Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks (vol 11, 1551, 2020)

被引:1
|
作者
Golestani, Negar
Moghaddam, Mahta
机构
[1] Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, 90089, CA
基金
美国国家航空航天局;
关键词
D O I
10.1038/s41467-020-16581-2
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challenges inherent in designing a sensor-based activity recognition system operating in and around a lossy medium such as the human body to gain a trade-off among power consumption, cost, computational complexity, and accuracy. We introduce an innovative wireless system based on magnetic induction for human activity recognition to tackle these challenges and constraints. The magnetic induction system is integrated with machine learning techniques to detect a wide range of human motions. This approach is successfully evaluated using synthesized datasets, laboratory measurements, and deep recurrent neural networks. © 2020, The Author(s).
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页数:1
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