期刊
HEALTHCARE
卷 10, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/healthcare10071210
关键词
Internet of Health Things (IoHT); IMU; machine learning; motion monitoring; disease diagnosis
资金
- National Key Research and Development Plan Foundation [2018YFB2002700]
- CAS (Chinese Academy of Sciences) Leading Science and Technology (category A) Project [XDA22020100]
- CAS (Chinese Academy of Sciences) STS (Science and Technology Service Network Initiative) Project [2019T3015]
- National Natural Science Foundation of China [61803017, 61827802]
With the rapid development of IoT technologies, IMU-based systems have played a significant role in disease detection. However, traditional numerical interpretation methods struggle to provide satisfactory accuracy due to low-quality raw data and strong electromagnetic interference. Recent years have seen the proposal of machine learning techniques to map IMU-captured data on disease detection and progress. Through the analysis of 81 articles, it is concluded that ML technology can be crucial in disease diagnosis, severity assessment, characteristic estimation, and rehabilitation monitoring.
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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