A Pervasive Simplified Method for Human Movement Pattern Assessing

Human movement pattern can be a valuable information for rehabilitation therapy, sport medicine and elderly people monitoring, but acquisition of them through multi-cite accelerormeters would result in uncomfortable wearing and complex data processing. In this paper, method of using a single waist-fixed accelerometer to detect human movement pattern was investigated and evaluated. 10 subjects were asked to run or walk on a treadmill in a regular way. A 5th order Butterworth low pass filter with cutoff frequency 20Hz was designed to filter the acceleration data and denoise the sample. By collecting the velocity from treadmill as label data and the individual´s waist acceleration data, training data set was established. A Bayesian network classifier trained by EM learning algorithm was developed for human movement pattern assessing. Experiment showed that the method could predict the human walking and running state with a considerable accuracy more than 90%. Such accuracy could also be achieved even with a single superior-inferior acceleration feature. The classification of fast speed walking and normal speed one also achieved satisfying result. This indicated that in some application in which walking and running state were only needed to classify could employ the low power, low computational complexity uniaxial accelerometer as the human movement detector.