IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review
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...
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| Format: | Article |
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MDPI AG
2022-06-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/7/1210 |
| _version_ | 1797406209517551616 |
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| author | Fan Bo Mustafa Yerebakan Yanning Dai Weibing Wang Jia Li Boyi Hu Shuo Gao |
| author_facet | Fan Bo Mustafa Yerebakan Yanning Dai Weibing Wang Jia Li Boyi Hu Shuo Gao |
| author_sort | Fan Bo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T03:23:03Z |
| format | Article |
| id | doaj.art-fd8d5029e0394ea3bb8dd0df2ae28adf |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T03:23:03Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-fd8d5029e0394ea3bb8dd0df2ae28adf2023-12-03T15:07:02ZengMDPI AGHealthcare2227-90322022-06-01107121010.3390/healthcare10071210IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A ReviewFan Bo0Mustafa Yerebakan1Yanning Dai2Weibing Wang3Jia Li4Boyi Hu5Shuo Gao6Smart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, ChinaDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USASchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaSmart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, ChinaSmart Sensing Research and Development Center, Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, ChinaDepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USASchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, ChinaWith 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.https://www.mdpi.com/2227-9032/10/7/1210Internet of Health Things (IoHT)IMUmachine learningmotion monitoringdisease diagnosis |
| spellingShingle | Fan Bo Mustafa Yerebakan Yanning Dai Weibing Wang Jia Li Boyi Hu Shuo Gao IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review Healthcare Internet of Health Things (IoHT) IMU machine learning motion monitoring disease diagnosis |
| title | IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review |
| title_full | IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review |
| title_fullStr | IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review |
| title_full_unstemmed | IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review |
| title_short | IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review |
| title_sort | imu based monitoring for assistive diagnosis and management of ioht a review |
| topic | Internet of Health Things (IoHT) IMU machine learning motion monitoring disease diagnosis |
| url | https://www.mdpi.com/2227-9032/10/7/1210 |
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