Authors:
Rohitpal Singh
1
;
Brittany Lewis
1
;
Brittany Chapman
2
;
Stephanie Carreiro
2
and
Krishna Venkatasubramanian
1
Affiliations:
1
Worcester Polytechnic Institute, Worcester, MA and U.S.A.
;
2
University of Massachusetts Medical School, Worcester, MA and U.S.A.
Keyword(s):
Opioid Epidemic, Wearable Technology, Biosensor, Adherence, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Devices
;
Health Information Systems
;
Human-Computer Interaction
;
Pattern Recognition and Machine Learning
;
Pervasive Health Systems and Services
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
Wearable biosensors can be used to monitor opioid use, a problem of dire societal consequence given the current
opioid epidemic in the US. Such surveillance can prompt interventions that promote behavioral change.
The effectiveness of biosensor-based monitoring is threatened by the potential of a patient’s collaborative
non-adherence (CNA) to the monitoring. We define CNA as the process of giving one’s biosensor to someone
else when surveillance is ongoing. The principal aim of this paper is to leverage accelerometer and blood volume
pulse (BVP) measurements from a wearable biosensor and use machine-learning for the novel problem
of CNA detection in opioid surveillance. We use accelerometer and BVP data collected from 11 patients who
were brought to a hospital Emergency Department while undergoing naloxone treatment following an opioid
overdose. We then used the data collected to build a personalized classifier for individual patients that capture
the uniqueness of their blood volume
pulse and triaxial accelerometer readings. In order to evaluate our detection
approach, we simulate the presence (and absence) of CNA by replacing (or not replacing) snippets of the
biosensor readings of one patient with another. Overall, we achieved an average detection accuracy of 90.96%
when the collaborator was one of the other 10 patients in our dataset, and 86.78% when the collaborator was
from a set of 14 users whose data had never been seen by our classifiers before.
(More)