Paper Info
Title
Multi-sensor information fusion based on machine learning for real applications in human activity recognition: State-of-the-art and research challenges
Abstract
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.
Year
DOI
Venue
2022
10.1016/j.inffus.2021.11.006
Information Fusion
Keywords
DocType
Volume
Wearable device,Information fusion,Human activity recognition,Machine learning,Deep learning,Transfer learning
Journal
80
ISSN
Citations 
PageRank 
1566-2535
6
0.56
References 
Authors
0
10
Name
Order
Citations
PageRank
Sen Qiu160.56
Hongkai Zhao260.56
Nan Jiang360.56
Zhelong Wang460.56
Long Liu560.56
Yi An660.56
Hongyu Zhao760.56
Xin Miao860.56
Ruichen Liu961.57
Giancarlo Fortino1060.90