IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
Open accessAbstract
With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.
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The advantages of audio, location, and activity tracking have led to extensive research in these modalities, suggesting their potential for future large-scale population screening and monitoring.
“These advantages have led to extensive research in these three modalities [24–26], suggesting their potential for future large-scale population screening and monitoring.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [25]
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Flagged first, then by citations
Research works printed but not linked: 1
- [128]Garcia-Ceja E. Riegler M. Jakobsen P. Tørresen J. Nordgreen T. Oedegaard K.J. Fasmer O.B. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients Proceedings of the 9th ACM Multimedia Systems Conference Amsterdam, The Netherlands 12–15 June 2018 472 477
Cited by
Sorted by citations
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works
- Authors
- Bo, F., Yerebakan, M., Dai, Y., Wang, W., Li, J., Hu, B., & Gao, S.
- Affiliations
- Chinese Academy of Sciences (CN)
- University of Florida (US)
- Beihang University (CN)
- Corresponding author
- Li, Jia
- Hu, Boyi
- Gao, Shuo
- Venue
- Healthcare
- Publisher
- MDPI AG
- Published
- 2022-06-28
- Volume
- 10
- Issue
- 7
- Pages
- 1210
- ISSN
- 2227-9032 (ISSN-L)
- PMC
- PMC9318359
- OpenAlex
- W4283709029
- Cited by
- 35
- References
- 149
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 9 · 2023: 9 · 2022: 1
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Plan Foundation (2018YFB2002700, XDA22020100, 2019T3015, 61803017, 61827802)
- CAS (Chinese Academy of Sciences) Leading Science and Technology (category A) Project (2018YFB2002700, XDA22020100, 2019T3015, 61803017, 61827802)
- CAS (Chinese Academy of Sciences) STS (Science and Technology Service Network Initiative) Project (2018YFB2002700, XDA22020100, 2019T3015, 61803017, 61827802)
- National Natural Science Foundation of China (2018YFB2002700, XDA22020100, 2019T3015, 61803017, 61827802)
- 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 › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- inertial measurement unit, Internet of Medical Things, machine learning, remote patient monitoring, mental health detection, disease severity assessment, rehabilitation monitoring, electromagnetic interference, wearable sensors
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