraction accuracy, which is better than the keyframe algorithms of KFE and motion block. The proposed algorithm is sensitive to human motion features and human pose and is suitable for motion video annotation review.

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Mi, Jian, and Yasutake Takahashi. "Humanoid Robot Motion Modeling Based on Time-Series Data Using Kernel PCA and Gaussian Process Dynamical Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 6 (October 20, 2018): 965–77. http://dx.doi.org/10.20965/jaciii.2018.p0965.

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In this article, contrary to popular studies on human motion learning, we focus on addressing the problem of humanoid robot motions directly. Performances of different kernel functions with principal components analysis (PCA) in Gaussian process dynamical models (GPDM) are investigated to build efficient humanoid robot motion models. A novel kernel-PCA-GPDM method is proposed for building different types of humanoid robot motion models. Compared with the standard-PCA-GPDM and auto-encoder-GPDM methods, our proposed method is more efficient in humanoid robot motion modeling. In this work, three types of NAO robot motion models are studied: walk-model, lateral-walk model, and wave-hand model, where motion data are collected from an Aldebaran NAO robot using magnetic rotary encoder sensors. Using kernel-PCA-GPDM method, the motion data are first projected from the high 23-dimension observation space to a 3-dimension low latent space. Then, three types of humanoid robot motion models are learned in the 3D latent space. Compared with other kernel-PCA-GPDM or auto-encoder-GPDM methods, our proposed novel kernel-PCA-GPDM method performs efficiently in motion learning. Finally, we realize humanoid robot motion representation to verify the motion models that we build. The experimental results show that our proposed kernel-PCA-GPDM method builds efficient and smooth motion models.

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Xi, Chaojie. "The Construction of Adaptive Learning for Sports Based on Aerobics Trajectory Recognition Model." Journal of Function Spaces 2022 (August 9, 2022): 1–8. http://dx.doi.org/10.1155/2022/8339745.

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Perceiving the movement track of aerobics is a key element of learning aerobics, but the current aerobics movement is not very professional, the ability to identify the movement track is weak, and improper movement in the movement process is easy to cause physical injury. In order to improve the safety of athletes in bodybuilding training, this paper uses Kinect to hold the coach’s body contour, determine the standard level of coaches’ sports, and combine the characteristics for aerobics training, so as to improve the sports level of coaches, through data acquisition, data processing, and feature extraction to assist sports learning, as well as human posture recognition. The calculation and recognition of human skeleton joints are completed by two algorithms, which improve the human motion recognition algorithm. The aerobics data collected by Kinect device is specified and digitized, which enhances the robustness of the system and improves the performance of the algorithm and the accuracy of the motion data.

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Yong, Ching Yee, Rubita Sudirman, Nasrul Humaimi Mahmood, and Kim Mey Chew. "Human Body and Body Part Movement Analysis Using Gyroscope, Accelerometer and Compass." Applied Mechanics and Materials 284-287 (January 2013): 3120–25. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3120.

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This study investigates and acts as a trial clinical outcome for human motion and behaviour analysis in consensus of health related quality of life in Malaysia. It was developed to analyse and access the quality of human limbs motion that can be used in hospitals, clinics and human motion researches. An experiment was set up in a laboratory environment with conjunction of analysing human motion and its behaviour. The instruments demonstrate adequate internal consistency of results as below: 1. Compass sensor gives a better result with less standard deviation values especially in x-axis according descriptive statistical data. 2. Compass sensor gives a clearer scatter plot for better classification. 3. R2 (amount of variation explained) for sensor attached on arm is lower than hip and that means data collected from this site have a consistent trend. A

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Li, Shun, Liqing Cui, Changye Zhu, Baobin Li, Nan Zhao, and Tingshao Zhu. "Emotion recognition using Kinect motion capture data of human gaits." PeerJ 4 (September 15, 2016): e2364. http://dx.doi.org/10.7717/peerj.2364.

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Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.

