TY - GEN
T1 - A machine learning approach for medication adherence monitoring using body-worn sensors
AU - Hezarjaribi, Niloofar
AU - Fallahzadeh, Ramin
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2016 EDAA.
PY - 2016/4/25
Y1 - 2016/4/25
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84973641589&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973641589&partnerID=8YFLogxK
U2 - 10.3850/9783981537079_0883
DO - 10.3850/9783981537079_0883
M3 - Conference contribution
AN - SCOPUS:84973641589
T3 - Proceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
SP - 842
EP - 845
BT - Proceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
Y2 - 14 March 2016 through 18 March 2016
ER -