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.

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

“…The simulator's added value is to corrupt such data by means of suitable perturbations that are typical of the considered movement disorder. The choice to use IMU data as the basis for the simulation is due to the large amount of scientific research adopting inertial devices for healthcare purposes [19,20]. Although general, to give practical proof of its working principle, the developed simulator is here presented with a focus one of its specific features: the production of PD tremor-related data.…”

Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors

“…Inertial sensors play a key role especially in the development of new assistive technologies [ 26 , 27 , 28 ], they are classified into two major families: (i) accelerometers that measure linear acceleration; and (ii) gyroscopes that measure angular velocity. Most accelerometers and gyroscopes are designed using MEMS (micro-electro-mechanical systems) technology, which allows a reduction in sensor size and thus a wider range of applications [ 29 , 30 ].…”

Robust Attitude and Heading Estimation under Dynamic Motion and Magnetic Disturbance

“…According to the latest research report, the consumer-grade IMU market is expected to grow to 838 million US dollars by 2026, with a compounded annual growth rate of 5% [ 1 ]. Using MEMS-IMU to track motion is crucial in various applications, such as pedestrian dead reckoning (PDR) [ 2 , 3 ], human activity recognition [ 4 , 5 ], health monitoring, rehabilitation robotics, and autonomous vehicles, which benefit fields such as the Internet of Things, smart cities, human–machine interaction, and medical health [ 6 ]. Any improvement in this technology will profoundly impact and extend a series of unprecedented systems and applications.…”