Fabrication of advanced triboelectric nanogenerators based on freestanding few-layer β-rhombohedral borophene sheets for posture recognition

Self-powered pumping switched TENG enabled real-time wireless metal tin height and position recognition and counting for production line management

With increasing work pressure in modern society, prolonged sedentary positions with poor sitting postures can cause physical and psychological problems, including obesity, muscular disorders, and myopia. In this paper, we present a self-powered sitting position monitoring vest (SPMV) based on triboelectric nanogenerators (TENGs) to achieve accurate real-time posture recognition through an integrated machine learning algorithm. The SPMV achieves high sensitivity (0.16 mV/Pa), favorable stretchability (10%), good stability (12,000 cycles), and machine washability (10 h) by employing knitted double threads interlaced with conductive fiber and nylon yarn. Utilizing a knitted structure and sensor arrays that are stitched into different parts of the clothing, the SPMV offers a non-invasive method of recognizing different sitting postures, providing feedback, and warning users while enhancing long-term wearing comfortability. It achieves a posture recognition accuracy of 96.6% using the random forest classifier, which is higher than the logistic regression (95.5%) and decision tree (94.3%) classifiers. The TENG-based SPMV offers a reliable solution in the healthcare system for non-invasive and long-term monitoring, promoting the development of triboelectric-based wearable electronics.

Aiming at the low accuracy of posture recognition and power supply of wearable devices, a human posture recognition algorithm based on time series shapelets and long short-term memory network (LSTM) ???? proposed. In this algorithm, triboelectric nanogenerator (TENG) is used as a self-driven sensor to collect data of human knees and feet, and shapelets time series feature extraction based on dynamic time warping (DTW) distance is used, and the feature value is used as the input information of bi-directional long short-term memory network. Finally, the data processed by bidirectional network is classified by soft-max function to obtain the recognition of human daily actions. Feature extraction based on shapelets time series has good performance. At the same time, LSTM is skilled at dealing with time series problems, so both are integrated. Experimental results show that, compared with other gesture recognition algorithms, the recognition accuracy of this algorithm is as high as 97.3%, and it can more effectively recognize everyday human activities.

The disposal of cigarette product waste has become an urgent environmental issue. Triboelectric nanogenerators (TENGs) bring the possibility of resource utilization of discarded cigarette packages due to their ability to use various materials for sensing and energy harvesting. However, the TENGs based on waste materials reported have problems such as low utilization rate of waste and high manufacturing costs. To address these issues, a fully discarded cigarette pack‐based flexible triboelectric sensor is proposed. The material is entirely sourced from the main waste of cigarette packs and the preparation process is simple, making it both environmentally friendly and economically advantageous. The sensor exhibits a wide detection range and good sensitivity to the change of force. In addition, it performs well in applications such as human motion monitoring, sitting posture recognition, and human‐computer interaction. Specifically, it can effectively perceive joint movements and changes in plantar pressure resulting from various actions; combined with a linear support vector machine, it achieves accurate recognition of four common sitting postures with an accuracy of 99.5%; and it has been integrated into a wearable remote control system, enabling remote interaction with rescue vehicles. This study provides a new solution for the high‐value utilization of discarded cigarette packages.

Sedentary, inadequate sleep and exercise can affect human health. Artificial intelligence (AI) and Internet of Things (IoT) create the Artificial Intelligence of Things (AIoT), providing the possibility to solve these problems. This paper presents a novel approach to monitor various human behaviors for AIoT‐based health management using triboelectric nanogenerator (TENG) sensors. The insole with solely one TENG sensor, creating a most simplified system that utilizes machine learning (ML) for personalized motion monitoring, encompassing identity recognition and gait classification. A cushion with 12 TENG sensors achieves real‐time identity and sitting posture recognition with accuracy rates of 98.86% and 98.40%, respectively, effectively correcting sedentary behavior. Similarly, a smart pillow, equipped with 15 sensory channels, detects head movements during sleep, identifying 8 sleep patterns with 96.25% accuracy. Ultimately, constructing an AIoT‐based health management system to analyze these data, displaying health status through human‐machine interfaces, offers the potential to help individuals maintain good health.image

With the rapid advancement of humanoid robot technology, precise evaluation of its comprehensive performance is crucial for ensuring stable operation in applications. Here, a multimodal humanoid robot performance evaluation system based on a single‐channel tactile‐sliding triboelectric nanogenerator (SCTS‐TENG) is proposed. SCTS‐TENG enables 1D tactile position recognition using the skewness (SK) of the single‐channel signal, pressure perception mimicking human skin during sliding, and direction identification. Inspired by the process of ancient silk trade, a performance testing system for humanoid robots is constructed using the SCTS‐TENG and a camera. Testing specifications for rotational contact of the dexterous hand and contact‐sliding‐separation of the robotic arm are defined, and an in‐depth analysis is conducted on output signal characteristics under different influencing variables. The SCTS‐TENG signals are converted into 2D images using the gramian angular difference field (GADF) method, achieving 97.22% accuracy when used as a standalone modality. This is further fused with camera images, which achieved 75% accuracy as a standalone modality, to develop a multimodal deep learning (DL) system based on the Visual Geometry Group (VGG)19 model. This fusion improves the recognition accuracy to 99.03%. This system provides a low‐cost, self‐powered, and high‐precision solution for sensing and evaluation of humanoid robots.

A fully stretchable textile-based triboelectric nanogenerator for human motion monitoring

Recently, owing to the development of artificial intelligence technology, human posture recognition has aroused great interest in the academic community. Thus, we designed a triboelectric nanogenerator based on PDMS layer and MXene/PDMS layer (PM-TENG) to obtain mechanical energy and sense human posture. According to the results, the open-circuit voltage ([Formula: see text] of PM-TENG can arrive at 372 V, and the short-circuit current ([Formula: see text] of PM-TENG can reach 16.21 [Formula: see text]A, respectively. Due to its highly sensitive sensor to complex human motor states like folding, stretching, squeezing, and tapping, it can not only be used to harvest mechanical energy from its surroundings, but also to monitor human movement and behavior. Thus, human motion behaviors like walking, leg lifting, and light and high jumps may be tracked and identified by reading pulse electrical signal production. This research will provide a new idea for human motion posture monitoring.