LVQ Neural Network Applied for Upper Limb Motion Recognition for Home-based Stroke Rehabilitation
To improve the rehabilitation effectiveness and reduce the hospital costs, a new upper limb motion recognition model, through which hospital based clinicians can remotely supervise home based stroke rehabilitation, is proposed in this paper. Firstly, the real time limb motion data is collected using a 3-axis accelerometer sensor which is fixed on the upper limb of a patient. Secondly, the Wavelet Transform is employed to extract the approximation coefficients of different types of rehabilitation motions. Finally, a recognition model is established based on an LVQ neural network. 2 typical rehabilitation motions, Bobath handshaking and wrist turning, were chosen to test this proposed recognition system. The experiment results indicate that the recognition accurate rate can achieve as high as 100％. This pilot forms a foundation to further develop a home based remote training and assessment system for stroke rehabilitation.
Lei Yu Liquan Guo Xudong Gu Jianming Fu Qiang Fang
Suzhou Institute of Biomedical Engineering and Technology, CAS, China Rehabilitation Medical Center of The Second Hospital of Jiaxing, China
国际会议
2011 International Symposium on Bioelectronics and Bioinformatics(第二届国际生物医学电子学与生物信息学学术会议 ISBB 2011)
苏州
英文
151-154
2011-11-03（万方平台首次上网日期，不代表论文的发表时间）