Tiantong Wang

Peking University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Qining Wang

Peking University

Yunbiao Zhao

State Key Laboratory of Nuclear Physics and Technology

Zilu Wang

Jiangsu University

Dongjie Jiang

University of Chinese Academy of Sciences

Ziqiang Zhao

Peking University

Rongli Wang

Peking University First Hospital

Ming Xu

University of Hong Kong

Nuo Xu

Peking University

Jingeng Mai

Peking University

Xiaoting Yuan

Peking University

Cooperative Institutions

Peking University

Chinese Academy of Sciences

Beijing Institute of Nanoenergy and Nanosystems

University of Chinese Academy of Sciences

China Rehabilitation Research Center

Beijing Academy of Artificial Intelligence

State Key Laboratory of Nuclear Physics and Technology

Wuhan University

Key Laboratory of Nuclear Radiation and Nuclear Energy Technology

Qingdao University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

A Dual‐Mode, Scalable, Machine‐Learning‐Enhanced Wearable Sensing System for Synergetic Muscular Activity Monitoring

Advanced Materials Technologies (2024)

Tiantong Wang Dongjie Jiang Yuwen Lu Nuo Xu Zilu Wang

Abstract Simultaneously detecting muscular deformation and biopotential signals provides comprehensive insights of the muscle activity. However, the substantial size and weight of detecting equipment result in reduced wearer benefits and comfort. It remains a challenge to establish a flexible and lightweight wearable system for mapping muscular morphological parameters while collecting biopotentials. Herein, a fully integrated dual‐mode wearable system for monitoring lower‐extremity muscular activity is introduced. The system utilizes an iontronic pressure sensing matrix (16 channels) for precise mapping of force myography (FMG) within a single muscle, while simultaneously capturing the muscular electrophysiological signals using a self‐customized electromyography (EMG) sensing module. Experimental results show that the bimodal sensing system is capable of capturing complementary and comprehensive aspects of muscular activity, which reflect activation and architectural changes of the muscle. By leveraging machine learning techniques, the integrated system significantly ( p < 0.05) enhances the average gait phase recognition accuracy to 96.35%, and reduces the average ankle joint angle estimation error to 1.44°. This work establishes a foundation for lightweight and bimodal muscular sensing front‐ends, which is promising in applications of human–machine interfaces and wearable robotics.

10.1002/admt.202400857

Cite

Citations (0)

Three-Dimensional Iontronic Force Sensor for Robotic Force Feedback

Lecture notes in computer science (2025)

Nuo Xu Tiantong Wang Dongjie Jiang Yunbiao Zhao

Force transducer

10.1007/978-981-96-0786-0_18

Cite

Citations (0)

Track-Etch Membranes as Tools for Template Synthesis of Highly Sensitive Pressure Sensors

ACS Applied Materials & Interfaces (2021)

Yunbiao Zhao Tiantong Wang Ziqiang Zhao Qining Wang

Flexible pressure sensors with high sensitivity are highly desired in wearable electronics and human-machine interaction. Introducing the surface microstructures to the capacitive-type sensors can improve sensitivity and reduce response time. However, conventional techniques for the fabrication of highly sensitive and large-area pressure sensors still remain challenging. Here, a template synthesis approach is reported for fabrication of a large-area and low-cost ionic micropillar array templated from track-etch membranes. The pressure sensors based on the ionic micropillars gel dielectric layers exhibit a low limit of detection (∼0.5 Pa) and high sensitivity (14.83 kPa-1) in the low-pressure regime (0-5 kPa) and linear sensitivity (1.96 kPa-1) over a wide pressure range of 24-230 kPa. The versatility of the sensors is demonstrated in various human physiological signal detection scenarios and spatial pressure distribution. Furthermore, a real-time pressure mapping insole was fabricated on the basis of a large-area micropillared ionic gel dielectric layer combined with the screen-printing technique. The scalable and low-cost fabrication of pressure sensors with micropillars templated from a track-etch membrane provides new insights into the future development of health monitoring and human-machine interaction.

10.1021/acsami.1c21648

Cite

Citations (16)

A Wearable Co-Located Neural-Mechanical Signal Sensing Device for Simultaneous Bimodal Muscular Activity Detection

IEEE Transactions on Biomedical Engineering (2023)

