Silver Nanowire-Based Flexible Strain Sensor for Human Motion Detection.

Accurately capturing human movements is a crucial element of health status monitoring and a necessary precondition for realizing future virtual reality/augmented reality applications. Flexible motion sensors with exceptional sensitivity are capable of detecting physical activities by converting them into resistance fluctuations. Silver nanowires (AgNWs) have become a preferred choice for the development of various types of sensors due to their outstanding electrical conductivity, transparency, and flexibility within polymer composites. Herein, we present the design and fabrication of a flexible strain sensor based on silver nanowires. Suitable substrate materials were selected, and the sensor's sensitivity and fatigue properties were characterized and tested, with the sensor maintaining reliability after 5000 deformation cycles. Different sensors were prepared by controlling the concentration of silver nanowires to achieve the collection of motion signals from various parts of the human body. Additionally, we explored potential applications of these sensors in fields such as health monitoring and virtual reality. In summary, this work integrated the acquisition of different human motion signals, demonstrating great potential for future multifunctional wearable electronic devices.

Noncontact liquid-solid nanogenerators as self-powered droplet sensors.

Liquid-solid triboelectric nanogenerators (L-S TENGs) can generate corresponding electrical signal responses through the contact separation of droplets and dielectrics and have a wide range of applications in energy harvesting and self-powered sensing. However, the contact between the droplet and the electret will cause the contact L-S TENG's performance degradation or even failure. Here we report a noncontact triboelectric nanogenerator (NCLS-TENG) that can effectively sense droplet stimuli without contact with droplets and convert them into electrical energy or corresponding electrical signals. Since there is no contact between the droplet and the dielectric, it can continuously and stably generate a signal output. To verify the feasibility of NCLS-TENG, we demonstrate the modified murphy's dropper as a smart infusion monitoring system. The smart infusion monitoring system can effectively identify information such as the type, concentration, and frequency of droplets. NCLS-TENG show great potential in smart medical, smart wearable and other fields.

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG.

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF achieved an improved average prediction accuracy of 96.60%, outperforming seven benchmark models. Even with an extended 500ms prediction horizon, the accuracy only marginally decreased to 93.22%. The averaged stable prediction times for detecting next upcoming transitions spanned from 31.47 to 371.58 ms across the 100-500 ms time advances. Although the prediction accuracy of the trained Deep-STF initially dropped to 71.12% when tested on four new terrains, it achieved a satisfactory accuracy of 92.51% after fine-tuning with just 5 trials and further improved to 96.27% with 15 calibration trials. These results demonstrate the remarkable prediction ability and adaptability of Deep-STF, showing great potential for integration with walking-assistive devices and leading to smoother, more intuitive user interactions.