In this paper, the autoregressive (AR) model of time-series is presented to recognize human activity from a tri-axial accelerometer data. Four orders of autoregressive model for accelerometer data is built and the AR coefficients are extracted as features for activity recognition. Classification of the human activities is performed with Support Vector Machine (SVM). The average recognition results for four activities (running, still, jumping and walking) using the proposed AR-based features are 92.25%, which are better than using traditional frequently used time domains features (mean, standard deviation, energy and correlation of acceleration data) and FFT features. The results show that AR coefficients obvious discriminate different human activities and it can be extract as an effective feature for the recognition of accelerometer date.
In REF , the authors extract the autoregressive coefficients of accelerometer data as the characteristics of activity recognition, and use SVM to classify human activity.
Activity recognition from acceleration data using AR model representation and SVM
2008 International Conference on Machine Learning and Cybernetics
2008 International Conference on Machine Learning and Cybernetics
Computer Science