Rehabilitation Technology and Relapse-Detecting Software for Opioid Addiction Patients


  • Kendall Johnson
  • Padmanabhan Seshaiyer



Opioid addiction rates in the United States have long been on the rise, but the expansion of rehabilitation programs have been falling behind. In 2013, an estimated 20.4 million people out of 22.7 million who required treatment for illicit drug addiction failed to receive proper treatment at specialty facilities. Without proper rehabilitation, patients can often fall back into a cycle of substance abuse. Considering the societal stigma of addiction and seeking treatment, technological rehabilitation provides a more conservative alternative for these patients in terms of cost, privacy, and convenience. Technological rehabilitation is threatened, however, by collaborative non-adherence (CNA), which is the process of giving one’s biosensor to someone else. The goal of this mathematical modeling project is to improve a previously made device, create an app that will be used in tandem with the device, and modify existing algorithms to increase accuracy. In this project we designed updated code models and smartwatch applications that best benefit a recovering opioid addiction patient. Our code models will be adapted to help prevent CNA, and our machine learning algorithms will be modified with the goal of at least 90% accuracy when used on patients outside of our database. Our application will keep track of a patient’s sobriety status and encourage healthy habits. We also plan to provide an online community consisting of past opioid addicts that can connect via the app and act as mutual support. This project has helped us to provide insight on how technology can be used for opioid addiction treatment, and has also examined how group rehabilitation may be more beneficial than isolated treatment. Our app and device are also the beginning to a cheaper avenue of solving the opioid crisis and is an innovative step into the realm of technological rehabilitation. We hope to further develop and deploy our prototype with the user community to validate the mathematical model, computational algorithms, and app. 





College of Science: Department of Mathematical Sciences