A Comparison of Inertial Motion Capture Systems: DorsaVi and Xsens

Drapeaux, Alisa; Carlson, Kevin

doi:10.7575/aiac.ijkss.v.8n.3p.24

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…Numerous research institutions and laboratories are studying how to capture human motion data, and analyze and restore it. ese sets of motion capture systems have begun to move from laboratory research to the consumer market, and are beginning to open up in conjunction with virtual reality technology [8]. e main motion capture technologies on the market today are optical image-based technologies based on image processing and light point positioning.…”

Section: Introductionmentioning

confidence: 99%

A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Han

2022

Security and Communication Networks

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Computer-aided sports systems are an important area of current research. To be specific, it can combine computer vision, computer graphics, and motion capture technologies with the characteristics of sports, and design and develop assisted systems according to user needs. As a result, it is of great practical importance and application to improve the entertainment and science of sports learning, to stimulate public participation in sports, and to learn sports skills. Table tennis has a very great popular base in China and has a wide range of audiences. However, for beginners or amateurs of table tennis, it is difficult to learn table tennis skills by relying on books, videos, etc., due to the lack of professional technical guidance, which makes it relatively difficult for them to continuously improve their skills. Sometimes they tend to form wrong technical habits, which are not easy to detect and correct in time, and can lead to sports injuries over time. Therefore, the lack of professional instruction has hindered the development of table tennis in public fitness to a certain extent. On the other hand, in a table tennis course, the learning of table tennis techniques is mostly dependent on the teacher’s explanation. Students are bored with the repetitive and boring teaching because they need to repeat the exercises several times. The formation of correct table tennis movements requires repeated instruction from the instructor to address problems with the student’s movements. However, due to limited teaching resources, it is difficult for teachers to accommodate different students during the teaching process. In addition, with a limited team of instructors, the level of proficiency varies, making it difficult for them to make an objective assessment. As a result, with the rapid development and application of modern science and technology in the field of sports, there is an urgent need to explore intelligent sports technology instructional support systems to improve the shortcomings of the traditional teaching model. Motion recognition technology, as an essential branch of multimodal interaction technology, has been developing rapidly in recent years. This research focus on designing and implementing a system for recognizing human motion based on skeletal point pose information by combining inertial sensors. This system can collect data from sensors located at the main skeletal points of the human body and transmits them to the host computer through multi-Bluetooth pairing transmission. After that, support vector machines are applied to classify human movements and to recognize general human movements. This system has significant advantages for human movement recognition and classification due to the distinctive technical characteristics of table tennis. The recognition and classification of both players in the video can play an important role in the technical analysis and tactical arrangement of the players. As a result, the table tennis motion correction system based on human motion feature recognition has crucial research significance and application value.

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Section: Introductionmentioning

confidence: 99%

A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Han

2022

Security and Communication Networks

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“…The motion capture system was used only when the data collection was carried out in a training centre [ 30 ]. Currently, it is known that wearable systems allow physiotherapists and other professionals working with movement to assess human movement in a more robust, rigorous, valid, and reliable manner in a real context [ 46 ]. The number of IMUs or their placement was not consistent between the studies; however, in the gait analysis, the right leg and upper/lower back were always analysed.…”

Section: Discussionmentioning

confidence: 99%

Methodological Considerations in the Kinematic and Kinetic Analysis of Human Movement among Healthy Adolescents: A Scoping Review of Nonlinear Measures in Data Processing

Silva

Ribeiro

Figueira

et al. 2022

Sensors

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Nonlinear measures have increasingly revealed the quality of human movement and its behaviour over time. Further analyses of human movement in real contexts are crucial for understanding its complex dynamics. The main objective was to identify and summarize the nonlinear measures used in data processing during out-of-laboratory assessments of human movement among healthy adolescents. Summarizing the methodological considerations was the secondary objective. The inclusion criteria were as follows: According to the Population, Concept, and Context (PCC) framework, healthy teenagers between 10 and 19 years old that reported kinetic and/or kinematic nonlinear data-processing measurements related to human movement in non-laboratory settings were included. PRISMA-ScR was used to conduct this review. PubMed, Science Direct, the Web of Science, and Google Scholar were searched. Studies published between the inception of the database and March 2022 were included. In total, 10 of the 2572 articles met the criteria. The nonlinear measures identified included entropy (n = 8), fractal analysis (n = 3), recurrence quantification (n = 2), and the Lyapunov exponent (n = 2). In addition to walking (n = 4) and swimming (n = 2), each of the remaining studies focused on different motor tasks. Entropy measures are preferred when studying the complexity of human movement, especially multiscale entropy, with authors also carefully combining different measures, namely entropy and fractal analysis.

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“…The system achieved errors in the range of 5.4–6.1% across three participants when compared to a force platform [ 125 ]. The dorsaVi technology produces comparable kinematic data to the Xsens Awinda system, indicating that the different mounting methods have little effect on the data collected [ 126 ].…”

Section: Commercialised Wearable Technologies For Sportsmentioning

confidence: 99%

E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities

Yang,

McErlain-Naylor,

Isaia

et al. 2024

Sensors

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E-textiles have emerged as a fast-growing area in wearable technology for sports and fitness due to the soft and comfortable nature of textile materials and the capability for smart functionality to be integrated into familiar sports clothing. This review paper presents the roles of wearable technologies in sport and fitness in monitoring movement and biosignals used to assess performance, reduce injury risk, and motivate training/exercise. The drivers of research in e-textiles are discussed after reviewing existing non-textile and textile-based commercial wearable products. Different sensing components/materials (e.g., inertial measurement units, electrodes for biosignals, piezoresistive sensors), manufacturing processes, and their applications in sports and fitness published in the literature were reviewed and discussed. Finally, the paper presents the current challenges of e-textiles to achieve practical applications at scale and future perspectives in e-textiles research and development.

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A Comparison of Inertial Motion Capture Systems: DorsaVi and Xsens

Cited by 10 publications

References 13 publications

A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

A Table Tennis Motion Correction System Based on Human Motion Feature Recognition

Methodological Considerations in the Kinematic and Kinetic Analysis of Human Movement among Healthy Adolescents: A Scoping Review of Nonlinear Measures in Data Processing

E-Textiles for Sports and Fitness Sensing: Current State, Challenges, and Future Opportunities

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