期刊
INFORMATION FUSION
卷 80, 期 -, 页码 241-265出版社
ELSEVIER
DOI: 10.1016/j.inffus.2021.11.006
关键词
Wearable device; Information fusion; Human activity recognition; Machine learning; Deep learning; Transfer learning
资金
- National Natural Science Foundation of China [61803072, 61873044, 61903062]
- Natural Science Foundation of Liaoning Province, China [2021-MS-111]
- Fundamental Research Funds for the Central Universities, China [DUT20JC03, DUT20JC44]
This paper introduces common wearable sensors, smart wearable devices, and key application areas, proposing fusion methods for multi-modality and multi-location sensors. It comprehensively surveys important aspects of wearable sensor fusion methods in human activity recognition, including new technologies in unsupervised learning and transfer learning, while also discussing open research issues that need further investigation and improvement.
This paper firstly introduces common wearable sensors, smart wearable devices and the key application areas. Since multi-sensor is defined by the presence of more than one model or channel, e.g. visual, audio, environmental and physiological signals. Hence, the fusion methods of multi-modality and multi-location sensors are proposed. Despite it has been contributed several works reviewing the stateoftheart on information fusion or deep learning, all of them only tackled one aspect of the sensor fusion applications, which leads to a lack of comprehensive understanding about it. Therefore, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of the fusion methods of wearable sensors. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of multi-sensor applications for human activity recognition, including those recently added to the field for unsupervised learning and transfer learning. Finally, the open research issues that need further research and improvement are identified and discussed.
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