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With the prospect of a smart society in the foreseeable future, humans are experiencing an increased link to electronics in the digital world, which can benefit our life and productivity drastically. In recent decades, advances in the Human Machine Interface (HMI) have improved from tactile sensors, such as touchpads and joysticks, to now include the accurate detection of dexterous body movements in more diversified and sophisticated devices. Advancements in highly adaptive machine learning techniques, neural interfaces, and neuromorphic sensing have generated the potential for an economic and feasible solution for next-generation applications such as wearable HMIs with intimate and multi-directional sensing capabilities. This review offers a general knowledge of HMI technologies beginning with tactile sensors and their piezoresistive, capacitive, piezoelectric, and triboelectric sensing mechanisms. A further discussion is given on how machine learning, neural interfaces, and neuromorphic electronics can be used to enhance next-generation HMIs in an upcoming 5 G infrastructure and advancements in the internet of things and artificial intelligence of things in the near future. The efficient interactions with kinetic and physiological signals from human body through the fusion of tactile sensor and neural electronics will bring a revolution to both the advanced manipulation and medical rehabilitation.
Zhu et al. (Mon,) studied this question.