Gait Recognition and Walking Exercise Intensity Estimation System
Date Issued
2012
Date
2012
Author(s)
Hsiao, Bo-Tang
Abstract
Cardiovascular disease patients need suggestions from doctors for an acceptable level of exercise intensity and exercise prescription. And over-weight patients control their weight by self-management. Developing a monitoring system which can record personalized exercise intensity is necessary for above patients.
This thesis proposes a feasible system for personalized gait recognition and walking exercise intensity estimation. On analyzing gait recognition, this thesis uses α-β filters to obtain better athletic attitudes, and further uses Empirical Mode Decomposition (EMD) to filter noise of athletic attitude to acquire Fourier Transform energy spectrum. Thus, the Linear Discriminant Analysis (LDA) can apply to this energy spectrum for training and recognition. When the motion is recognized as walking, the walking exercise intensity is estimated. This thesis also discusses the correlation between inertia work and exercise intensity by using residual function of Empirical Mode Decomposition (EMD) and quadratic approximation to filter the baseline shift of acceleration sensor to reduce the serious integral effect. And further we are can derive better exercise intensity and instantaneous speed.
This thesis uses measured 10 subjects including 5 males and 5 females to recognize four types of gait from upstairs, downstairs, walking, and running. For gait recognition, 30% of collected raw data is used for training samples, and recognition rate of verifying the 70% data can reach more than 90%. After applying our method to ten different walking speeds from each subject, we found that step calculation shows 95% accuracy, and Y.Kurihara’s exercise intensity method can be enhanced, and the regression equation correlation can be increased from 0.55 to 0.81. These results prove our method can improve exercise intensity estimation. The proposed method has been implemented on smart phones and graphic user interface on personal computers to help understanding the change of athletic stance and to verify the proposed algorithm for further application on remote monitoring, cloud computing, and self-management.
Subjects
Exercise Intensity
Gait Recognition
Linear Discriminant Analysis(LDA)
Empirical Mode Decomposition(EMD)
SDGs
Type
thesis
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