Human activity recognition (HAR) using smartphone sensors utilize time-series, multivariate data to detect activities. Time-series data have inherent local dependency characteristics. Moreover, activities tend to be hierarchical and translation invariant in nature. Consequently, convolutional neural networks (convnet) exploit these characteristics, which make it appropriate in dealing with time-series sensor data. In this paper, we propose an architecture of convnets with sensor data gathered from smartphone sensors to recognize activities. Experiments show that increasing the number of convolutional layers increases performance, but the complexity of the derived features decreases with every additional layer. Moreover, preserving the information passed from layer to layer is more important, as opposed to blindly increasing the hyperparameters to improve performance. The convnet structure can also benefit from a wider filter size and lower pooling size setting. Lastly, we show that convnet outperforms all the other state-of-the-art techniques in HAR, especially SVM, which achieved the previous best result for the data set.
|Title of host publication||Neural Information Processing - 22nd International Conference, ICONIP 2015, Proceedings|
|Editors||Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu|
|Number of pages||8|
|Publication status||Published - 2015|
|Event||22nd International Conference on Neural Information Processing, ICONIP 2015 - Istanbul, Turkey|
Duration: 2015 Nov 9 → 2015 Nov 12
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||22nd International Conference on Neural Information Processing, ICONIP 2015|
|Period||15/11/9 → 15/11/12|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)