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35

Dariush, Behzad, Hooshang Hemami, and Mohamad Parnianpour. "Multi-Modal Analysis of Human Motion From External Measurements." Journal of Dynamic Systems, Measurement, and Control 123, no. 2 (February 1, 2001): 272–78. http://dx.doi.org/10.1115/1.1370375.

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The “analysis” or “inverse dynamics” problem in human motion studies assumes knowledge of the motion of the dynamical system in various forms and/or measurements of ground reaction forces to determine the applied forces and moments at the joints. Conceptually, methods of attacking such problems are well developed and satisfactory solutions have been obtained if the input signals are noise free and the dynamic model is perfect. In this ideal case, an inverse solution exists, is unique, and depends continuously on the initial data. However, the inverse solution may require the calculation of higher order derivatives of experimental observations contaminated by noise—a notoriously difficult problem. The byproduct of errors due to numerical differentiation is grossly erroneous joint force and moment calculations. This paper provides a framework for analyzing human motion for different sensing conditions in a manner that avoids or minimizes the number of derivative computations. In particular, two sensing modalities are considered: 1) image based and 2) multi-modal sensing: combining imaging, force plate, and accelerometery.

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36

Warren, William H. "Collective Motion in Human Crowds." Current Directions in Psychological Science 27, no. 4 (July 11, 2018): 232–40. http://dx.doi.org/10.1177/0963721417746743.

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The balletic motion of bird flocks, fish schools, and human crowds is believed to emerge from local interactions between individuals in a process of self-organization. The key to explaining such collective behavior thus lies in understanding these local interactions. After decades of theoretical modeling, experiments using virtual crowds and analysis of real crowd data are enabling us to decipher the “rules of engagement” governing these interactions. On the basis of such results, my students and I built a dynamical model of how a pedestrian aligns his or her motion with that of a neighbor and how these binary interactions are combined within a neighborhood of interaction. Computer simulations of the model generate coherent motion at the global level and reproduce individual trajectories at the local level. This approach has yielded the first experiment-driven, bottom-up model of collective motion, providing a basis for understanding more complex patterns of crowd behavior in both everyday and emergency situations.

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TARLOCHAN, R., S. RAMESH, and B. M. HILLBERRY. "DYNAMIC ANALYSIS OF THE HUMAN KNEE." Biomedical Engineering: Applications, Basis and Communications 14, no. 03 (June 25, 2002): 122–26. http://dx.doi.org/10.4015/s1016237202000188.

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The objective of this paper is to present a dynamic analysis of the human knee for various activities such as walking and stair ascending/descending. The results of this work is significant as it can used as inputs to joint simulators for artificial joint study, for rehabilitation purposes and to some extend applied to sports biomechanics. In the present work an experimental study consisting of a motion sensor and a force plate was used to capture the motion of subjects and to provide ground force reactions data that will serve as inputs to the model. Subjects in the 50-65 years old bracket performed activities such as walking, stairs ascending and descending. This age bracket was chosen because most of patients with knee-related problems are found in this age group. Some of the results from the present work correlated with that of published literature. However, the present analysis was based on a more comprehensive model of the knee, which makes the results more realistic to the actual mechanical behavior of the lower limb.

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Szczęsna, Agnieszka. "Quaternion Entropy for Analysis of Gait Data." Entropy 21, no. 1 (January 17, 2019): 79. http://dx.doi.org/10.3390/e21010079.

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Nonlinear dynamical analysis is a powerful approach to understanding biological systems. One of the most used metrics of system complexities is the Kolmogorov entropy. Long input signals without noise are required for the calculation, which are very hard to obtain in real situations. Techniques allowing the estimation of entropy directly from time signals are statistics like approximate and sample entropy. Based on that, the new measurement for quaternion signal is introduced. This work presents an example of application of a nonlinear time series analysis by using the new quaternion, approximate entropy to analyse human gait kinematic data. The quaternion entropy was applied to analyse the quaternion signal which represents the segments orientations in time during the human gait. The research was aimed at the assessment of the influence of both walking speed and ground slope on the gait control during treadmill walking. Gait data was obtained by the optical motion capture system.

