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Hand hygiene is crucial in preventing the spread of infections and diseases. Lack of hand hygiene is one of the major reasons for healthcare associated infections (HAIs) in hospitals. Adherence to hand hygiene compliance by the workers in the food business is very important for preventing food-borne illness. In addition to healthcare settings and food businesses, hand washing is also vital for personal well-being. Despite the importance of hand hygiene, people often do not wash hands when necessary. Automatic detection of hand washing activity can facilitate justin-time alerts when a person forgets to wash hands. Monitoring hand washing practices is also essential in ensuring accountability and providing personalized feedback, particularly in hospitals and food businesses. Inertial sensors available in smart wrist devices can capture hand movements, and so it is feasible to detect hand washing using these devices. However, it is challenging to detect hand washing using wrist wearable sensors since hand movements are associated with a wide range of activities. In this paper, we present HAWAD, a robust solution for hand washing detection using wrist wearable inertial sensors. We leverage the distribution of penultimate layer output of a neural network to detect hand washing from a wide range of activities. Our method reduces false positives by 77% and improves F1-score by 30% compared to the baseline method.
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Clean Vibes: Hand Washing Monitoring Using Structural Vibration Sensing
We present a passive and non-intrusive sensing system for monitoring hand washing activity using structural vibration sensing. Proper hand washing is one of the most effective ways to limit the spread and transmission of disease, and has been especially critical during the COVID-19 pandemic. Prior approaches include direct observation and sensing-based approaches, but are limited in non-clinical settings due to operational restrictions and privacy concerns in sensitive areas such as restrooms. Our work introduces a new sensing modality for hand washing monitoring, which measures hand washing activity-induced vibration responses of sink structures, and uses those responses to monitor the presence and duration of hand washing. Primary research challenges are that vibration responses are similar for different activities, occur on different surfaces/structures, and tend to overlap/coincide. We overcome these challenges by extracting information about signal periodicity for similar activities through cepstrum-based features, leveraging hierarchical learning to differentiate activities on different surfaces, and denoting “primary/secondary” activities based on their relative frequency and importance. We evaluate our approach using real-world hand washing data across 4 different sink structures/locations, and achieve an average F1-score for hand washing activities of 0.95, which represents a 8.8X and 10.2X reduction in error over two different baseline approaches.
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- Award ID(s):
- 2026699
- PAR ID:
- 10333667
- Date Published:
- Journal Name:
- ACM Transactions on Computing for Healthcare
- ISSN:
- 2691-1957
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3511890
