Name
Title | ||
|---|---|---|
Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges. |
Abstract | ||
|---|---|---|
•Review of deep learning methods for sensor based human activity recognition.•Categorize the studies into generative, discriminative and hybrid methods.•Present training, evaluation procedures and Common datasets.•Outline open research issues presented as future directions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.eswa.2018.03.056 | Expert Systems with Applications |
Keywords | Field | DocType |
Deep learning,Mobile and wearable sensors,Human activity recognition,Feature representation,Review | Activity recognition,Computer science,Wearable computer,Algorithm,Feature extraction,Home automation,Artificial intelligence,Deep learning,Contextual image classification,Wireless sensor network,Feature learning,Machine learning | Journal |
Volume | ISSN | Citations |
105 | 0957-4174 | 38 |
PageRank | References | Authors |
0.93 | 139 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henry Friday Nweke | 1 | 59 | 4.57 |
| Teh Ying Wah | 2 | 126 | 9.82 |
| Mohammed Ali Al-Garadi | 3 | 104 | 5.69 |
| Uzoma Rita Alo | 4 | 42 | 3.02 |