Domain Adaptation for Human Fall Detection Using WiFi Channel State Information

2019 ◽  
pp. 177-181 ◽  
Author(s):  
Hirokazu Narui ◽  
Rui Shu ◽  
Felix F Gonzalez-Navarro ◽  
Stefano Ermon
Keyword(s):  
Channel State Information ◽  
Domain Adaptation ◽  
Fall Detection ◽  
Channel State ◽  
State Information

scholarly journals WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network

2021 ◽  
Vol 11 (8) ◽  
pp. 3329
Author(s):  
Pengli Hu ◽  
Chengpei Tang ◽  
Kang Yin ◽  
Xie Zhang
Keyword(s):  
Computational Complexity ◽  
Gesture Recognition ◽  
Channel State Information ◽  
Domain Adaptation ◽  
Recognition Performance ◽  
Recognition System ◽  
Channel State ◽  
Data Set ◽  
State Information ◽  
Cross Domain

Wi-Fi sensing technology based on deep learning has contributed many breakthroughs in gesture recognition tasks. However, most methods concentrate on single domain recognition with high computational complexity while rarely investigating cross-domain recognition with lightweight performance, which cannot meet the requirements of high recognition performance and low computational complexity in an actual gesture recognition system. Inspired by the few-shot learning methods, we propose WiGR, a Wi-Fi-based gesture recognition system. The key structure of WiGR is a lightweight few-shot learning network that introduces some lightweight blocks to achieve lower computational complexity. Moreover, the network can learn a transferable similarity evaluation ability from the training set and apply the learned knowledge to the new domain to address domain shift problems. In addition, we made a channel state information (CSI)-Domain Adaptation (CSIDA) data set that includes channel state information (CSI) traces with various domain factors (i.e., environment, users, and locations) and conducted extensive experiments on two data sets (CSIDA and SignFi). The evaluation results show that WiGR can reach 87.8%–94.8% cross-domain accuracy, and the parameters and the calculations are reduced by more than 50%. Extensive experiments demonstrate that WiGR can achieve excellent recognition performance using only a few samples and is thus a lightweight and practical gesture recognition system compared with state-of-the-art methods.

scholarly journals WmFall: WiFi-based multistage fall detection with channel state information

2018 ◽  
Vol 14 (10) ◽  
pp. 155014771880571 ◽  
Author(s):  
Xu Yang ◽  
Fangyuan Xiong ◽  
Yuan Shao ◽  
Qiang Niu
Keyword(s):  
Channel State Information ◽  
Learning Algorithm ◽  
Detection System ◽  
Fall Detection ◽  
Special Method ◽  
Special Equipment ◽  
Channel State ◽  
State Information ◽  
Detection Systems ◽  
Deep Learning Algorithm

Traditional fall detection systems require to wear special equipment like sensors or cameras, which often brings the issues of inconvenience and privacy. In this article, we introduce a novel multistage fall detection system using the channel state information from WiFi devices. Our work is inspired by the fact that different actions have different effects on WiFi signals. By fully analyzing and exploring the channel state information characters, the falling actions can be distinguished from other movements. Considering that falling and sitting are very similar to each other, a special method is designed for distinguishing them with deep learning algorithm. Finally, the fall detection system is evaluated in a laboratory, which has 89% detection precision with false alarm rate of 8% on the average.

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