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Real-Time Physical Activity Recognition on Smart Mobile Devices Using Convolutional Neural Networks
| Content Provider | MDPI |
|---|---|
| Author | Peppas, Konstantinos Tsolakis, Apostolos C. Krinidis, Stelios Tzovaras, Dimitrios |
| Copyright Year | 2020 |
| Description | Given the ubiquity of mobile devices, understanding the context of human activity with non-intrusive solutions is of great value. A novel deep neural network model is proposed, which combines feature extraction and convolutional layers, able to recognize human physical activity in real-time from tri-axial accelerometer data when run on a mobile device. It uses a two-layer convolutional neural network to extract local features, which are combined with 40 statistical features and are fed to a fully-connected layer. It improves the classification performance, while it takes up 5–8 times less storage space and outputs more than double the throughput of the current state-of-the-art user-independent implementation on the Wireless Sensor Data Mining (WISDM) dataset. It achieves 94.18% classification accuracy on a 10-fold user-independent cross-validation of the WISDM dataset. The model is further tested on the Actitracker dataset, achieving 79.12% accuracy, while the size and throughput of the model are evaluated on a mobile device. |
| Starting Page | 8482 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10238482 |
| Journal | Applied Sciences |
| Issue Number | 23 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-11-27 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Industrial Engineering Activity Recognition Convolutional Neural Networks Deep Learning Human Activity Recognition Mobile Inference Time Series Classification Feature Extraction Accelerometer Data |
| Content Type | Text |
| Resource Type | Article |
