Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain

Aung, Min S. Hane, Alquaddoomi, Faisal, Hsieh, Cheng-Kang, Rabbi, Mashfiqui, Yang, Longqi, Pollak, J. P., Estrin, Deborah and Choudhury, Tanzeem (2016) Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain. IEEE Journal of Selected Topics in Signal Processing, 10 (5). pp. 962-974. ISSN 1932-4553

[thumbnail of Accepted_Manuscript]
PDF (Accepted_Manuscript) - Accepted Version
Download (510kB) | Preview


Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing.

Item Type: Article
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 26 Sep 2019 12:30
Last Modified: 22 Oct 2022 05:16
URI: https://ueaeprints.uea.ac.uk/id/eprint/72396
DOI: 10.1109/JSTSP.2016.2565381

Actions (login required)

View Item View Item