The diversity in the phone placements of different mobile users' dailylife increases the difficulty of recognizing human activities by using mobile phone accelerometer data. To solve this problem, a compressed sensing method to recognize human activities that is based on compressed sensing theory and utilizes both raw mobile phone accelerometer data and phone placement information is proposed. First, an over-complete dictionary matrix is constructed using sufficient raw tri-axis acceleration data labeled with phone placement information. Then, the sparse coefficient is evaluated for the samples that need to be tested by resolving L1 minimization. Finally, residual values are calculated and the minimum value is selected as the indicator to obtain the recognition results. Experimental results show that this method can achieve a recognition accuracy reaching 89.86%, which is higher than that of a recognition method that does not adopt the phone placement information for the recognition process. The recognition accuracy of the proposed method is effective and satisfactory.
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