Human Machine Interface with Wearable Electronics Using Biodegradable Triboelectric Films for Calligraphy Practice and Correction

Letter handwriting,especially stroke correction,is of great importance for recording languages and expressing and exchanging ideas for individual behavior and the public.In this study,a biodegradable and conductive carboxymethyl chitosan-silk fibroin(CSF)film is prepared to design wearable triboelectric nanogenerator(denoted as CSF-TENG),which outputs of V_(oc)≈165 V,I_(sc)≈1.4μA,and Q_(sc)≈72 mW cm^(−2).Further,in vitro biodegradation of CSF film is performed through trypsin and lysozyme.The results show that trypsin and lysozyme have stable and favorable biodegradation properties,removing 63.1%of CSF film after degrading for 11 days.Further,the CSF-TENG-based human-machine interface(HMI)is designed to promptly track writing steps and access the accuracy of letters,resulting in a straightforward communication media of human and machine.The CSF-TENG-based HMI can automatically recognize and correct three representative letters(F,H,and K),which is benefited by HMI system for data processing and analysis.The CSF-TENG-based HMI can make decisions for the next stroke,highlighting the stroke in advance by replacing it with red,which can be a candidate for calligraphy practice and correction.Finally,various demonstrations are done in real-time to achieve virtual and real-world controls including writing,vehicle movements,and healthcare.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

Regular exercise paves the way to a healthy life.However,conventional sports events are susceptible to weather conditions.Current motion sensors for home-based sports are mainly limited by operation power consumption,single-direction sensitivity,or inferior data analysis.Herein,by leveraging the 3-dimensional printing technique and triboelectric effect,a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory.By integrating with a belt,this sensor could be used to identify some low degree of freedom motions,e.g.,waist or gait motion,with a high accuracy of 93.8%.Furthermore,when wearing the sensor at the ankle position,signals generated from shank motions that contain more abundant information could also be effectively collected.By means of a deep learning algorithm,the kicking direction and force could be precisely differentiated with an accuracy of 97.5%.Toward practical application,a virtual reality-enabled fitness game and a shooting game were successfully demonstrated.This work is believed to open up new insights for the development of future household sports or rehabilitation.

Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so on.According to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and intelligence.Herein,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other technologies.First,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so on.Then self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage technologies.Next,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart systems.In the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

Triboelectric Nanogenerators and Hybridized Systems for Enabling Next-Generation IoT Applications

In the past few years,triboelectric nanogenerator-based(TENG-based)hybrid generators and systems have experienced a widespread and flourishing development,ranging among almost every aspect of our lives,e.g.,from industry to consumer,outdoor to indoor,and wearable to implantable applications.Although TENG technology has been extensively investigated for mechanical energy harvesting,most developed TENGs still have limitations of small output current,unstable power generation,and low energy utilization rate of multisource energies.To harvest the ubiquitous/coexisted energy forms including mechanical,thermal,and solar energy simultaneously,a promising direction is to integrate TENG with other transducing mechanisms,e.g.,electromagnetic generator,piezoelectric nanogenerator,pyroelectric nanogenerator,thermoelectric generator,and solar cell,forming the hybrid generator for synergetic single-source and multisource energy harvesting.The resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations,toward achieving a maximum,reliable,and stable output generation.Hence,in this review,we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems,in the aspects of operation principles,structure designs,optimization strategies,power management,and system integration.The recent progress of TENG-based hybrid generators and hybridized systems for the outdoor,indoor,wearable,and implantable applications is also provided.Lastly,we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring,human activity sensation,human-machine interaction,smart home,healthcare,wearables,implants,robotics,Internet of things(IoT),and many other fields.

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

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.

Volatile organic compounds sensing based on Bennet doubler-inspired triboelectric nanogenerator and machine learning-assisted ion mobility analysis

Ion mobility analysis is a well-known analytical technique for identifying gas-phase compounds in fastresponse gas-monitoring systems.However,the conventional plasma discharge system is bulky,operates at a high temperature,and inappropriate for volatile organic compounds(VOCs)concentration detection.Therefore,we report a machine learning(ML)-enhanced ion mobility analyzer with a triboelectric-based ionizer,which offers good ion mobility selectivity and VOC recognition ability with a small-sized device and non-strict operating environment.Based on the charge accumulation mechanism,a multi-switched manipulation triboelectric nanogenerator(SM-TENG)can provide a direct current(DC)bias at the order of a few hundred,which can be further leveraged as the power source to obtain a unique and repeatable discharge characteristic of different VOCs,and their mixtures,with a special tip-plate electrode configuration.Aiming to tackle the grand challenge in the detection of multiple VOCs,the ML-enhanced ion mobility analysis method was successfully demonstrated by extracting specific features automatically from ion mobility spectrometry data with ML algorithms,which significantly enhance the detection ability of the SM-TENG based VOC analyzer,showing a portable real-time VOC monitoring solution with rapid response and low power consumption for future internet of things based environmental monitoring applications.