A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer

Authors:
Adil Mehmood Khan;Young-Koo Lee;Sungyoung Y. Lee;Tae-Seong Kim
Affiliations:
Department of Computer Engineering, Kyung Hee University, Yongin-si , Korea;Department of Computer Engineering, Kyung Hee University, Yongin-si , Korea;Department of Computer Engineering, Kyung Hee University, Yongin-si , Korea;Department of Biomedical Engineering, Kyung Hee University, Yongin-si, Korea
Venue:
IEEE Transactions on Information Technology in Biomedicine
Year:
2010

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Abstract

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.