Detecting hand washing activity among activities of daily living and classification of WHO hand washing techniques using wearable devices and machine learning algorithms

Abstract

During COVID‐19, awareness of proper hand washing has increased significantly. It is critical that people learn the correct hand washing techniques and adopt good hand washing habits. Hence, this study proposes using wearable devices to detect hand washing activity among other daily living activities (ADLs) and classify steps proposed by the World Health Organization (WHO). Two experiments were conducted with 16 participants, aged from 20 to 31. The first experiment was hand washing following WHO regulation (ten participants), and the second experiment was performing eight ADLs (eight participants). All participants wore two wearable devices equipped with accelerometers and gyroscopes; one on each wrist. Four machine learning classifiers were compared in classifying hand washing steps in the leave‐one‐subject‐out (LOSO) mode. The SVM model with Gaussian kernel achieved the best performance in classifying 11 washing hands steps, with an average F1‐score of 0.8501. When detected among the other ADLs, hand washing following WHO regulation obtained the F1‐score of 0.9871. The study demonstrates that wearable devices are feasible to detect hand washing activity and the hand washing techniques as well. The classification results of getting the soap and rubbing thumbs are low, which will be the main focus in the future study.

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

“…They used a customized U-Net architecture to classify nine washing gestures. Zhang et al [16] used Byteflies sensors on each wrist to collect data from the hand-washing process. Features were extracted in both time and frequency domain.…”

Effect of pregnancy on female gait characteristics: a pilot study based on portable gait analyzer and induced acceleration analysis

“…However, the laboratory environment may affect the gait characteristics of participants, and participants’ gait parameters may be affected by psychological discomfort with the laboratory environment. And a single or small amount of gait data collected in the laboratory environment may not fully reflect the real situation ( Sun et al, 2018 ; Akhavanhezaveh and Abbasi-Kesbi, 2021 ), and there are some limitations in the experimental site ( Zhang et al, 2021 ). Therefore, in this study, we use a portable gait analyzer (IDEEA, MiniSun, Fresno, CA, United States) to collect the gait data of participants.…”