Sparse representation based classification scheme for human activity recognition using smartphones

The availability of built-in sensors in mobile devices have paved a way for researchers to accurately determine human activities through these sensors. In this paper, we present a novel action recognition system based on sparse representation wherein, eight different human activities were classified. Our proposed classifier employs data of accelerometer, gyroscope, magnetometer and orientation sensor equipped in smartphones for recognizing human activities. Time-domain and frequency-domain features are derived from the acquired sensor data. We have introduced a novel algorithm for fusing the data from the four sensors using a sparse representation based technique that aid in achieving the best classification performance. In the proposed algorithm, if the majority of the sensors indicate a particular class as the output, then that specific class is assigned as the actual test class. However, if there is a disagreement between the classified output of different sensors, then a novel weighted fusion scheme is introduced to fuse the scores and the residue produced by different sensors. The weight used in fusion is chosen to be the standard deviation of the score vector. Thus, the features of excellent sensors are made to bestow more on to the result of action recognition. Finally, the action label is recognized based on an activity metric that maximizes the score while minimizing the residue. The performance analysis of the proposed system is performed using leave-one-subject-out approach. Performance evaluation metrics like recall, precision, specificity, F-score and accuracy are utilized in projecting the performance of the proposed system. It was shown that the proposed system attained a high overall accuracy of about 97.13%.