A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer

• Published in: IEEE transactions on information technology in biomedicine Vol. 14; no. 5; pp. 1166 - 1172
• Main Authors: Khan, A M, Young-Koo Lee, Lee, S Y, Tae-Seong Kim
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2010
• Subjects: Acceleration; Accelerometer; Accelerometers; Adult; Artificial neural networks; artificial-neural nets (ANNs); autoregressive (AR) modeling; Computer vision; Discriminant Analysis; Female; human-activity recognition; Humans; IEEE activities; Legged locomotion; Linear discriminant analysis; Male; Monitoring, Ambulatory - methods; Motor Activity; Neural Networks (Computer); Normal Distribution; Regression Analysis; Signal Processing, Computer-Assisted; Vectors; Wearable sensors
• ISSN: 1089-7771; 1558-0032; 1558-0032
• DOI: 10.1109/TITB.2010.2051955

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.