Triboelectric‐Inertial Sensing Glove Enhanced by Charge‐Retained Strategy for Human‐Machine Interaction
Open accessAbstract
As technology advances, human‐machine interaction (HMI) demands more intuitive and natural methods. To meet this demand, smart gloves, capable of capturing intricate hand movements, are emerging as vital HMI tools. Moreover, triboelectric‐based sensors, with their self‐powered, cost‐effective, and material various characteristics, can offer promising solutions for enhancing existing glove systems. However, a key limitation of these sensors is that charge leakage in the measurement circuit results in capturing only transient signals, rather than continuous changes. To address this issue, a charge‐retained circuit that effectively prevents triboelectric signal attenuation is developed, enabling accurate measurement of continuous finger movements. This innovation forms the foundation of a highly integrated smart glove system, enhancing HMI functionality by combining continuous triboelectric signals with inertial sensor data. The system showcases a diverse range of applications, including complex robotic control, virtual reality interaction, smart home lighting adjustments, and intuitive interface operations. Furthermore, by leveraging artificial intelligence (AI) techniques, the system achieves accurate recognition of complex sign language with an impressive 99.38% accuracy. This work presents a charge‐retained approach for continuous sensing with triboelectric‐based sensors, offering valuable insights for developing future multifunctional HMI and sign language recognition systems. The proposed smart glove system, with its dual‐mode sensing and AI integration, holds great potential for revolutionizing various domains and enhancing user experiences.
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For sign language (finger flexion and hand orientation), knuckle strains and angles are biomechanical features measured by IMU and strain sensor.
“| Sign language (finger flexion and hand orientation) | Knuckle strains and angles | IMU, Strain sensor | 194 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [194]
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Cited by
Sorted by citations
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
Self-citation
- Authors
- Yang, B., Cheng, J., Qu, X., Song, Y., Yang, L., Shen, J., Bai, Z., & Ji, L.
- Affiliations
- State Key Laboratory of Tribology (CN)
- Beijing Lvkedu Science and Technology Co. Ltd. Beijing 100190 China
- China University of Geosciences (Beijing) (CN)
- Corresponding author
- Cheng, Jia
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2024-11-22
- Volume
- 12
- Issue
- 3
- Pages
- e2408689-e2408689
- ISSN
- 2198-3844 (ISSN-L)
- PMC
- PMC11744583
- OpenAlex
- W4404619018
- Cited by
- 59
- References
- 41
- Citations by year
- 2026: 27 · 2025: 31 · 2024: 1
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Advanced Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (52375294, 52075286)
- China Postdoctoral Science Foundation (2023M731943, BX20230169)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Sensor and Energy Harvesting Materials
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- triboelectric sensors, inertial sensors, smart glove, human-machine interaction, charge retention, charge leakage, finger movement detection, sign language recognition, self-powered sensors, virtual reality interaction, robot control
- MeSH terms
- Wearable Electronic Devices, Artificial Intelligence, Equipment Design, Humans, Man-Machine Systems, Robotics
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