Design of Micro-Bluetooth Motion Acquisition System

To realize the digitization of infant motion information and explore more abundant human health information with motion information to facilitate early treatment, a micro-Bluetooth motion acquisition system is designed. The low-power design of the micro-Bluetooth motion capture sensor in the system and intelligent algorithm for optimizing the precision of the infant movement measured angles can realize the system’s sustainable use and data reliability. With the help of collecting and analyzing human arm, leg, and head movement information, we can recognize that the system can carry out more research and experiments on natural infant movements

Classification and Analysis of Human Body Movement Characteristics Associated with Acrophobia Induced by Virtual Reality Scenes of Heights

Acrophobia (fear of heights), a prevalent psychological disorder, elicits profound fear and evokes a range of adverse physiological responses in individuals when exposed to heights, which will lead to a very dangerous state for people in actual heights. In this paper, we explore the behavioral influences in terms of movements in people confronted with virtual reality scenes of extreme heights and develop an acrophobia classification model based on human movement characteristics. To this end, we used wireless miniaturized inertial navigation sensors (WMINS) network to obtain the information of limb movements in the virtual environment. Based on these data, we constructed a series of data feature processing processes, proposed a system model for the classification of acrophobia and non-acrophobia based on human motion feature analysis, and realized the classification recognition of acrophobia and non-acrophobia through the designed integrated learning model. The final accuracy of acrophobia dichotomous classification based on limb motion information reached 94.64%, which has higher accuracy and efficiency compared with other existing research models. Overall, our study demonstrates a strong correlation between people’s mental state during fear of heights and their limb movements at that time

Conformal, stretchable, breathable, wireless epidermal surface electromyography sensor system for hand gesture recognition and rehabilitation of stroke hand function

Surface electromyography (sEMG) plays a significant role in the everyday practice of clinic hand function rehabilitation. The materials and design of current typical clinic sEMG electrodes are rigid Ag/AgCl or flexible polyimide (PI) film, which cannot provide a stable interface between electrodes and skin for adequate long-term high-quality data. Thus, conformal, soft, breathable, wireless epidermal sEMG sensor systems have broad potential relevance to clinic rehabilitation settings. Herein, we demonstrate a stretchable epidermal sEMG sensor array system with optimized materials and structure strategies for hand gesture recognition and hand function rehabilitation. The optimized serpentine structures with marvelous stretchability and improved fill ratio, provide lower impedance and high-quality sEMG signals. Moreover, the easy-to-use airhole method further ensures stable and long-term contact with the skin for recording. In addition, integrated with a customized flexible wireless data acquisition system, the capability for real-time 8-channel sEMG monitoring is developed, and taking together with the CNN-based algorithm, the system can automatically and reliably realize the 7 kinds of hand gestures with an accuracy of 81.02%. Moreover, the low-cost yet high-performance epidermal sEMG sensor system demonstrated its conceptual feasibility in quantitatively evaluation of stroke patient’s hand and facilitating human-robot collaboration in hand rehabilitation by proof-of-the-concept clinical testing