A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.
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.
Department of Computer Engineering, Kyung Hee University, Yongin-si 446-701, Korea. kadil@oslab.khu.ac.kr
Adil Mehmood
AM
Khan
Young-Koo
YK
Lee
Sungyoung Y
SY
Lee
Tae-Seong
TS
Kim
D000054:Acceleration; D000328:Adult; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity; D016571:Neural Networks, Computer; D016011:Normal Distribution; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted