Passive and In-Situ assessment of mental and physical well-being using mobile sensors
Open accessAbstract
The idea of continuously monitoring well-being using mobile-sensing systems is gaining popularity. In-situ measurement of human behavior has the potential to overcome the short comings of gold-standard surveys that have been used for decades by the medical community. However, current sensing systems have mainly focused on tracking physical health; some have approximated aspects of mental health based on proximity measurements but have not been compared against medically accepted screening instruments. In this paper, we show the feasibility of a multi-modal mobile sensing system to simultaneously assess mental and physical health. By continuously capturing fine grained motion and privacy-sensitive audio data, we are able to derive different metrics that reflect the results of commonly used surveys for assessing well-being by the medical community. In addition, we present a case study that highlights how errors in assessment due to the subjective nature of the responses could potentially be avoided by continuous sensing and inference of social interactions and physical activities.
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The NeuroSence application detects sleep and speech/conversation data using pre-trained classifiers on accelerometer and audio data.
“Furthermore, our application detects sleep [17] and speech/conversation data [54, 76] by incorporating pre-trained classifiers using accelerometer and audio data from a microphone, respectively.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [76]
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- Authors
- Rabbi, M., Ali, S., Choudhury, T., & Berke, E.
- Affiliations
- Cornell University (US)
- Dartmouth Medical School, Lebanon, NH, USA
- Venue
- Proceedings of the 13th international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2011-09-17
- Volume
- 2011
- Pages
- 385-394
- PMC
- PMC4180507
- OpenAlex
- W2161897659
- Cited by
- 243
- References
- 55
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 15 · 2023: 25 · 2022: 18 · 2021: 22 · 2020: 27 · 2019: 15 · 2018: 16 · 2017: 23 · 2016: 17 · 2015: 13 · 2014: 17 · 2013: 14 · 2012: 9
- FWCI
- 9.52
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institute on Aging (K23AG036934)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- well-being assessment, mental health, physical health, mobile sensing, in situ measurement, continuous monitoring, multimodal sensing, motion sensing, audio sensing, physical activity
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