Material-Structure Codesign in Triboelectric Sensors: A Body-Region-Specific Roadmap for Human Motion Monitoring and Healthcare.

The combination of nanogenerator technology and traditional textile materials has given rise to textile-based triboelectric nanogenerators (T-TENGs) structured from fibers, yarns, and fabrics. Due to their lightweight, flexibility, washability, and cost-effectiveness, T-TENGs offer a promising platform for powering and sensing in next-generation wearable electronics, with particularly significant potential in smart healthcare and sports monitoring. However, the inherent electrical and structural limitations of textile materials often restrict their power output, signal stability, and sensing range, making it challenging to achieve both high electrical performance and high sensing sensitivity. This review focuses on the application of T-TENGs in smart healthcare and sports. It systematically presents recent developments in textile material selection, sensing structure, fabric design, working mechanisms, accuracy optimization, and practical application scenarios. Furthermore, it provides a critical analysis of the recurring structural and material limitations that constrain performance and offers constructive pathways to address them. Key challenges such as the low charge density of textile interfaces may be mitigated by selecting low-hygroscopicity materials, applying hydrophobic treatments, and optimizing textile structures to enhance contact efficiency and environmental stability. Issues of signal instability under dynamic deformation call for advanced structural designs that accommodate strain without compromising electrical pathways, coupled with robust signal processing algorithms. By providing a comparative analysis across materials and structures, this review aims to inform future designs and accelerate the translation of high-performance T-TENGs from laboratory research to real-world implementation.

Self‐powered pressure sensors are gaining popularity in human–machine interaction and mobile systems for their energy efficiency. Among the many types of self‐powered sensors, triboelectric sensors have numerous advantages, including diversity of materials, ease of fabrication, and high voltage output. However, their signal is prone to be affected by both intrinsic and extrinsic factors including environmental change and discharging, which can significantly deteriorate the accuracy of measurement. To address this, a simple yet effective solution is proposed: a mechanically induced spike‐based self‐calibration method for a triboelectric pressure sensor. The sensor generates two signals: an open‐circuit voltage and a spiking calibration voltage, enabling real‐time calculation of current surface charge density. The calibration signal generates a spike at each predetermined discrete pressure change, whether positive or negative direction, denoting the corresponding direction of the pressure variation. This system successfully calibrates signals from various effects, including humidity change (20%–80%), discharging (over 10 days), and charge accumulation. This sensor has potential applications in precision agriculture for efficient crop harvesting and packaging in diverse environmental conditions.

A flexible dual-mode triboelectric sensor for strain and tactile sensing toward human-machine interface applications

Wearable human-machine interface (HMI) with bidirectional and multimodal tactile information exchange is of paramount importance in teleoperation by providing more intuitive data interpretation and delivery of tactilely related signals. However, the current sensing and feedback devices still lack enough integration and modalities. Here, we present a Tactile Sensing and Rendering Patch (TSRP) that is made of a customized expandable array which consists of a piezoelectric sensing and feedback unit fused with an elastomeric triboelectric multidimensional sensor and its inner pneumatic feedback structure. The primary functional unit of TSRP is mainly featured with a soft silicone substrate with compact multilayer structure integrating static and dynamic multidimensional tactile sensing capabilities, which synergistically leverage both triboelectric and piezoelectric effects. Additionally, based on the air chamber created by the triboelectric sensor and the converse piezoelectric effect, it provides pneumatic and vibrational haptic feedback simultaneously for both static and dynamic perception regeneration. With the aid of the other variants of this unit, the array shaped TSRP is capable of simulating different terrains, geometries, sliding, collisions, and other critical interactive events during teleoperation via skin perception. Moreover, immediate manipulation can be done on TSRP through the tactile sensors. The preliminary demonstration of TSRP interface with a completed control module in robotic teleoperation is provided, which shows the feasibility of assisting certain tasks in a complex environment by direct tactile communication. The proposed device offers a potential method of enabling bidirectional tactile communication with enriched key information for improving interaction efficiency in the fields of robot teleoperation and training.

