Abstract Background Unified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson’s disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson’s disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications. Methods We developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time–frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time–frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data. Results The algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scoresmore »
HAWAD: Hand Washing Detection using Wrist Wearable Inertial Sensors
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.
- Award ID(s):
- 1646470
- Publication Date:
- NSF-PAR ID:
- 10190248
- Journal Name:
- International Conference on Distributed Computing in Sensor Systems and workshops
- ISSN:
- 2325-2944
- Sponsoring Org:
- National Science Foundation
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