MobiRAR: Real-Time Human Activity Recognition Using Mobile Devices

In this paper we present MobiRAR, a real-time human activity recognition system using mobile devices. The system utilizes the acceleration sensing data from the accelerometer commonly instrumented in mobile devices such as smart phones or smart watches. Our activity recognition method comprises of four steps: data processing, segmentation, feature extraction, and classification. Particularly, the set of features extracted from acceleration sensing data is invariant to device rotations. The proposed method is rigorously evaluated through a dataset consisting of 10 everyday activities including unknown activities collected from 17 users. The results demonstrate that the activities can be distinguished with the overall accuracies of more than 93% precision and recall for individual evaluation and over 80% precision and recall for subject independent evaluation. These results are really promising for practical applications acquiring the recognition of human activities using mobile devices such as energy expenditure estimation and human behavior monitoring.