Flexible tactile sensors play an important role in the development of wearable electronics and human-machine interaction (HMI) systems. However, poor sensing abilities, an indispensable external energy supply, and limited material selection have significantly constrained their advancement. Herein, a self-powered flexible triboelectric sensor (TES) is proposed by integrating lotus-root-derived porous carbon (PC) into polydimethylsiloxane (PDMS). Owing to the superior charge capturing capability of PC, the PDMS/PC (PPC)-based TES exhibits an open-circuit voltage (Voc) of 22.8 V when it is periodically patted by skin at the pressure of 2 N and the frequency of 1 Hz, which is 5 times higher than that of a pristine PDMS-based TES. Furthermore, the as-prepared self-powered TES exhibits a high sensitivity of 3.24 V kPa-1 below 15 kPa for detecting human motion signals, such as finger clicks, joint bends, etc. Last but not the least, after the assembly of a PPC-based TES array and construction of an HMI system, the robotic snake can be controlled remotely by recognizing finger touching signals. This work shows broad potential applications for the self-powered TES in the fields of intelligent robotics, flexible electronics, disaster relief, and intelligence spying.

Efficient monitoring and recognition of movement are crucial in enhancing athletic performance. Traditional methods have limitations in terms of high site requirements and power consumption, making them unsuitable for long-term tracking and monitoring. A potential solution to low-power monitoring of body area networks is triboelectric sensors. However, the current analysis method for badminton triboelectric sensing data is relatively simple, while flexible, triboelectric sensors based on 3D printing face issues such as discomfort when joints are bent or twisted in a large range. In light of this, a flexible arch-shaped triboelectric sensor based on 3D printing (FA-Sensor) is proposed. By combining neural network algorithms with the signal acquisition module and the master computer, an intelligent multi-sensor node system for badminton monitoring is established. The FA-Sensor exhibits high sensitivity to bending and twisting motions due to its elastic TPE shell and arched shape design. It minimizes interference with human motion during bending (10°–150°) or twisting (20°–100°) over a wide range. The peak output voltage of the FA-Sensor demonstrates a clear functional relationship with the bending angle, exhibiting piecewise sensitivities of 7.98 and 29.28 mV/°, respectively. For seven different parts of the human body, it can be quickly customized to different sizes, with stable and repeatable response outputs. In application, the badminton sports monitoring system enables real-time feedback and recognition of four typical technical movements, achieving a recognition accuracy rate of 97.2%. The system enables athletes to analyze and enhance badminton technology while also exhibiting promising potential for application in other intelligent sports domains.

High-performance flexible self-powered triboelectric pressure sensor based on chemically modified micropatterned PDMS film

In view of the problems that robotic arms find it difficult to effectively identify and grasp workpieces with different textures and hardness in complex industrial environments, and the low path planning accuracy of robotic arms in practical application scenarios, this paper proposes a composite sensor based on piezoresistive effect and triboelectric effect. The piezoresistive sensor and the triboelectric sensor simultaneously generate output signals, and the identification of different achieves high-precision dynamic monitoring of the robotic arm's gripping objects and joint movements. The base material of the piezoresistive sensor and the negative electrode material of the triboelectric sensor are both porous MXene/PDMS structures. The piezoresistive sensor enhances its conductivity by spin-coating CNT slurry on the surface of the base material and uses PVA hydrogel as the electrode. The triboelectric sensor uses copper as the positive electrode material. Experiments show that the developed sensor has a measurement range (0.015 kPa-70 kPa) and good repeatability. The experiment verifies that the composite sensor can be applied to the high-precision detection of robotic arm's flexible gripping and joint movements.