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39

Ko, Kyeong-Ri, and Sung Bum Pan. "Feature Extraction and Classification of Posture for Four-Joint based Human Motion Data Analysis." Journal of the Institute of Electronics and Information Engineers 52, no. 6 (June 25, 2015): 117–25. http://dx.doi.org/10.5573/ieie.2015.52.6.117.

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Papić, Vladan, Vlasta Zanchi, and Mojmil Cecić. "Motion analysis system for identification of 3D human locomotion kinematics data and accuracy testing." Simulation Modelling Practice and Theory 12, no. 2 (May 2004): 159–70. http://dx.doi.org/10.1016/j.simpat.2003.08.008.

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Miura, Takeshi, Takaaki Kaiga, Takeshi Shibata, Katsubumi Tajima, and Hideo Tamamoto. "Physical constitution adjustment for a human body model used in motion capture data analysis." IEEJ Transactions on Electrical and Electronic Engineering 11 (December 2016): S140—S141. http://dx.doi.org/10.1002/tee.22357.

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Lambert-Shirzad, Navid, and H. F. Machiel Van der Loos. "On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population." Journal of Neurophysiology 117, no. 1 (January 1, 2017): 290–302. http://dx.doi.org/10.1152/jn.00435.2016.

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Human motor behavior is highly goal directed, requiring the central nervous system to coordinate different aspects of motion generation to achieve the motion goals. The concept of motor synergies provides an approach to quantify the covariation of joint motions and of muscle activations, i.e., elemental variables, during a task. To analyze goal-directed movements, factorization methods can be used to reduce the high dimensionality of these variables while accounting for much of the variance in large data sets. Three factorization methods considered in this paper are principal component analysis (PCA), nonnegative matrix factorization (NNMF), and independent component analysis (ICA). Bilateral human reaching data sets are used to compare the methods, and advantages of each are presented and discussed. PCA and NNMF had a comparable performance on both EMG and joint motion data and both outperformed ICA. However, NNMF's nonnegativity condition for activation of basis vectors is a useful attribute in identifying physiologically meaningful synergies, making it a more appealing method for future studies. A simulated data set is introduced to clarify the approaches and interpretation of the synergy structures returned by the three factorization methods.NEW & NOTEWORTHY Literature on comparing factorization methods in identifying motor synergies using numerically generated, simulation, and muscle activation data from animal studies already exists. We present an empirical evaluation of the performance of three of these methods on muscle activation and joint angles data from human reaching motion: principal component analysis, nonnegative matrix factorization, and independent component analysis. Using numerical simulation, we also studied the meaning and differences in the synergy structures returned by each method. The results can be used to unify approaches in identifying and interpreting motor synergies.

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43

de Lange, A., R. Huiskes, and J. M. G. Kauer. "Effects of Data Smoothing on the Reconstruction of Helical Axis Parameters in Human Joint Kinematics." Journal of Biomechanical Engineering 112, no. 2 (May 1, 1990): 107–13. http://dx.doi.org/10.1115/1.2891160.

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In biomechanical joint-motion analyses, the continuous motion to be studied is often approximated by a sequence of finite displacements, and the Finite Helical Axis (FHA) or “screw axis” for each displacement is estimated from position measurements on a number of anatomical or artificial landmarks. When FHA parameters are directly determined from raw (noisy) displacement data, both the position and the direction of the FHA are ill-determined, in particular when the sequential displacement steps are small. This implies, that under certain conditions, the continuous pathways of joint motions cannot be adequately described. The purpose of the present experimental study is to investigate the applicability of smoothing (or filtering) techniques, in those cases where FHA parameters are ill-determined. Two different quintic-spline smoothing methods were used to analyze the motion data obtained with Roentgenstereophotogrammetry in two experiments. One concerning carpal motions in a wrist-joint specimen, and one relative to a kinematic laboratory model, in which the axis positions are a priori known. The smoothed and nonsmoothed FHA parameter errors were compared. The influences of the number of samples and the size of the sampling interval (displacement step) were investigated, as were the effects of equidistant and nonequidistant sampling conditions and noise invariance.

