Wearable sensors can be used to monitor opioid use and other key behaviors of interest, and to prompt interventions that promote behavioral change. The effectiveness of such systems is threatened by the potential of a subject's deliberate non-compliance (DNC) to the monitoring. We define deliberate non-compliance as the process of giving one's device to someone else when surveillance is on-going. The principal aim of this thesis is to develop an approach to leverage movement and cardiac features from a wearable sensor to detect such deliberate non-compliance by individuals under surveillance for opioid use. Data from 11 participants who presented to the Emergency Department following an opioid overdose was analyzed. Using a personalized machine learning classifier (model), we evaluated if a snippet of blood volume pulse (BVP) and accelerometer data received is coming from the expected participant or an alternate person. Analysis of our classier shows the viability of this approach, as we were able to detect DNC (or compliance) with over 90% accuracy within 3 seconds of its occurrence.

Creator

• Singh, Rohitpal

Contributors

• Venkatasubramanian, Krishna Kumar

Degree

• MS

Unit

• Data Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-081018-144145

Keyword

• Deliberate non-compliance detection
• Machine Learning
• opioid surveillance
• triaxial accelerometer
• blood volume pulse (BVP)

Advisor

• Venkatasubramanian, Krishna Kumar

Defense date

• 2018-08-03

Year

• 2018

Date created

• 2018-08-10

Resource type

• Thesis

Rights statement

• In Copyright

Last modified

• 2023-12-05