Reducing environment exposure to COVID-19 by IoT sensing and computing with deep learning

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff’s hands are considered a major source of hospital infection. Effective hand-washing can prevent up to 30% of diarrhea-related illnesses, which is crucial in preventing nosocomial infections (Tartari et al. in Clin Microbiol Infect 23(9):596–598, 2017). This paper proposes an electronic-based real-time hand-washing identification framework called Alpha Hand Washing (ALPHA HW). The system uses camera-based identification, edge computing, and deep learning to automatically identify correct hand-washing behaviors, thereby facilitating effective hand-washing (Bertasius et al. in: Computer vision and pattern recognition, 2015). We achieved an accuracy of 78.0% mAP and a speed of 52 FPS in detecting scenes using specific monitoring datasets in hospitals by constructing the complex recognition system into a grid computing problem. Leveraging edge computing, our system achieves real-time identification with low memory consumption and high-efficiency computation. Alpha HW presents scientific and financial values in epidemic prevention and control that can facilitate popularization to reduce virus spread (Bewley et al. in 2016 IEEE international conference on image processing, 2016).