期刊
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE
卷 14, 期 5, 页码 1166-1172出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITB.2010.2051955
关键词
Accelerometer; artificial-neural nets (ANNs); autoregressive (AR) modeling; human-activity recognition
类别
资金
- Ministry of Knowledge Economy (MKE), Korea [NIPA-2010-(C1090-1021-0003)]
Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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