Motion Symmetry Evaluation Using Accelerometers and Energy Distribution

Symmetry

Aleš Procházka¹  Oldřich Vyšata¹  Martin Vališ²  Hana Charvátová³ 

[1] Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic;Department of Neurology, University Hospital Hradec Králové, Faculty of Medicine in Hradec Králové, Charles University in Prague, 500 05 Hradec Králové, Czech Republic;Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 01 Zlín, Czech Republic;

关键词: microelectromechanical sensors;    motion analysis;    symmetry;    digital signal processing;    wavelet transform;    feature extraction;    classification;    augmented reality;    neurology;   

DOI  :  10.3390/sym11070871

来源: DOAJ

【 摘 要 】

Analysis of motion symmetry constitutes an important area with many applications in engineering, robotics, neurology and biomedicine. This paper presents the use of microelectromechanical sensors (MEMS), including accelerometers and gyrometers, to acquire data via mobile devices so as to monitor physical activities and their irregularities. Special attention is devoted to the analysis of the symmetry of the motion of the body when the same exercises are performed by the right and the left limb. The analyzed data include the motion of the legs on a home exercise bike under different levels of load. The method is based on signal analysis using the discrete wavelet transform and the evaluation of signal segment features such as the relative energy at selected decomposition levels. The subsequent classification of the evaluated features is performed by k-nearest neighbours, a Bayesian approach, a support vector machine, and neural networks. The highest average classification accuracy attained is 91.0% and the lowest mean cross-validation error is 0.091, resulting from the use of a neural network. This paper presents the advantages of the use of simple sensors, their combination and intelligent data processing for the numerical evaluation of motion features in the rehabilitation and monitoring of physical activities.

【 授权许可】

Unknown