Huaxu Zhou

Human physiological signals play a crucial role in the fields of health assessment, sports medicine, and human–computer interaction. However, human vibration signals exhibit a wide frequency range and significant amplitude variations, which impose greater demands on the sensors’ detection range and sensitivity. A Bionic Crackle Vibration Sensor holding a wide frequency range and high sensitivity, based on the highly sensitive vibration sensing mechanism of the slit sensilla in arthropod legs is designed. The sensor demonstrates exceptionally high sensitivity (GF =42755) and frequency resolution (0.1 Hz or $1\times 10^{-4}$ ) over a wide frequency range (0.1–10000 Hz). Our sensor can accurately monitor vibration signals within ground provoked by human walking and jumping, as well as the pulse and vocal cord vibrations. Gait monitoring systems that integrate our sensors with insoles are proved to be capable of monitoring walking speed. Our Bionic Crackle Vibration Sensor provides innovative technological solutions for wearable medical electronics and human–computer interaction interfaces, demonstrating significant application potential in health monitoring, sports rehabilitation assessment, and early warnings for sub-health conditions.

Human body movements are flexible and multidirectional. To obtain more accurate parameters during human motion monitoring and health managing, ultra-sensitive and omnidirectional sensing equipment is needed. Therefore, an ultra-sensitive omnidirectional strain sensor array, which utilizes a bionic spider crack sensing mechanism, is proposed and designed to detect the multidirectional mechanical strains over the skin generated by human activities. Holding the mechanism of the bionic crack sensing layer as foundation, the bionic crack strain sensor exhibits an ultrahigh strain sensitivity of GF =1920031. The proposed omnidirectional strain sensor array consists of three bionic crack strain sensors spaced 120° apart. This configuration enables the array to acquire and distinguish mechanical strains from any direction within its coverage skin area. The sensor array can be attached to the skin firmly to measure skin deformation caused by joint and muscles’ activity, as well as the angle of the wrist movements. The monitoring data obtained by the fabricated sensor array was used to train and test a badminton swing action recognition model, and eight types of badminton swing actions were successfully identified with an overall accuracy of 98.125%. This demonstrates that the omnidirectional strain sensor array can provide high-precision human motion posture information for intelligent health management and medical limb monitoring.

Flexible pressure sensors have gained significant attention due to their widespread applications in health monitoring, electronic skin, and human-machine interaction. To meet the diverse pressure detection needs of different body parts, flexible pressure sensors must exhibit high sensitivity, low detection limits, and a wide detection range. Inspired by the slit sensilla in spider legs, this study designed and fabricated a novel flexible pressure sensor by integrating a biomimetic metal cracksensitive structure with a polyimide (PI) flexible substrate. By optimizing the crack-sensitive structure and substrate materials, we significantly enhanced the sensor's sensitivity and detection range. The sensor demonstrated an extremely high sensitivity (up to 56715 kPa^-1), a wide measurement range (36 Pa to 192 kPa), rapid response time (15 ms), low hysteresis (8%), excellent durability and repeatability (140 kPa, 1100 cycles). Experimental results indicate that the sensor could accurately monitor physiological signals such as pulse, respiration, and joint movements. Furthermore, the single biomimetic crack pressure sensor has been extended into a 3×3 sensor array, enabling spatial pressure mapping and recognition. This study not only theoretically demonstrates that optimized biomimetic crack designs and high-modulus materials can achieve high sensitivity and wide range in flexible pressure sensors, but also experimentally verifies the efficiency and practicality of the fabricated sensors in human health monitoring applications. Biomimetic crack flexible pressure sensor is expected to be widely used in physiological monitoring, human-machine interaction, and intelligent robotics in the future.

Aiming for high sensitivity and satisfying transmission loss, intelligent optimization methods involving machine learning are worthwhile to apply for optical pressure sensors development since machine learning is versatile to correlate the complex and nonlinear relationship between device performance and sensor structural parameters. This paper proposes a three-stage framework to conduct optimal design of an optical pressure sensor facing multiple optimization objectives. Firstly, a dataset consisting of 1600 samples with combinations of varying waveguide lengths and core widths is used to train and establish a prediction model, which also acts as the objective functions for subsequent optimization. Secondly, a genetic algorithm is applied to find out the Pareto front within the sample space according to optimization objectives. Thirdly, by inverse design based on the Pareto optimal set, the multi-objective optimization for sensor with optimal structural parameters is achieved. Experimental studies show that the optimized design of the graphene waveguide composite structure pressure sensor based on machine learning gives a sensitivity increase of 63.7% and a transmission loss reduction of 18.5% compared to the similar sensors designed in previous work. This effectively verifies the feasibility of the proposed optimization design method in this paper. Without large number of sensor fabrications and trial-and-error experiments, the reverse design of sensors with various performance requirements could be achieved automatically, which provides a new approach to improve the performance of sensors in the future.