Harmony: A Hand Wash Monitoring and Reminder System using Smart Watches

Abstract

Hand hygiene compliance is extremely important in hospitals, clinics and food businesses. Caregivers' compliance with hand hygiene is one of the most effective tools in preventing healthcare associated infections (HAIs) in hospitals and clinics. In food businesses, hand hygiene compliance is essential to prevent food contamination, and thus food borne illness. Washing hands properly is the cornerstone of hand hygiene. However, the hand wash compliance rate by the workers (care givers, waiters, chefs, food processors and so on) is not up to the mark. Monitoring hand wash compliance along with a reminder system increases the compliance rate significantly. Quality of a hand wash is also important which can be achieved by washing hands in accordance with standard guidelines. In this paper, we present Harmony, a hand wash monitoring and reminder system that monitors hand wash events and their quality, provides real time feedback, reminds the person of interest when he/she is required to wash hands, and stores related data in a server for further use. Worker worn smart watches are the key components of Harmony that can differentiate hand wash gestures from other gestures with an average accuracy of about 88%. Harmony is robust, scalable, and easy to install, and it overcomes most of the problems of existing related systems.

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

“…Compared to previous studies, our study has the advantages that: We include getting the soap as one class, even though the accuracy is 71%. Since getting soap was detected by the Bluetooth sensors integrated into the dispenser in the previous study [ 17 ], our result from wearable sensors suggests that the IoT sensors in the dispenser can be removed, which would simplify the installation and reduce the cost. In addition, this study categorises opening the faucet, rinsing hands, and turning off the faucet into one class.…”

“…Table 5 summarises the accuracy results of every hand washing step of the proposed method and previous studies. As some previous studies [17, 21] only presented their results in figures, their results are not listed here. Study [22] and study [21] included hand washing steps 8–10: rinsing hands, drying hands, and turning off the faucet.…”

“…Afterwards, segmented signals were then sliced by a fixed‐length window. In the previous studies, the applied windows size ranged from 0.06 s (three data points) [21] to 1 s (50 data points) [17], with the overlap rate in the range of (50%, 70%). In this study, the testing window size was varied between (0.08 s, 3.5 s), with an overlap rate of 0/50%.…”

“…A hidden Markov model (HMM) classifier was used with an accuracy of 85% with firstly determining the order of hand washing classes in the user‐independent mode. Compared to study [17, 18], the before (rubbing hands) and after (turning off the faucet) hand washing activities were included. These two classes were detected using an extra binary classifier, with an accuracy ranging from 35% to 54%.…”

“…In the study of Mondol et al. [ 17 ], the Harmony system was developed to detect the hand washing quality for food workers. In this system, a smartwatch was attached to the participant's wrist to collect the movement signals, a Bluetooth transmitter was integrated into the dispenser to detect whether the participant used the soap, and a Beacon sensor was used to send alerts when the participant was in the area that required hand hygiene or when the participant did not properly wash hands.…”