Integrated Sensing–Memory–Computing Artificial Tactile System for Physiological Signal Processing Based on ITO Nanowire Synaptic Transistors
Real-time, reliable, and continuous monitoring of tactile signals is essential for the development of future human–machine interfaces. However, the weak tactile signals (such as pulses) and vast monitoring data propose significant challenges for traditional perceptual monitoring systems. Inspired by skin tactile perception, a neuromorphic physiological signal processing system is proposed by integrating indium tin oxide (ITO) nanowire synaptic transistors and biohydrogels. The external tactile information can be converted to electrical signals by a highly sensitive array of hydrogels. These electrical signals are then processed and transmitted through the ITO nanowire synaptic transistors, enabling precise sensing and analysis of complex tactile information. This system demonstrates significant potential for a wide range of applications, including but not limited to Braille recognition, where it can enable precise tactile feedback and interaction for individuals with visual impairments. Furthermore, it offers a versatile platform for the real-time acquisition, monitoring, and processing of physiological signals such as those related to muscle activity, heart rate, or neural responses. This work offers promising potential for next-generation intelligent healthcare monitoring, diagnosing, and treating applications.
Authors
- Feiyang Xu (ORCID: https://orcid.org/0000-0002-0231-9408)
- Seeram Ramakrishna (ORCID: https://orcid.org/0000-0001-8479-8686)
- Yuanbin Qin (ORCID: https://orcid.org/0000-0003-3349-9787)
- Yu Zhang
- Fengyun Wang (ORCID: https://orcid.org/0000-0002-5558-423X)
- Jiaqi Xu (ORCID: https://orcid.org/0009-0007-1393-7459)
- Mengyao Wei
- Jinbo Ming
- Dalong Ge
- Liren Wu
Institutions
- Qingdao University (CN)
- National University of Singapore (SG)
- Université Bourgogne Franche-Comté (FR)
- Xi'an Jiaotong University (CN)
Publication Details
- Journal
- ACS Applied Nano Materials
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1021/acsanm.5c00196
- Citations
- 2
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.46
Funders
- Qingdao Postdoctoral Science Foundation
- Natural Science Foundation of Shanxi Province
- Shandong Provincial Postdoctoral Science Foundation