Integrating the enriched feature with machine learning algorithms for human movement and fall detection

  • Authors:

  • Chenghua Li;Man Lin;Laurence T. Yang;Chen Ding

  • Affiliations:

  • CUIIUC, ChangZhou University, ChangZhou, P.R. China and Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Canada;Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Canada;Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Canada;Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, Canada

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2014

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Abstract

Movement detection is gaining more and more attention among various pattern recognition problems. Recognizing human movement activity types is extremely useful for fall detection for elderly people. Wireless sensor network technology enables human motion data from wearable wireless sensor devices be transmitted for remote processing. This paper studies methods to process the human motion data received from wearable wireless sensor devices for detecting different types of human movement activities such as sitting, standing, lying, fall, running, and walking. Machine learning methods K Nearest Neighbor algorithm (KNN) and the Back Propagation Neural Network (BPNN) algorithm are used to classify the activities from the data acquired from sensors based on sample data. As there are a large amount of real-time raw data received from sensors and there are noises associated with these data, feature construction and reduction are used to preprocess these raw sensor data obtained from accelerometers embedded in wireless sensing motes for learning and processing. The singular value decomposition (SVD) technique is used for constructing the enriched features. The enriched features are then integrated with machine learning algorithms for movement detection. The testing data are collected from five adults. Experimental results show that our methods can achieve promising performance on human movement recognition and fall detection.