WashRing : An Energy-Efficient and Highly Accurate Handwashing Monitoring System Via Smart Ring

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Jiongzhang Chen
  • Chengwen Luo
  • Jia Zhang
  • Yuliang Zhao


Original languageEnglish
Journal / PublicationIEEE Transactions on Mobile Computing
Publication statusOnline published - 7 Dec 2022


The outbreak of COVID-19 has greatly changed everyone's lifestyle all over the world. One of the best ways to prevent the spread of infections is by washing hands properly. Although a number of hand hygiene monitoring systems have been proposed, they either cannot achieve high accuracy in practice or work only in limited environments such as hospitals. Therefore, a ubiquitous, energy-efficient and highly accurate hand hygiene monitoring system is still lacking. In this paper, we present WashRing—the first smart ring-based handwashing monitoring system. In WashRing, we design a Partially Observable Markov Decision Process (POMDP) based adaptive sampling approach to achieve high energy efficiency. Then, we design an automatic feature extraction scheme based on wavelet scattering and a CNN-LSTM neural network to achieve fine-grained gesture recognition. Finally, we model the handwashing gesture classification as a few-shot learning problem to mitigate the burden of collecting extensive data from five fingers. We collect data from 25 subjects over 2 months and evaluate the system performance on both commercial OURA ring and customized ring. Evaluation results show that WashRing achieves 97.8% accuracy which is 10.2%–15.9% higher than state-of-the-arts. Our adaptive sampling approach reduces energy consumption by 64.2% compared to fixed duty cycle sampling strategies.

Research Area(s)

  • Batteries, Data models, Deep learning, Energy efficiency, energy-efficiency, Feature extraction, hand washing, Mobile computing, Monitoring, Thumb, wearable devices