IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Abstract

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.

Keywords: IMU; Internet of Health Things (IoHT); disease diagnosis; machine learning; motion monitoring.

Figure 1

Generalized workflow for monitoring human body motions.

Figure 2

A taxonomy of the selected works.

Figure 3

Distribution of the sensor locations.

Figure 4

Traditional inertial tracking system. a: acceleration; m: magnetic field; ω: angular rate; p: position; v: velocity; φ: attitude; δX: estimated error. Abbreviations: ZARU: Zero Angular Rate Update; HDE: Heuristic Drift Elimination.

Figure 5

Machine learning-based method.

Figure 6

A summary of the reviewed works. The color of each bubble represents the number of reviewed works covering a given data processing method (row) and detailed application scenarios (column).