Exploiting IMU Sensors for IOT Enabled Health Monitoring
Abstract
Inertial Measurement Units (IMUs) embedded in commercial mobile devices are a good choice for continuous monitoring in healthcare domain due to their attractive form factor and low power consumption. We present improved and accurate sensing algorithms using a single IMU to sense basic events like step count, stride length, fall, immobility etc. Our algorithms have been shown to perform better than the state of the art algorithms, and are implemented in such a way that IMU is not bound to any specific position or orientation with respect to the user. We propose a 3-layer based framework for a complete end-to-end system architecture for IoT enabled health monitoring, useful for application in areas like individual fitness monitoring and elderly care.
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Larger batteries tend to reduce charging time and increase between-charging time, but they are uncomfortable to use because they are larger in form size than smaller batteries and increase the overall dimensions of the wearable device.
“Larger batteries tend to reduce charging time and increase between-charging time, but they are uncomfortable to use, since they are larger in form size than smaller batteries and increase the overall dimensions of the wearable device [122].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [122]
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Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435 · see citing works
- Authors
- Chandel, V., Sinharay, A., Ahmed, N., & Ghose, A.
- Affiliations
- Tata Consultancy Services (India) (IN)
- Venue
- Proceedings of the First Workshop on IoT-enabled Healthcare and Wellness Technologies and Systems
- Publisher
- ACM
- Published
- 2016-06-30
- Pages
- 21-22
- OpenAlex
- W2461508865
- Cited by
- 42
- References
- 8
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 10 · 2022: 1 · 2021: 11 · 2020: 5 · 2019: 4 · 2017: 4 · 2016: 1
- FWCI
- 1.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- inertial measurement unit, mobile devices, IoT health monitoring, step counting, stride length estimation, fall detection, fitness tracking, elderly care
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