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IMU-Based Monitoring for Assistive Disease Diagnosis and Management-A Review(2).pdf (1.3 MB)
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IMU-Based Monitoring for Assistive Disease Diagnosis and Management of IoHT: A Review

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posted on 25.02.2022, 06:25 by Fan Bo, Mustafa Yerebakan, Yanning DaiYanning Dai, Weibing Wang, Jia Li, Shuo GaoShuo Gao, Hu Boyi
With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) 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. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends.

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Email Address of Submitting Author

shuo_gao@buaa.edu.cn

Submitting Author's Institution

the School of Instrumentation and Optoelectronic Engineering, Beihang University

Submitting Author's Country

China