With the extensive applications of biomagnetic signals derived from active biological tissue in both clinical diagnoses and human-computer-interaction, there is an increasing need for approachable weak biomagnetic sensing technology. The inherent merits of giant magnetoresistance (GMR) and its high integration with multiple technologies makes it possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost sensors, considering that the magnetic field intensity attenuates rapidly with distance. This paper focuses on the state-of-art in integrated GMR technology for approachable biomagnetic sensing from the perspective of discipline fusion between them. The progress in integrated GMR to overcome the challenges in weak biomagnetic signal detection towards high resolution portable applications is addressed. The various strategies for 1/f noise reduction and sensitivity enhancement in integrated GMR technology for sub-pT biomagnetic signal recording are discussed. In this paper, we review the developments of integrated GMR technology for in vivo/vitro biomagnetic source imaging and demonstrate how integrated GMR can be utilized for biomagnetic field detection. Since the field sensitivity of integrated GMR technology is being pushed to fT/Hz^0.5 with the focused efforts, it is believed that the potential of integrated GMR technology will make it preferred choice in weak biomagnetic signal detection in the future.

Following the rule of human-centricity, Human Motion Digital Twin (HMDT) attempts to apply human motion data to ensure the development and well-being of human beings. Particularly, perception and estimation of human motion play fundamental roles in realizing HMDT. This work proposes an inertial motion capture system for human motion digital twin (InMoDT). The designed motion capture device is made up of a hub node and inertial measurement units attached to the human body. The proposed algorithm framework supported by sensor fusion and pose calibration algorithms, enables to acquire orientations of sensors and body segments. With the deployment of algorithms, InMoDT achieves an average root mean square error of 4.7 $^{\circ}$ in estimating orientations when compared with an optical motion capture system. Experimental results show a great correlation ( $92.5\%$ ) and agreement ( $97.8\%$ ) between InMoDT and the optical system. The abilities of InMoDT are spotted in terms of human-centric applications based on the integration of human, cyber system, and physical system, such as motion monitoring and estimation, and human-robot teleoperation. Note to Practitioners —This paper is motivated by the problem of inadequate attention on humans in Cyber-Physical System (CPS) while the roles of operators have a significant effect on industry. With the popular applications of digital twins in CPS, HMDT is expected to monitor, analyze, and assess motion data for facilitating the Human-Cyber-Physical System (HCPS) In this research work, the authors present a system for whole-body motion capture. The proposed system based on a wearable inertial sensor-based device provides a solution to construct HMDT. Moreover, a novel algorithm framework is employed, which consists of the sensor fusion algorithm and the kinematic constraints-based pose calibration algorithm. Experimental results demonstrate the system’s effectiveness in motion sensing accuracy, correlation, and agreement in comparison with the gold standard. The validated applications of the system lie in motion monitoring and estimation, and human-robot teleoperation, showing the potential for enhancing HMDT.

To date, numerous tactile sensors and algorithms have been developed to tackle various perception problems. However, excessive endeavors are devoted to the multifarious definition, extraction, and analysis of hand-crafted features in order to improve the perception accuracy. To address this problem, in this paper, we designed a tactile finger containing four sensing elements to perceive both dynamic and static stimuli, and meanwhile proposed a novel signal processing pipeline. The pipeline mainly consisted of time-series signals conversion, automatic deep features extractor, and a shallow recognition model. When the tactile finger was applied to explore 16 surfaces based on a robotic platform, the four-channel signals were converted and concatenated into a time-frequency image via continuous wavelet transform. A deep feature extraction network was constructed based on a pretrained deep learning (DL) model, Resnet101, to extract the required features, which acted as high-level representations of the most discriminative components from the tactile images. Finally, these features were fed into a shallow machine learning model, i.e., extreme learning machine, achieving an accuracy as high as 92.38%. In such a manner, the powerful learning capability of DL models was transferred to the new recognition model directly while the tedious feature extraction procedures were alleviated. Besides, several relevant issues, such as the layer depth, the DL model type, and the shallow recognition model, are addressed and discussed to reveal their influences on the performance.

With the increasing demand in seamless interface between artificial devices and biological structures, flexible bioelectronics has been developed rapidly in recent years. Compared with traditional rigid bioelectronics, flexible devices are more adaptable to the integration for various parts both inside and outside of the organism. Significant achievements have been made in biomedical devices, neuroelectronics and wearable devices. With the development of flexible bioelectronics, electromagnetics is becoming a crucial part in signal interference reduction and information transmission or feedback, taking advantages of strong penetration and rapid response in a variety of biological materials. In this review, we focus on the latest developments in electromagnetic based flexible bioelectronics, involving materials, sensation, seamless integration, and power supply, as well as the latest achievements in the fields of external wearables, internal implants, soft robotics and drug delivery system. Based on these, the main challenges facing flexible bioelectronics, are analyzed, including stretchability caused by mismatch between mechanical properties of soft and hard components, biocompatibility, environmental stability, to facilitate the further development of flexible bioelectronics.

As a preliminary study for bearingless permanent magnet slice motor (BPMSM) development, an effective means for BPMSM mechanical structure optimization is proposed here by developing a virtual prototype based on Ansoft Maxwell to realize overall performance improvements. First, the sensitivity evaluation index of the candidate mechanical structural parameters for individual BPMSM performance is constructed for selection. Orthogonal tests are performed to determine the dominant mechanical structural parameters to be optimized by utilizing monitored data based on Ansoft Maxwell. A linear regression model of the mechanical structural parameters for specific performances is obtained by utilizing the gradient descent method. Then, a multi-structural optimization regression model of the selected dominant mechanical structural parameters for overall performance is established using an analytic hierarchy process and solved using a genetic algorithm. The simulation results show that the performance of the optimized BPMSM has been comprehensively improved. Specifically, the passive axial stiffness, passive tilting stiffness, force-current coefficient, and motor efficiency increased by 56.4%, 71.3%, 19.6%, and 8.7%, respectively.