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.

Falling represents one of the most serious health risks for elderly people; it may cause irreversible injuries if the individual cannot obtain timely treatment after the fall happens. Therefore, timely and accurate fall detection algorithm research is extremely important. Recently, a number of researchers have focused on fall detection and made many achievements, and most of the relevant algorithm studies are based on ideal class-balanced datasets. However, in real-life applications, the possibilities of Activities of Daily Life (ADL) and fall events are different, so the data collected by wearable sensors suffers from class imbalance. The previously developed algorithms perform poorly on class-imbalanced data. In order to solve this problem, this paper proposes an algorithm that can effectively distinguish falls from a large amount of ADL signals. Compared with the state-of-the-art fall detection algorithms, the proposed method can achieve the highest score in multiple evaluation methods, with a sensitivity of 99.33%, a specificity of 91.86%, an F-Score of 98.44% and an AUC of 98.35%. The results prove that the proposed algorithm is effective on class-imbalanced data and more suitable for real-life application compared to previous works.

Robust and accurate human stride length estimation (SLE) using smartphone integrated inertial measurement units (IMU) is essential in pedestrian dead reckoning (PDR) and mobile health applications. However, the change of smartphone carrying mode (i.e. sensor location) in daily usage often leads to significant estimation errors. To address this problem, we propose a novel SLE framework called Mode-Independent Neural Network (MINN) using multi-source unsupervised domain adaptation (UDA) methods. First, we present a hierarchical neural network to extract spatio-temporal features based on multi-level ResNet and GRU. Then, we use adversarial training and a subclass classifier to build a UDA network that can extract mode-invariant features shared by the data from different modes. Finally, we integrate these architectures into an end-to-end learning framework. Through a systematic evaluation under the leave-one-out setting on two public SLE datasets, the MINN outperforms the state-of-the-art algorithms by achieving stride length error rates of 2.5% and 5.1% in supervised settings. We also evaluated the mode-independent adaptability of this model by performing single and multiple UDA tasks. The results demonstrate that the proposed MINN significantly improves the generalization of SLE model under new subjects or modes.

The main temperature compensation method for MEMS piezoresistive pressure sensors is software compensation, which processes the sensor data using various algorithms to improve the output accuracy. However, there are few algorithms designed for sensors with specific ranges, most of which ignore the operating characteristics of the sensors themselves. In this paper, we propose three temperature compensation methods based on swarm optimization algorithms fused with machine learning for three different ranges of sensors and explore the partitioning ratio of the calibration dataset on Sensor A. The results show that different algorithms are suitable for pressure sensors of different ranges. An optimal compensation effect was achieved on Sensor A when the splitting ratio was 33.3%, where the zero-drift coefficient was 2.88 × 10⁻⁷/°C and the sensitivity temperature coefficient was 4.52 × 10⁻⁶/°C. The algorithms were compared with other algorithms in the literature to verify their superiority. The optimal segmentation ratio obtained from the experimental investigation is consistent with the sensor operating temperature interval and exhibits a strong innovation.

Robust and accurate attitude and heading estimation using Micro-Electromechanical System (MEMS) Inertial Measurement Units (IMU) is the most crucial technique that determines the accuracy of various downstream applications, especially pedestrian dead reckoning (PDR), human motion tracking, and Micro Aerial Vehicles (MAVs). However, the accuracy of the Attitude and Heading Reference System (AHRS) is often compromised by the noisy nature of low-cost MEMS-IMUs, dynamic motion-induced large external acceleration, and ubiquitous magnetic disturbance. To address these challenges, we propose a novel data-driven IMU calibration model that employs Temporal Convolutional Networks (TCNs) to model random errors and disturbance terms, providing denoised sensor data. For sensor fusion, we use an open-loop and decoupled version of the Extended Complementary Filter (ECF) to provide accurate and robust attitude estimation. Our proposed method is systematically evaluated using three public datasets, TUM VI, EuRoC MAV, and OxIOD, with different IMU devices, hardware platforms, motion modes, and environmental conditions; and it outperforms the advanced baseline data-driven methods and complementary filter on two metrics, namely absolute attitude error and absolute yaw error, by more than 23.4% and 23.9%. The generalization experiment results demonstrate the robustness of our model on different devices and using patterns.

With the continuous development of the IoT, compact wireless communication modules have become indispensable components, and their antennas are gradually being developed from external devices into onboard integrated devices. The serpentine antenna, a variant of the monopole antenna known for its small size and easy integration, is often applied to engineering practices. However, its performance has always been closely affected by the size of the surrounding grounding plane. By conducting a characteristic mode analysis (CMA), this study explored the variation patterns in the ground plane size and the resonant frequency. Based on the simulation results, it was clear that when the ground plane size is less than a quarter of the working wavelength, the ground plane will have a significant effect on the antenna’s resonant frequency. Thus, this study further analyzed a serpentine antenna with a grounding branch, and through analysis of the basic law of the influence of grounding structure on the antenna’s performance, we found that by adjusting the branch length, the matching performance of the antenna can be effectively improved. Furthermore, by changing the size of the ground plate, the antenna’s resonant frequency can be adjusted. Such a conclusion will hopefully provide a reference for future designs of integrated antennas in engineering applications.