Deep learning and transfer learning for device-free human activity recognition: A survey
Device-free activity recognition plays a crucial role in smart building, security, and human–computer interaction, which shows its strength in its convenience and cost-efficiency. Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models, bu...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-12-01
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| Series: | Journal of Automation and Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949855422000077 |
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| Summary: | Device-free activity recognition plays a crucial role in smart building, security, and human–computer interaction, which shows its strength in its convenience and cost-efficiency. Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models, but it suffers from the limitation of manual feature design. Deep learning overcomes such issues by automatic high-level feature extraction, but its performance degrades due to the requirement of massive annotated data and cross-site issues. To deal with these problems, transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics. This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition. We begin with the motivation of deep learning and transfer learning, and then introduce the major sensor modalities. Then the deep and transfer learning techniques for device-free human activity recognition are introduced. Eventually, insights on existing works and grand challenges are summarized and presented to promote future research. |
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| ISSN: | 2949-8554 |