Name
Title | ||
|---|---|---|
A hybrid deep convolutional and recurrent neural network for complex activity recognition using multimodal sensors. |
Abstract | ||
|---|---|---|
Complex activities refer to users' activities performed in their daily lives (e.g., having dinner, shopping, etc.). Complex activity recognition is a valuable issue in wearable and mobile computing. The time-series sensory data from multimodal sensors have sophisticated relationships to characterize the complex activities (e.g., intra-sensor relationships, inter-sensor relationships, and temporal relationships), making the traditional methods based on manually designed features ineffective. To this end, we propose HConvRNN, an end-to-end deep neural network for complex activity recognition using multimodal sensors by integrating convolutional neural network (CNN) and recurrent neural network (RNN). To be specific, it uses a hierarchical CNN to exploit the intra-sensor relationships among similar sensors and merge intra-sensor relationships of different sensor modalities into inter-sensor relationships, and uses a RNN to model the temporal relationships of signal dynamics. The experiments based on real-world datasets show that HConvRNN outperforms the existing complex activity recognition methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.neucom.2019.06.051 | Neurocomputing |
Keywords | Field | DocType |
Complex activity,Multimodal sensors,Convolutional neural network,Recurrent neural network | Modalities,Mobile computing,Activity recognition,Wearable computer,Convolutional neural network,Recurrent neural network,Exploit,Artificial intelligence,Artificial neural network,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
362 | 0925-2312 | 2 |
PageRank | References | Authors |
0.35 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqi Lv | 1 | 18 | 3.81 |
| Wei Xu | 2 | 329 | 38.14 |
| Tieming Chen | 3 | 29 | 5.11 |