A wearable sensor system for medication adherence prediction
Abstract
Objective Studies have revealed that non-adherence to prescribed medication can lead to hospital readmissions, clinical complications, and other negative patient outcomes. Though many techniques have been proposed to improve patient adherence rates, they suffer from low accuracy. Our objective is to develop and test a novel system for assessment of medication adherence. Methods Recently, several smart pill bottle technologies have been proposed, which can detect when the bottle has been opened, and even when a pill has been retrieved. However, very few systems can determine if the pill is subsequently ingested or discarded. We propose a system for detecting user adherence to medication using a smart necklace, capable of determining if the medication has been ingested based on the skin movement in the lower part of the neck during a swallow. This, coupled with existing medication adherence systems that detect when medicine is removed from the bottle, can detect a broader range of use-cases with respect to medication adherence. Results Using Bayesian networks, we were able to correctly classify between chewable vitamins, saliva swallows, medication capsules, speaking, and drinking water, with average precision and recall of 90.17% and 88.9%, respectively. A total of 135 instances were classified from a total of 20 subjects. Conclusion Our experimental evaluations confirm the accuracy of the piezoelectric necklace for detecting medicine swallows and disambiguating them from related actions. Further studies in real-world conditions are necessary to evaluate the efficacy of the proposed scheme.
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A necklace has been described that detects swallowing of tablets based on neck movements.
“Recently, a necklace has been described that detects swallowing of tablets based on neck movements[25].”
From Adherence is associated with a favorable outcome after lung transplantation (2019) · cites this paper as [25]
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Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition
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User-Centered Design of Internet of Robotic Things (IoRT) for People Living with Dementia
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- Authors
- Kalantarian, H., Motamed, B., Alshurafa, N., & Sarrafzadeh, M.
- Affiliations
- University of California, Los Angeles (US)
- Northwestern University (US)
- Corresponding author
- Kalantarian, Haik
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2016-05
- Volume
- 69
- Pages
- 43-52
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W2330005225
- Cited by
- 86
- References
- 52
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 7 · 2023: 14 · 2022: 11 · 2021: 12 · 2020: 8 · 2019: 7 · 2018: 6 · 2017: 9 · 2016: 1
- FWCI
- 5.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Family Practice › Medication Adherence and Compliance
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- medication adherence, wearable sensors, piezoelectric sensors, Bayesian networks, action classification, hospital readmission
- MeSH terms
- Wearable Electronic Devices, Administration, Oral, Bayes Theorem, Deglutition, Forecasting, Humans, Medication Adherence
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