Healthcare is undergoing a rapid transformation from traditional hospital and specialist focused approach to a distributed patient-centric approach. Advances in several technologies fuel this rapid transformation of healthcare vertical. Among various technologies, communication technologies have enabled to deliver personalized and remote healthcare services. At present, healthcare widely uses the existing 4G network and other communication technologies for smart healthcare applications and are continually evolving to accommodate the needs of future intelligent healthcare applications. As the smart healthcare market expands the number of applications connecting to the network will generate data that will vary in size and formats. This will place complex demands on the network in terms of bandwidth, data rate, and latency, among other factors. As this smart healthcare market matures, the connectivity needs for a large number of devices and machines with sensor-based applications in hospitals will necessitate the need to implement Massive-Machine Type Communication. Further use cases such as remote surgeries and Tactile Internet will spur the need for Ultra Reliability and Low Latency Communications or Critical Machine Type Communication. The existing communication technologies are unable to fulfill the complex and dynamic need that is put on the communication networks by the diverse smart healthcare applications. Therefore, the emerging 5G network is expected to support smart healthcare applications, which can fulfill most of the requirements such as ultra-low latency, high bandwidth, ultra-high reliability, high density, and high energy efficiency. The future smart healthcare networks are expected to be a combination of the 5G and IoT devices which are expected to increase cellular coverage, network performance and address security-related concerns. This paper provides a state-of-the-art review of the 5G and IoT enabled smart healthcare, Taxonomy, research trends, challenges, and future research directions.

Longitudinal variations in maximal oxygen intake with age and activity.

Diet, muscle glycogen, and endurance performance.

In this work, amido‐graphene oxide (GO‐NH 2 ) loaded chitosan (CTS) composite material (CTS/GO‐NH 2 ) that acts as both the triboelectric and sensing film was prepared on rotary fan‐shaped triboelectric nanogenerator for humidity detection. Compared with the pristine CTS‐based triboelectric humidity sensor (CTS‐THS) and GO‐NH 2 ‐THS, the CTS/GO‐NH 2 ‐based humidity sensor exhibited higher humidity response and better linearity in the relative humidity (RH) range of 18.7%RH–91.5%RH. The above results can be explained by the massive exposed and less concealed hydrophilic functional groups of CTS with the help of the wrinkle structure of GO‐NH 2 . Meanwhile, the CTS/GO‐NH 2 ‐THS possessed good repeatability and acceptable hysteresis (~ 6.2%RH). Finally, a humidity sensing mechanism coupling triboelectric contact charging effect with electrons transfer principle under moisture environment was established to interpret the enhanced humidity sensing performance of the composite film‐based THS. This work demonstrates that CTS/GO‐NH 2 composite film can be utilized to fabricate humidity sensors based on the triboelectric effect.

Oxygen uptake during maximal treadmill and bicycle exercise.

Mechanoreceptor-inspired in-ear triboelectric sensor for unconstrained physiological monitoring and human–machine interaction

Upper extremity cumulative trauma distorders (UECTD) have been identified as an occupational health problem in professional Sign Language Interpreters (SLI). A previous study of UECTD in SLI has indicated significant differences between interpreters working with pain and those working without pain. This earlier research focused on gross measures of hand/wrist movement, work/rest cycles, and deviations from an optimal work envelope. The present paper describes a detailed biomechanical analysis of wrist and forearm activity associated with SLI. This assessment included forearm (flexion and extension) and wrist (flexion/extension and radial/ulnar deviation) measures of movement frequency, counts of individual motion, joint movement velocities and accelerations as well as range of motion. The analyses revealed that the postures required for interpreting result in the signing hand frequently held in a fully pronated position, with the palm facing out. The wrist was most frequently in an ulnar deviation and/or extension while the elbow was flexed more than 90° and held in close to the body with the fingers pointing up. The frequency of motions for the forearm and wrist were observed to be 270 per minute (4.5 Hz), which is equivalent to 13,600 per 50 minute lecture hour. The mean absolute joint movement velocity and acceleration values were relatively high in contrast to industrial jobs with wrist and forearm accelerations between 34,754 degreees/sec(2) and 36,046 degrees/sec(2), respectively. The findings from this biomechanical analysis indicates that SLI can involve highly repetitive, awkward movements with significant accelerations of the hand and wrist. Such job characteristics may predispose interpreters to upper extremity CTD-related disorders.