High‐Performance MXene/TPU Flexible Piezoresistive Sensors for Enhanced Gesture Recognition and Interactive Control Applications
Source abstract
Abstract
Abstract Recent advancements in human‐machine interaction have demanded flexible sensors with high sensitivity and mechanical robustness for real‐time gesture recognition. In this study, a novel MXene/TPU‐based flexible piezoresistive sensor is developed through a combination of electrospinning and MXene functionalization techniques. By optimizing the soaking–drying cycles of MXene nanosheets on TPU nanofiber membranes, the sensor achieved tunable sensitivity. The sensor demonstrated an exceptional sensitivity of 78 556 within the strain range of 110–150% and exhibited outstanding durability (>3000 cycles). Furthermore, a smart glove embedded with five sensors is developed, integrated with a low‐power multichannel acquisition system and an LSTM–Transformer hybrid model. The integrated system delivers superior gesture recognition accuracy of 99.47% across 32 gestures, outperforming conventional methods such as SVM (96.47%), KNN (93.51%), and RNN (98.77%). In addition, real‐time robotic hand control and VR interaction further validated its practicality in wearable electronics. Overall, this study presents a comprehensive framework for the design of flexible sensors with enhanced sensitivity, stretchability, and stability, contributing to the advancement of intelligent human‐machine interface technologies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · MXene and MAX Phase Materials · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Precision Measuring Technology and Instrument School of Precision Instrument and Opto‐electronics Engineering Tianjin University Tianjin 300072 P. R. China
Funding
National Natural Science Foundation of China
References (45)
Show source-provided references
1. 10.1016/j.iot.2023.10078010.1016/j.iot.2023.100780 ↗
2. 10.1038/s41586-023-06221-210.1038/s41586-023-06221-2 ↗
3. 10.1016/j.ssaho.2023.10053210.1016/j.ssaho.2023.100532 ↗
4. 10.1007/s10479-023-05285-710.1007/s10479-023-05285-7 ↗
5. 10.1021/acsami.3c0870910.1021/acsami.3c08709 ↗
6. 10.1016/j.cviu.2024.10409510.1016/j.cviu.2024.104095 ↗
7. 10.1016/j.heliyon.2024.e3220710.1016/j.heliyon.2024.e32207 ↗
8. 10.1038/s41528-023-00246-310.1038/s41528-023-00246-3 ↗
9. 10.1016/j.jcis.2022.03.01310.1016/j.jcis.2022.03.013 ↗
10. 10.1021/acsapm.3c0247610.1021/acsapm.3c02476 ↗
11. 10.1007/s40820-023-01311-210.1007/s40820-023-01311-2 ↗
12. 10.1039/D4TC00467A10.1039/D4TC00467A ↗
13. 10.1021/acsnano.4c1310610.1021/acsnano.4c13106 ↗
14. 10.1016/j.nanoen.2021.10660610.1016/j.nanoen.2021.106606 ↗
15. 10.3390/mi1203025210.3390/mi12030252 ↗
16. 10.1088/1361-665X/ac102c10.1088/1361-665X/ac102c ↗
17. 10.1186/s40537-024-00973-y10.1186/s40537-024-00973-y ↗
18. 10.1016/j.neunet.2024.10659810.1016/j.neunet.2024.106598 ↗
19. 10.1016/j.eswa.2022.11914010.1016/j.eswa.2022.119140 ↗
20. 10.1016/j.enbuild.2023.11339610.1016/j.enbuild.2023.113396 ↗
21. 10.1016/j.colsurfa.2024.13382110.1016/j.colsurfa.2024.133821 ↗
22. 10.1039/D3MH01138K10.1039/D3MH01138K ↗
23. 10.1021/acsami.7b1969910.1021/acsami.7b19699 ↗
24. 10.1021/acsami.2c0477310.1021/acsami.2c04773 ↗
25. 10.1002/marc.20210087310.1002/marc.202100873 ↗
26. 10.1021/acsnano.8b0780510.1021/acsnano.8b07805 ↗
27. 10.1002/mame.20190073610.1002/mame.201900736 ↗
28. 10.1007/s10854-020-03969-510.1007/s10854-020-03969-5 ↗
29. 10.1021/acsami.2c1898910.1021/acsami.2c18989 ↗
30. 10.1021/acs.macromol.3c0202510.1021/acs.macromol.3c02025 ↗
31. 10.1016/j.compositesb.2019.10769610.1016/j.compositesb.2019.107696 ↗
32. 10.1002/adfm.20191050410.1002/adfm.201910504 ↗
33. 10.1016/j.matlet.2018.10.06810.1016/j.matlet.2018.10.068 ↗
34. 10.1021/acsami.2c0828510.1021/acsami.2c08285 ↗
35. 10.1016/j.snb.2022.13222810.1016/j.snb.2022.132228 ↗
36. 10.1016/j.compscitech.2020.10844810.1016/j.compscitech.2020.108448 ↗
37. 10.1016/j.nanoen.2019.10414310.1016/j.nanoen.2019.104143 ↗
38. 10.1002/aelm.20200124210.1002/aelm.202001242 ↗
39. 10.1016/j.cej.2019.12339110.1016/j.cej.2019.123391 ↗
40. 10.1021/acsami.2c0657410.1021/acsami.2c06574 ↗
41. 10.1021/acssuschemeng.1c0674110.1021/acssuschemeng.1c06741 ↗
42. 10.1016/j.compositesb.2021.10866510.1016/j.compositesb.2021.108665 ↗
43. 10.1021/acs.iecr.3c0090510.1021/acs.iecr.3c00905 ↗
44. D.Wei H.Xu inIn 2024 IEEE Int. Conf. on Robotics and Automation (ICRA) IEEE New Jersey 2024.
45. 10.1109/JSEN.2023.324465910.1109/JSEN.2023.3244659 ↗