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44

Choi, Hyeon Ki, and Si Yeol Kim. "Computer-Graphics Based Analysis of Human Foot Kinematics during the Gait." Key Engineering Materials 321-323 (October 2006): 1115–18. http://dx.doi.org/10.4028/www.scientific.net/kem.321-323.1115.

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A computer-graphics based biomechanical model was constructed to investigate the kinematics of foot joints during the stance-phase of walking. In the model, all joints were assumed to act as monocentric, single degree of freedom hinge joints. To obtain the inputs to the model, the motion of foot segments was captured during the gait by a four-camera video system. The model fitted in an individual subject was simulated with these motion data. The ranges of motion of the first tarsometatarsal joint and the first metatarsophanlangeal joint were 8 ∼13 and -13 ∼ 48 respectively. The kinematic data of joints were similar to those of the previous studies. Our method based on the graphical computer model is considered useful for kinematic analysis of small joints including foot joints. Also, the results of this study will provide important information to the biomechanical studies which deal with human gait.

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45

Zhou, Dong, Zhi Qi Guo, Mei Hui Wang, and Chuan Lv. "Human Factors Assessment Based on the Technology of Human Motion Capturing." Applied Mechanics and Materials 44-47 (December 2010): 532–36. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.532.

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We aim to combine the technology of capturing human motion and the technology of virtual reality to carry on assessment of human factors. The unique point in this method is that not only the reliable data of the maintenance worker can be gained, but also the quantitative analytic result based on the virtual environment can be obtained. In the paper, human motion capture technology, ergonomics evaluation and the interface technology have been considered comprehensively. overall technical program of human factors evaluation, which is based on human motion capturing technology, have been carried on; the technology, which include the captured data of human motion translating into the virtual environment, building the virtual human model and virtual human simulation, both based on captured data in the working site, are taken as innovations; replicable technology of the captured data in the virtual environment have been broken through. Carrying on the quantitative analysis of worker working postures, fatigue and human force and torque in the maintenance process, which is based on the technology of human factors evaluation by using the captured data in the working site, is researched. We have verified the feasibility of this technology through an example. The method provides a new way and operational technology for human factors assessment in maintenance process of aviation equipment.

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Purkayastha, Sagar N., Michael D. Byrne, and Marcia K. O’Malley. "Human-Scale Motion Capture with an Accelerometer-Based Gaming Controller." Journal of Robotics and Mechatronics 25, no. 3 (June 20, 2013): 458–65. http://dx.doi.org/10.20965/jrm.2013.p0458.

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Gaming controllers are attractive devices for research due to their onboard sensing capabilities and low cost. However, a proper quantitative analysis regarding their suitability for motion capture has yet to be fully reported. In this paper, a detailed analysis of the accelerometers of the Nintendo Wiimote is presented. The gravity-compensated acceleration data from the accelerometers of theWiimote were plotted, compared and correlated with computed acceleration data derived from a six-camera motion capture system. The results show high correlation and low mean absolute error between the gravity-compensated data from the accelerometers of the controllers and computed acceleration from position data of the motion capture system. From the results obtained, it can be inferred that the Wiimote is well suited for motion capture applications where post-processing of data is practical.

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47

Xu, Yuanhong. "Characteristic Analysis Technology of Moving People Based on Image Recognition." Mobile Information Systems 2022 (July 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/1604993.

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In recent years, the visual analysis of human motion has become a frontier direction in the field of computer vision. It recognizes, identifies, and tracks people in image sequences, as well as understands and describes their actions. Using computer vision technology to study the field of image processing and pattern recognition, as well as extracting and effectively identifying human motion features from video images, has become a hot topic of concern with the rapid development and popularization of information technology. This study presents a method for analyzing the characteristics of moving human bodies based on image recognition, introduces the extraction function method, and analyzes the characteristics of the extraction function to improve the ability to analyze the characteristics of moving human bodies. To achieve fine segmentation, the hierarchical clustering algorithm is used to segment the periodic motion in each motion. Different benchmark databases and self-built data were used in the experiments. Experiments show that the algorithm can achieve good classification and recognition results while maintaining low computational complexity and extracting less feature data. It can also organically integrate the static and dynamic features of human walking.

