Name
Title | ||
|---|---|---|
A Machine Learning Approach For Medication Adherence Monitoring Using Body-Worn Sensors |
Abstract | ||
|---|---|---|
One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists. |
Year | Venue | Field |
|---|---|---|
2016 | PROCEEDINGS OF THE 2016 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) | Decision tree,Monitoring system,Wearable computer,Computer science,Artificial intelligence,Chronic disease,Machine learning |
DocType | ISSN | Citations |
Conference | 1530-1591 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niloofar Hezarjaribi | 1 | 1 | 1.04 |
| Ramin Fallahzadeh | 2 | 40 | 6.63 |
| Hassan Ghasemzadeh | 3 | 656 | 61.36 |