NDLI: A BP Neural Network Based Method for Upper Limb Motion Strength Evaluation

Content Provider	IEEE Xplore Digital Library
Author	Yao Li Zhenbo Guo Kaixi Wang
Copyright Year	2013
Description	Author affiliation: Coll. of Inf. Eng., Qingdao Univ., Qingdao, China (Yao Li; Zhenbo Guo; Kaixi Wang)
Abstract	Motion intensity is a comprehensive property to reflect to the motion speed, motion frequency and motion explosive power. Motion intensity evaluation plays a very important role in both the stroke patients' rehabilitation program development and competitive athletes' daily training. The traditional motion intensity evaluation takes the heart rate or rate of perceived exertion as evaluation parameters, which can't determine the motion intensity of peoples because everyone has subtile differences in these aspects. This paper proposes a new upper limb motion intensity evaluation model based on BP neural network, whose inputs are the change rate of angle and motion amplitudes which are computed according to the measured values from the three-axis acceleration sensor, and whose output is the motion intensity grade. This new model is verified via the MATLAB neural network toolbox, and the simulation experiment shows that the model has higher efficiency in evaluating the upper motion intensity grade than the traditional method and the accuracy rate reaches 93.75%.
Sponsorship	IEEE Comput. Soc.
Starting Page	171
Ending Page	176
File Size	371387
Page Count	6
File Format	PDF
e-ISBN	9781479928309
DOI	10.1109/CLOUDCOM-ASIA.2013.12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Intensity evaluation Acceleration sensor BP neural network Upper limb motion Sensors Acceleration Mathematical model MATLAB Biological neural networks
Content Type	Text
Resource Type	Article

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