Harnessing Digital Biomarkers of Substance Use and Addiction with Large-Scale Mobile Sensor Data

#WearableHealthSensing #MultimodalPhysiologicalData #SubstanceUse, #DrugAddiction #Opioid #Pharmacokinetics #TimeSeriesAnalysis

IEEE CS SD Invited Seminar Series 2024 - Lecture 7 (Virtual)

Mobile sensors are often used in health to track and monitor health, ranging from daily activities to diagnosing life-threatening conditions; however, they are underutilized for substance use and its disorders. Our work is focused on developing digital biomarkers from the physiological data captured from wearable devices for addiction. Specifically, we build models that combine the multimodal sensor data from wearable devices to detect drug administration, predict drug-induced mental states such as drug craving and euphoria. We further show that integrating drug pharmacokinetics into these data-driven models enhances the accuracy of drug monitoring, thereby increasing the generalizability and trust. A consistent pattern observed among these models was bias based on drug-usage history; therefore, we develop a model that screens users and distinguishes opioid misusers from prescription users, which would allow for more accurate prescription of opioids, minimizing the risk of addiction. 

  Date and Time




  • Date: 11 Jun 2024
  • Time: 05:30 PM to 06:30 PM
  • All times are (UTC-07:00) Pacific Time (US & Canada)
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  • Co-sponsored by Media Partner: Open Research Institute (ORI)
  • Starts 29 May 2024 12:00 AM
  • Ends 11 June 2024 06:30 PM
  • All times are (UTC-07:00) Pacific Time (US & Canada)
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Bhanu T. Gullapalli of University of California San Diego


Harnessing Digital Biomarkers of Substance Use and Addiction with Large-Scale Mobile Sensor Data


 I’m a PhD Candidate with Professor Tauhidur Rahman at the University of California, San Diego. I’m currently working with the wonderful people at the MOSAIC Lab, who focus on advancing the capabilities of mobile sensor data in health. I focus on utilizing multimodal physiological data from wearable devices, along with individuals’ demographic characteristics (age, comorbidities, etc.), to develop digital biomarkers in the field of substance use addiction. Primarily, I work with mobile sensor data collected in both hospital settings and the real world to predict various aspects of the addiction cycle for different drugs. Secondly, I aim to bridge the gap between the sensed biomarkers and intervenable actions to effectively help individuals break the cycle of addiction. In terms of my social life, I enjoy listening to people and stories of any form. In my free time, I like to map stars, draw, and write poems and short stories.

Address:United States


  • Invited talk from Bhanu Teja Gullapalli, PhD Candidate at the University of California San Diego. 
  • Q/A Session

- 7th lecture of the 2024 Invited Seminar Series (Virtual) organized by IEEE Computer Society San Diego Chapter. Previous lectures: 2023 and 2024 invited seminar series.