Wearable Sensors-Enabled Human–Machine Interaction Systems: From Design to Application

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics. Gait reveals sensory information in daily life containing personal information, regarding identification and healthcare. Current wearable electronics of gait analysis are mainly limited by high fabrication cost, operation energy consumption, or inferior analysis methods, which barely involve machine learning or implement nonoptimal models that require massive datasets for training. Herein, we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data. The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information, regarding the identity, health status, and activity of the users. To further address the issue of ineffective analysis methods, an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed, which produces a 93.54% identification accuracy of 13 participants and detects five different human activities with 96.67% accuracy. Toward practical application, we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring, healthcare, identification, and future smart home applications.

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Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

Cardiovascular diseases remain the leading cause of death worldwide. The rapid development of flexible sensing technologies and wearable pressure sensors have attracted keen research interest and have been widely used for long‐term and real‐time cardiovascular status monitoring. Owing to compelling characteristics, including light weight, wearing comfort, and high sensitivity to pulse pressures, physiological pulse waveforms can be precisely and continuously monitored by flexible pressure sensors for wearable health monitoring. Herein, an overview of wearable pressure sensors for human pulse wave monitoring is presented, with a focus on the transduction mechanism, microengineering structures, and related applications in pulse wave monitoring and cardiovascular condition assessment. The conceptualizations and methods for the acquisition of physiological and pathological information related to the cardiovascular system are outlined. The biomechanics of arterial pulse waves and the working mechanism of various wearable pressure sensors, including triboelectric, piezoelectric, magnetoelastic, piezoresistive, capacitive, and optical sensors, are also subject to systematic debate. Exemple applications of pulse wave measurement based on microengineering structured devices are then summarized. Finally, a discussion of the opportunities and challenges that wearable pressure sensors face, as well as their potential as a wearable intelligent system for personalized healthcare is given in conclusion.

Wearable Pressure Sensors for Pulse Wave Monitoring

Triboelectric nanogenerator based self-powered sensor for artificial intelligence

Sign language recognition, especially the sentence recognition, is of great significance for lowering the communication barrier between the hearing/speech impaired and the non-signers. The general glove solutions, which are employed to detect motions of our dexterous hands, only achieve recognizing discrete single gestures (i.e., numbers, letters, or words) instead of sentences, far from satisfying the meet of the signers’ daily communication. Here, we propose an artificial intelligence enabled sign language recognition and communication system comprising sensing gloves, deep learning block, and virtual reality interface. Non-segmentation and segmentation assisted deep learning model achieves the recognition of 50 words and 20 sentences. Significantly, the segmentation approach splits entire sentence signals into word units. Then the deep learning model recognizes all word elements and reversely reconstructs and recognizes sentences. Furthermore, new/never-seen sentences created by new-order word elements recombination can be recognized with an average correct rate of 86.67%. Finally, the sign language recognition results are projected into virtual space and translated into text and audio, allowing the remote and bidirectional communication between signers and non-signers. Though wearable gloves are widely used for gesture associated applications (e.g. sign language recognition), sentence identification of sign language remains a challenge. Here, the authors report AI-enabled recognition system helps barrier-free communication between signers and non-signers.

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AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.

Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications. Designing efficient sensors for human machine interaction remains a challenge. Here, the authors present a soft robotic fingers system based on a triboelectric nanogenerator (L-TENG) sensor to capture the continuous motion of soft gripper and a soft tactile (T-TENG) sensor for tactile sensing, that can achieve an object recognition accuracy of 98.1%.

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Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Bioinspired superhydrophobic surfaces have attracted many industrial and academic interests in recent years. Inspired by unique superhydrophobicity and anisotropic friction properties of snake scale surfaces, this study explores the feasibility to produce a bionic superhydrophobic stainless steel surface via laser precision engineering, which allows the realization of directional superhydrophobicity and dynamic control of its water transportation. Dynamic mechanism of water slid-ing on hierarchical snake scale structures is studied, which is the key to reproduce artificially bioinspired multifunctional materials with great potentials to be used for water harvesting, droplet manipulation, pipeline transportation, and vehicle acceleration.

Xuyan Hou

Papers

Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Directional sliding of water: biomimetic snake scale surfaces

Anisotropic Superhydrophobic Properties of Bioinspired Surfaces by Laser Ablation of Metal Substrate inside Water

Study on impact energy absorption and adhesion of biomimetic buffer system for space robots

Adhesion properties of carbon nanotube arrays for an adhesive foot of a space crawling robot