Flexible wearable intelligent sensing system for wheelchair sports monitoring

Wearable flexible sensors based on dual-network ionic hydrogels with xanthan gum/sodium alginate/polyacrylamide/gallium indium alloy

With the rapid development of wearable electronic devices and smart sensors, flexible sensors have received much attention due to their excellent mechanical properties and good adaptability. However, developing a simple method to produce conductive hydrogels with excellent electrical conductivity, mechanical properties, environmental stability, and durability is still a major challenge. In this study, a novel dual-network composite flexible sensor was developed, which was mainly composed of xanthan gum (XG), sodium alginate (SA), polyacrylamide (PAAm), and gallium‑indium alloy (Ga-In). The sensor combined the good biocompatibility and thickening properties of natural polysaccharides, the flexibility of polymers, and the excellent electrical conductivity of conductive metal alloys. The sensors exhibited good mechanical properties (stress ≈ 400 KPa, strain ≈ 540 %), high fatigue resistance, recoverability and excellent environmental adaptability. In addition, the addition of liquid metal could increase the conductivity (1.83 S m-1) of the hydrogel while maintaining high transparency, and the flexible sensor device constructed from it had high sensitivity to strain (GF = 2.75). Therefore, the hydrogel as a flexible sensor showed promising applications in detecting human movement, which could monitor the movement of human joints, micro-expressions, and handwriting. This will provide new ideas for scientific management of sports and health monitoring.

AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices

Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco-friendly power generation from mechanical motion. They harness mechanical energy while enabling self-sustaining sensing for self-powered devices. However, challenges such as material optimization, fabrication techniques, design strategies, and output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, and adaptive responses, is revolutionizing fields like healthcare, industrial automation, and smart infrastructure. When integrated with TENGs, AI can overcome current limitations by enhancing output, stability, and adaptability. This review explores the synergistic potential of AI-driven TENG systems, from optimizing materials and fabrication to embedding machine learning and deep learning algorithms for intelligent real-time sensing. These advancements enable improved energy harvesting, predictive maintenance, and dynamic performance optimization, making TENGs more practical across industries. The review also identifies key challenges and future research directions, including the development of low-power AI algorithms, sustainable materials, hybrid energy systems, and robust security protocols for AI-enhanced TENG solutions.

Wireless Sensing System Based on Biodegradable Triboelectric Nanogenerator for Evaluating Sports and Sleep Respiratory

The rapid growth of the Internet of Things and wearable sensors has led to advancements in monitoring technology in the field of health. One such advancement is the development of wearable respiratory sensors, which offer a new approach to real-time respiratory monitoring compared to traditional methods. However, the energy consumption of these sensors raises concerns about environmental pollution. To address the issue, this study proposes the use of a triboelectric nanogenerator (TENG) as a sustainable energy source. The electrical conductivity of the TENG is improved by incorporating chitosan and carbon nanotubes, with the added benefit of chitosan's biodegradability reducing negative environmental impact. A wireless intelligent respiratory monitoring system (WIRMS) is then introduced, which utilizes a degradable triboelectric nanogenerator for real-time respiratory monitoring, diagnosis, and prevention of obstructive respiratory diseases. WIRMS offers stable and highly accurate respiratory information monitoring, while enabling real-time and nondestructive transmission of information. In addition, machine learning technology is used for sleep respiration state analysis. The potential applications of WIRMS extend to wearables, medical monitoring and sports monitoring, thereby presenting innovative ideas for modern medical and sports monitoring.

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion

In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.

Research on the Sensing Characteristics of an Integrated Grid-like Sensor Based on a Triboelectric Nanogenerator

With the development of the integration and miniaturization of sensing devices, the concept of self-sensing devices has been proposed. A motion state is self-sensed via the structure or integration of an actuator in the construction of a sensing unit. This device is then used to capture the perception and measurement of states such as position, displacement, and speed. A triboelectric nanogenerator converts mechanical energy into electrical energy through the coupling effect of contact generation and electrostatic induction, which represents one of the reliable ways through which to realize integrated sensing. In this world, the power generation technology of the TENG is applied to a sensing device. The sensing characteristics of a grid-like TENG are designed and analyzed in freestanding triboelectric mode. Firstly, a relation model of displacement, velocity, voltage, and charge is established. The charge-transfer increment and current amounts are linearly related to the velocity. The open-circuit voltage has a positive relationship with the displacement. The maximum open-circuit voltage and the maximum charge transfer are fixed values, and they are only related to the inherent parameters of a triboelectric nanogenerator. Next, the sensor model is constructed using COMSOL Multiphysics 6.0. The simulation results show that the relationships between output voltage and charge transfer, as well as those between the increments of charge transfer, velocity, and displacement, are consistent with the results derived from the formula. Finally, a performance test of the designed sensor is carried out, and the results are consistent with the theoretical deduction and simulation. After analysis and processing of the output electrical signal by the host computer, it can feedback the frequency and speed value of the measured object. In addition, the output signal is stable, and there is no large fluctuation or attenuation during the 521-s vibration test. Because the working unit of the sensor is thin filmed, it is small in size, easy to integrate, and has no external power supply; moreover, it can be integrated into a device to realize the self-sensing of a motion state.