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Zeng, Ming, Chang Wei Chen, Qing Hao Meng, Hong Lin Ren, and Shu Gen Ma. "Biomechanical Analysis of Typical Upper Limb Movements Based on Kinect-LifeMOD." Applied Mechanics and Materials 599-601 (August 2014): 534–38. http://dx.doi.org/10.4028/www.scientific.net/amm.599-601.534.

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In traditional biomechanical analysis of upper limb, the high-precision motion data and lifelike human models are needed. It is obvious that those processes are costly and time-consuming. In this paper, a novel and simple combination method based on Kinect-LifeMOD is proposed. Firstly, the Microsoft Kinect (a latest depth sensor) is used to build a cheap and precise motion capture platform. Real-time and reliable key-node rotation data of human skeletons can be acquired by this motion capture system. Next, rotation data is converted into position data as the input of the LifeMOD software which can establish mathematical model of upper limb and execute biomechanical analysis automatically. The experimental results show that the proposed method could achieve the satisfactory performance.

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Liu, Hairen, and Wei Zhang. "Data Analysis of Athletes’ Physiological Indexes in Training and Competition Based on Wireless Sensor Network." Journal of Sensors 2021 (September 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/5923893.

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The application of physiological and biochemical indicators in athlete training and competition has become a hot research topic in related fields at home and abroad. Both coaches and scientific researchers hope to use quantitative physiological and biochemical indicators to study the load, fatigue, and recovery of athletes in training competitions and use them to scientifically guide athletes in training competitions, improve sports performance, and reduce injuries. This article introduces in detail the development status of wireless sensor network technology, energy consumption detection system, and ZigBee technology. On this basis, the focus is on the design of the detection terminal (coordinator and router node), the routing protocol of the ZigBee network, and the algorithm for the detection of human energy consumption. This subject proposes a design plan for the human exercise energy consumption detection system and researches and designs the wireless sensor network coordinator, router node, and host computer monitoring system. The microprocessors of the two types of network nodes use the single-chip microcomputer. Among them, the router node is composed of sensor modules, data transmission modules, and power modules; the software part is transplanted to ZigBee protocol Z-Stack, combined with the routing algorithm, and we add the corresponding node function code to achieve them. Based on the introduction of the development status and development points of the single-chip-based motion wireless sensor, this article focuses on the analysis of the single-chip-based motion wireless sensor network products. The common features of the single-chip microcomputer are wireless, huge low power consumption, and simple development. Engineering practice shows that the designed system is relatively good in terms of reliability and stability of data transmission; even in the case of severe noise interference and electromagnetic interference, the probability of network nodes malfunctioning is still very small. The router node processes and analyzes the collected motion data, calculates the energy consumption and motion state of human motion based on the acceleration value of each axis and extracts data characteristics, and transmits the obtained results to the coordinator for real-time display.

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Kamali, Kaveh, Ali Akbar Akbari, Christian Desrosiers, Alireza Akbarzadeh, Martin J. D. Otis, and Johannes C. Ayena. "Low-Rank and Sparse Recovery of Human Gait Data." Sensors 20, no. 16 (August 13, 2020): 4525. http://dx.doi.org/10.3390/s20164525.

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Due to occlusion or detached markers, information can often be lost while capturing human motion with optical tracking systems. Based on three natural properties of human gait movement, this study presents two different approaches to recover corrupted motion data. These properties are used to define a reconstruction model combining low-rank matrix completion of the measured data with a group-sparsity prior on the marker trajectories mapped in the frequency domain. Unlike most existing approaches, the proposed methodology is fully unsupervised and does not need training data or kinematic information of the user. We evaluated our methods on four different gait datasets with various gap lengths and compared their performance with a state-of-the-art approach using principal component analysis (PCA). Our results showed recovering missing data more precisely, with a reduction of at least 2 mm in mean reconstruction error compared to the literature method. When a small number of marker trajectories is available, our findings showed a reduction of more than 14 mm for the mean reconstruction error compared to the literature approach.

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