A motion capture system for sport training and rehabilitation

Mirabella, O.; Raucea, A.; Fisichella, Fausto; Gentile, Luigi

doi:10.1109/hsi.2011.5937342

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…Mirabella et al, [18] provided a motion capture method that offers both precise measurements of the human arm's motion parameters and a virtual model for the graphical reconstruction of the motion. The hardware uses several MEMS inertial sensors whose data is processed using quaternion based mathematical methods.…”

Section: Related Workmentioning

confidence: 99%

“…The relationship assumed that through directional connections between the adjacent input frames, the entire motion series shared the same spatial transmission properties [13]. The scope for reuse of existing motion sequences [16] is substantially restricted by a large number of unlabeled or minimum design data [17,18]. A successful way to reuse data has become an efficient way of classifying and fusing fragments.…”

Section: Introduction About Sports Rehabilitationmentioning

confidence: 99%

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Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Cui

Chang²

2020

IEEE Access

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Presently, A wide range of unlabeled and minimal style data significantly decreases the current motion sequence's reuse ability. An important method of data reuse has a successful classification and fragment separation, which has been discussed in this research. This paper focuses on these particular problems and the tremendous progress of deep learning in design and symbolic fields. A Limited Boltzmann Model (LBM) theory is based on the Advanced Spatio-Temporal Extraction Model (ASTEM), which has been used for analyzing the physiological motion of human skeletons. There are primarily three aspects to the results of the study. (1) For constructing a semi-combination model, the stack factor decomposition is used as a spatiotemporal model function and LBM discrimination. (2), Optimized algorithm used to create the three-channel generative LBM model using the weight decomposition idea and then extract the time and space-based abstract properties of the original motion series. (3) The unsupervised related model of frame detection is built using the perception of human interaction through 3D convolution LBM. A significant research direction of the medical analysis and extraction of sports data is used appropriately to interpret and gain valuable information and knowledge from motion analyses. Experimental outcomes show that this technique offers technical assistance and guidance for implementing a real cloud-based fusion system.

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Section: Related Workmentioning

confidence: 99%

Section: Introduction About Sports Rehabilitationmentioning

confidence: 99%

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Cui

Chang²

2020

IEEE Access

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“…In order to strengthen the authenticity of the PE virtual reality system, the human body mathematical model and the three-dimensional geometry are combined to create a human kinematic behaviour model with physical properties, which is integrated into the PE virtual reality system through the VC++, ODE and APIs of Vega Prime. The physics engine open source code in conjunction with VC++ is used for secondary development [20].…”

Section: Algorithm and Implementationmentioning

confidence: 99%

Physical Education Motion Correction System Based on Virtual Reality Technology

Yang

Meng²

2019

Int. J. Emerg. Technol. Learn.

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Compared with traditional physical education (PE), the computer virtual digital-based education system is convenient and efficient, which can effectively solve the shortcomings of traditional teaching mode, and correct the PE motions in real time. Thus, the athletes can master the motion technical skills as soon as possible and the training effect can be improved. For this, based on computer virtual reality technology, this paper constructs a PE motion correction system. Then, through the analysis for real-time motion data and human movement posture, it achieves the training objective of real-time PE teaching motion correction under the three-dimensional visualization. This shall provide a scientific basis for the evaluation of PE teaching and training methods.

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“…IMUs have also been integrated in consolidated tools accompanying rehabilitation treatment [ 70 ]. An example of a motion capture device based on MEMS inertial sensors, able to provide both an accurate measurement of some motion parameters of a human arm and a graphical reconstruction of the movement on a synthetic model, has been developed by Mirabella et al [ 71 ]. The developed caption system can be exploited not only in rehabilitation procedures, but also for sport training.…”

Section: Mems-based Sensor Technologies For Human Centred Applicatmentioning

confidence: 99%

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

Ciuti

Ricotti

Menciassi

et al. 2015

Sensors

130

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Over the past few decades the increased level of public awareness concerning healthcare, physical activities, safety and environmental sensing has created an emerging need for smart sensor technologies and monitoring devices able to sense, classify, and provide feedbacks to users’ health status and physical activities, as well as to evaluate environmental and safety conditions in a pervasive, accurate and reliable fashion. Monitoring and precisely quantifying users’ physical activity with inertial measurement unit-based devices, for instance, has also proven to be important in health management of patients affected by chronic diseases, e.g., Parkinson’s disease, many of which are becoming highly prevalent in Italy and in the Western world. This review paper will focus on MEMS sensor technologies developed in Italy in the last three years describing research achievements for healthcare and physical activity, safety and environmental sensing, in addition to smart systems integration. Innovative and smart integrated solutions for sensing devices, pursued and implemented in Italian research centres, will be highlighted, together with specific applications of such technologies. Finally, the paper will depict the future perspective of sensor technologies and corresponding exploitation opportunities, again with a specific focus on Italy.

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A motion capture system for sport training and rehabilitation

Cited by 11 publications

References 8 publications

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Deep Learning Based Advanced Spatio-Temporal Extraction Model in Medical Sports Rehabilitation for Motion Analysis and Data Processing

Physical Education Motion Correction System Based on Virtual Reality Technology

MEMS Sensor Technologies for Human Centred Applications in Healthcare, Physical Activities, Safety and Environmental Sensing: A Review on Research Activities in Italy

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