| Affiliation: | 1. Department of Electronic Science, Xiamen University, Xiamen, 361005 China;2. Academy for Advanced Interdisciplinary Science and Technology, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083 China
Beijing Key Laboratory for Advanced Energy Materials and Technologies, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083 China;3. Department of Ocean and Mechanical Engineering, Florida Atlantic University, Boca Raton, FL, 33431 USA;4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798 Singapore |
| Abstract: | Intuitive, efficient, and unconstrained interactions require human–machine interfaces (HMIs) to accurately recognize users' manipulation intents. Susceptibility to interference and conditional usage mode of HMIs will lead to poor experiences that limit their great interaction potential. Herein, a programmable and ultrasensitive haptic interface enabling closed-loop human–machine interactions is reported. A cross-scale architecture design strategy is proposed to fabricate the haptic interface, which optimizes the hierarchical contact process. The synergistic optimization of the cross-scale architecture between carbon nanotubes and the multiscale sensing structure realizes a haptic interface with ultrahigh sensitivity and a wide detection range of 15.1 kPa?1 and 180 kPa, which are improved by more than 900% over the performance of the common interface. The rapid response time of <5 ms and the limit of detection of 8 Pa of the haptic interface far surpass the somatosensory perception of human skin, which enables the haptic interface to accurately recognize interactive intents. A wireless pressure-data interactive glove (wireless PDI glove) is designed and realizes a round-the-clock operation, noise immunity, and efficient interactive control, which perfectly compensate for the flaws of typical vision and voice recognition modes. |
