SCH: EXP: LifeRhythm: A Framework for Automatic and Pervasive Depression Screening Using Smartphones

Award #: NSF IIS-1407205


Due to prevalence and significant health and economic impacts, depression is a profound public health problem. Currently, diagnosis of depression is based on physician-administered interview tools or patient self reporting. While physician-administered tools are more authoritative, availability is constrained both by cost and lack of access to trained mental health professionals. Patient self-reporting, on the other hand, suffers from recall bias and inconsistent patient participation. In particular, neither approach satisfactorily addresses the chronic and recurring nature of depression that requires frequent diagnosis for monitoring onset and progress. To address depression as a public health problem, there is urgent need for an objective, accurate, and easily accessible depression screening tool for mass utilization. The ubiquitous adoption of smartphones around the world creates new opportunities in automatic and pervasive screening of depression over large populations.

The goal of this project is to develop LifeRhythm, an automated system for automatic and pervasive depression screening using smartphone data. LifeRhythm continuously monitors the behavioral rhythms of individuals through their smartphones, extracts normalized features from the raw data, and applies multiple machine-learning models for real-time diagnosis. The project applies LifeRhythm to two settings that have complementary strengths. The first setting uses “high-resolution” sensing data collected from smartphones, which provides extremely rich and descriptive behavioral data, allowing the best leverage for machine learning models. The second setting uses “low-resolution” wireless association meta-data collected passively from large-scale WiFi networks, which eliminates the need of data collection on smartphones and can be especially valuable for a large organization, where it could automatically provide depression screening of tens of thousands of people simultaneously at very little cost. Development of LifeRhythm will be coupled with several tightly related machine-learning research efforts, including novel techniques for collaborative prediction, integrative learning, modeling of temporal dynamics, and model refinement using multiplicative-weights-based techniques. Though this proposal is primarily focused on development of screening tools, future work could naturally develop an associated intervention program. In addition, this research may lead to methodologies that are applicable to other mood disorders such as bipolar illness. The education plan of this proposal includes developing and enhancing various undergraduate and graduate-level courses, as well as disseminating the results to medical students through clinical supervision. The broader impacts will include dissemination of research results (and the annotated dataset) to the technical communities, increasing the participation from under-represented groups in research, and outreach activities.

People


This is a joint project between UCONN Storrs and UCONN Health Center, with collaborator Nasos Bamis (Seldera LLC) .

Storrs:

    Bing Wang (PI)
    Jinbo Bi (Co-PI)
    Alexander Russell (Co-PI)
    Asma Farhan (PhD Student)
    Huizhong Gao (Master student)
    Jin Lu (PhD student)
    Reynaldo Morillo (Master student)
    Shweta Ware (PhD Student)
    Sean Bridges (Undergraduate student)
    Ammad Shaikh (Undergraduate student)

UCHC:

    Alok Banga (former Co-PI)
    Jayesh Kamath (Co-PI)


Publications:

  • Automatic depression prediction using Internet traffic characteristics on smartphones
    C. Yue, S. Ware, R. Morillo, J. Lu, C. Shang, J. Bi, J. Kamath, A. Russell, A. Bamis, and B. Wang.
    Smart Health. Vol. 18. November, 2020.
  • Predicting depressive symptoms using smartphone data
    S. Ware, C. Yue, R. Morillo, J. Lu, C. Shang, J. Bi, A. Russell, A. Bamis, and B. Wang.
    Proceedings of ACM/IEEE CHASE, October 2019.
  • Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure
    Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander Russell, and Bing Wang.
    Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), Vol. 2, No. 4, December 2018 (Ubicomp 2019).
    • Fusing Location Data for Depression Prediction
      Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang.
      IEEE Transactions on Big Data, accepted.
    • Fusing Location Data for Depression Prediction
      Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang.
      Proceedings of Ubiquitous Intelligence and Computing (UIC), August 2017.