Abstract
Entering the 5G and internet of things (IoT) era, human–machine interfaces (HMIs) capable of providing humans with more intuitive interaction with the digitalized world have experienced a flourishing development in the past few years. Although the advanced sensing techniques based on complementary metal-oxide-semiconductor (CMOS) or microelectromechanical system (MEMS) solutions, e.g., camera, microphone, inertial measurement unit (IMU), etc., and flexible solutions, e.g., stretchable conductor, optical fiber, etc., have been widely utilized as sensing components for wearable/non-wearable HMIs development, the relatively high-power consumption of these sensors remains a concern, especially for wearable/portable scenarios. Recent progress on triboelectric nanogenerator (TENG) self-powered sensors provides a new possibility for realizing low-power/self-sustainable HMIs by directly converting biomechanical energies into valuable sensory information. Leveraging the advantages of wide material choices and diversified structural design, TENGs have been successfully developed into various forms of HMIs, including glove, glasses, touchpad, exoskeleton, electronic skin, etc., for sundry applications, e.g., collaborative operation, personal healthcare, robot perception, smart home, etc. With the evolving artificial intelligence (AI) and haptic feedback technologies, more advanced HMIs could be realized towards intelligent and immersive human–machine interactions. Hence, in this review, we systematically introduce the current TENG HMIs in the aspects of different application scenarios, i.e., wearable, robot-related and smart home, and prospective future development enabled by the AI/haptic-feedback technology. Discussion on implementing self-sustainable/zero-power/passive HMIs in this 5G/IoT era and our perspectives are also provided.
Highlights
After integrating a wireless printed circuit board (PCB) for signal collection, processing and transmission, a wearable sign-tospeech translation system could be achieved with the multi-class support vector machine (SVM) machine-learning algorithm, whose overall accuracy could be maintained higher than 98.63% with fast response time (
With deep learning enabled data analytics, the identity information associated with gait patterns can be extracted from the output signals, and high recognition accuracy of 96% could be achieved for 10 persons based on their specific walking gaits, enabling diversified applications in the smart home such as position sensing, activity/healthcare monitoring, and security
We systematically summarize the key technologies and progress of TENG-based human–machine interfaces (HMIs) in terms of different application scenarios, including wearable HMIs, robotic-related HMIs and HMIs for smart home applications
Summary
In this review, we systematically introduce the recent progress in the TENGbased HMIs from the following sections: (1) glove-based HMIs for advanced manipulation, gesture recognition and tactile sensing; (2) wearable HMIs for other biomechanical signal collection, e.g., eye motion, facial expressions, voice, posture, etc.; self-powered HMIs for (3) robotic perception and (4) smart home applications; (5) ML-enabled intelligent HMIs, and the possible future research direction enabled by the (6) haptic-feedback technology and towards (7) self-sustainable/zero-power/passive HMI terminals. Current issues and the potential development trends for TENG-based HMIs are provided for future research in this 5G/IoT era
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