A human skin-inspired self-powered flex sensor with thermally embossed microstructured triboelectric layers for sign language interpretation. (October 2020) - Maharjan, Pukar; Bhatta, Trilochan; Salauddin, Md; Rasel, M.S.; Rahman, M.T.; Rana, S.M.S.; Park, Jae Yeong
Abstract: Flex sensors are essential for mechanosensation of human gesture monitoring, electronic skin development, and human-machine interfaces, but require a power supply for their operation. In this work, a highly sensitive and facile fabricated self-powered triboelectric flex sensor (STFS) is presented which can efficiently detect the finger bending motion and monitor the hand gestures. Drawn inspiration from the highly sensitive human skin dermis-epidermis interlocked haptic performance, the fabricated STFS consists of randomly distributed microstructured (RDM) triboelectric layers imprinted from an emery paper through thermal embossing technology, achieving a high sensitivity of 0.77 VkPa −1 along with rapid rise time of 83 ms and high stability (>100, 000 loading-unloading cycles). Also, the proposed flex sensor exhibits an ultrawide range of pressure detection from 0.2 kPa to 500 kPa. Moreover, a real-time application of sign language interpretation by detecting finger gestures and converting those gestures into voice and text through smartphone application is successfully demonstrated. This facile fabrication process paved a highly cost-effective, large scalable and time-efficient development of the self-powered flex sensor device with superior sensing properties, and high mechanical robustness for hand gesture monitoring and sign language interpretation system as well as human-machine interface application. Graphical abstract: Image 1 Highlights: A human skin-inspired self-powered triboelectric flex sensor with randomly distributed microstructures is presented. Facile and cost-effective fabrication of a self-powered flex sensor based on thermal embossing of an emery paper. Flex sensor with a maximum sensitivity of 0.77 VkPa-1 and ultra-stability of >100, 000 cycles. The sensor works in an ultrawide range of pressure detection from 0.2 kPa to 500 kPa. Sign language interpretation is successfully demonstrated in real-time through a smartphone.