Leveraging multi-modal sensing for mobile health: A case review in chronic pain

Min S. Hane Aung, Faisal Alquaddoomi, Cheng-Kang Hsieh, Mashfiqui Rabbi, Longqi Yang, J. P. Pollak, Deborah Estrin, Tanzeem Choudhury

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
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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.
Original languageEnglish
Pages (from-to)962-974
Number of pages13
JournalIEEE Journal of Selected Topics in Signal Processing
Issue number5
Early online date9 May 2016
Publication statusPublished - 1 Aug 2016

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