Tiantong Wang Yunbiao Zhao Qining Wang

The co-located and concurrent measurement of both muscular neural activity and muscular deformation is considered necessary in many applications, such as medical robotics, assistive exoskeletons and muscle function evaluations. Nevertheless, conventional muscle-related signal perception systems either detect only one of these modalities, or are made with rigid and bulky components that cannot provide conformal and flexible interface. Herein, a flexible, easy-to-fabricate, bimodal muscular activity sensing device, which collects neural and mechanical signal at the same muscle location, is reported. The sensing patch includes a screen-printed sEMG sensor, and a pressure-based muscular deformation sensor (PMD sensor) based on a highly sensitive, co-planar iontronic pressure sensing unit. Both sensors are integrated on a super-thin (25 μm) substrate. The sEMG sensor shows a high signal-to-noise ratio of 37.1 dB, and the PMD sensor sensor exhibits a high sensitivity of 70.9 kPa -1. The responses of the sensor to three types of muscle activities (isotonic, isometric, and passive stretching) were analyzed and validated by ultrasound imaging. Bimodal signals during dynamic walking experiments with different level-ground walking speeds were also investigated. The application of the bimodal sensor was verified in gait phase estimation, and results show that the assembly of both modalities significantly reduce (p < 0.05) the average estimation error across all subjects and all walking speeds to 3.82%. Demonstrations show the potential of this sensing device for informative evaluation of muscular activities, and its abilities in human-robot interaction.

SIGNAL (programming language)

10.1109/tbme.2023.3287729

Cite

Citations (5)

A Diving Glove with Inertial Sensors for Underwater Gesture Recognition

Lecture notes in computer science (2023)

Qi Tang Jingeng Mai Tiantong Wang Qining Wang

Wired glove

10.1007/978-981-99-6486-4_20

Cite

Citations (0)

Triboelectric and iontronic dual-responsive bioinspired ionic skin for human–like dexterous robotic manipulation

Nano Energy (2024)

Dongjie Jiang Tiantong Wang Engui Wang Jiangtao Xue Wenhao Diao

Biomimetics

Biomimetic Materials

10.1016/j.nanoen.2024.110257

Cite

Citations (0)

Flexible Non-contact Capacitive Sensing for Hand Gesture Recognition

Lecture notes in computer science (2021)

Tiantong Wang Yunbiao Zhao Qining Wang

10.1007/978-3-030-89095-7_58

Cite

Citations (1)

A Flexible Iontronic Capacitive Sensing Array for Hand Gesture Recognition Using Deep Convolutional Neural Networks

Soft Robotics (2022)

Tiantong Wang Yunbiao Zhao Qining Wang

Hand gesture recognition, one of the most popular research topics in human-machine interaction, is extensively used in visual and augmented reality, sign language translation, prosthesis control, and so on. To improve the flexibility and interactivity of wearable gesture sensing interfaces, flexible electronic systems for gesture recognition have been widely studied. However, these systems are limited in terms of wearability, stability, scalability, and robustness. Herein, we report a flexible wearable hand gesture recognition system that is based on an iontronic capacitive pressure sensing array and deep convolutional neural networks. The entire capacitive array is integrated into a flexible silicone wristband and can be comfortably and conveniently wrapped around the wrist. The pressure sensing array, which is composed of an iontronic film sandwiched between two flexible screen-printed electrode arrays, exhibits a high sensitivity (775.8 kPa-1), fast response time (65 ms), and high durability (over 6000 cycles). Image processing techniques and deep convolutional neural networks are applied for sensor signal feature extraction and hand gesture recognition. Several contexts such as intertrial test (average accuracy of 99.9%), intersession rewearing (average accuracy of 93.2%), electrode shift (average accuracy of 83.2%), and different arm positions during measurement (average accuracy of 93.1%) are evaluated.

Robustness

10.1089/soro.2021.0209

Cite

Citations (21)

Hip Exoskeleton Based Trans-multi-articular Gait Intervention: Preliminary Study on Achilles Tendon Rehabilitation

Zilu Wang Zhihao Zhou Chenxi Zhang Tiantong Wang Heran Zhong

10.1109/embc53108.2024.10781750

Cite

Citations (0)

Fabrication of Breathable Multifunctional On-Skin Electronics Based on Tunable Track-Etched Membranes

ACS Applied Electronic Materials (2024)

Yunbiao Zhao Tiantong Wang Yue Li Ziqiang Zhao Jianming Xue

The on-skin electronics have been extensively studied in various applications such as human–machine interfaces, intelligent prostheses, and health monitoring. However, the current research on flexible electronics tends to focus largely on improving flexibility, functionality, and stability while overlooking the physiological comfort. Therefore, it is necessary to develop a flexible, permeable material and structure to improve long-term wearing comfort for on-skin electronics. Here, the fabrication of breathable multifunctional on-skin electronics based on highly flexible and tunable track-etched membranes is reported. The track-etched membranes are fabricated by a state-of-the-art ion bombardment strategy and feature a smooth surface and unique pore structure regarding precisely tunable pore size and pore density, which offer simultaneously controllable permeability, high functionality, and durability. The track-etched membrane with a pore size of 12.63 μm exhibits an ultrahigh air permeability (190.6 mm s–1) and moisture permeability (2051 g m–2 day–1). Finally, highly flexible and breathable pressure sensors and bioelectric electrodes based on track-etched membranes with advanced thermoregulation are proposed for continuous monitoring of motion and physiological signals.

Flexible Electronics

10.1021/acsaelm.3c01414

Cite

Citations (1)