As key interfaces for the disabled, optimal prosthetics should elicit natural sensations of skin touch or proprioception, by unambiguously delivering the multimodal signals acquired by the prosthetics to the nervous system, which still remains challenging. Here, a bioinspired temperature-pressure electronic skin with decoupling capability (TPD e-skin), inspired by the high-low modulus hierarchical structure of human skin, is developed to restore such functionality. Due to the bionic dual-state amplifying microstructure and contact resistance modulation, the MXene TPD e-skin exhibits high sensitivity over a wide pressure range and excellent temperature insensitivity (91.2% reduction). Additionally, the high-low modulus structural configuration enables the pressure insensitivity of the thermistor. Furthermore, a neural model is proposed to neutrally code the temperature-pressure signals into three types of nerve-acceptable frequency signals, corresponding to thermoreceptors, slow-adapting receptors, and fast-adapting receptors. Four operational states in the time domain are also distinguished after the neural coding in the frequency domain. Besides, a brain-like machine learning-based fusion process for frequency signals is also constructed to analyze the frequency pattern and achieve object recognition with a high accuracy of 98.7%. The TPD neural system offers promising potential to enable advanced prosthetic devices with the capability of multimodality-decoupling sensing and deep neural integration.
Keywords: bioinspired; electronic skin; human-prosthetics interfaces; machine learning; multimodal sensors; neural models.
© 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.