Smart data gloves capable of monitoring finger activities and inferring hand gestures are of significance to human-machine interfaces,robotics,healthcare,and Metaverse.Yet,most current smart data gloves present unstab...Smart data gloves capable of monitoring finger activities and inferring hand gestures are of significance to human-machine interfaces,robotics,healthcare,and Metaverse.Yet,most current smart data gloves present unstable mechanical contacts,limited sensitivity,as well as offline training and updating of machine learning models,leading to uncomfortable wear and suboptimal performance during practical applications.Herein,highly sensitive and mechanically stable textile sensors are developed through the construction of loose MXene-modified textile interface structures and a thermal transfer printing method with the melting-infiltration-solidification adhesion procedure.Then,a smart data glove with adaptive gesture recognition is reported,based on the integration of 10-channel MXene textile bending sensors and a near-sensor adaptive machine learning model.The near-sensor adaptive machine learning model achieves a 99.5%accuracy using the proposed post-processing algorithm for 14 gestures.Also,the model features the ability to locally update model parameters when gesture types change,without additional computation on any external device.A high accuracy of 98.1%is still preserved when further expanding the dataset to 20 gestures,where the accuracy is recovered by 27.6%after implementing the model updates locally.Lastly,an auto-recognition and control system for wireless robotic sorting operations with locally trained hand gestures is demonstrated,showing the great potential of the smart data glove in robotics and human-machine interactions.展开更多
Background With the increasing prominence of hand and finger motion tracking in virtual reality(VR)applications and rehabilitation studies,data gloves have emerged as a prevalent solution.In this study,we developed an...Background With the increasing prominence of hand and finger motion tracking in virtual reality(VR)applications and rehabilitation studies,data gloves have emerged as a prevalent solution.In this study,we developed an innovative,lightweight,and detachable data glove tailored for finger motion tracking in VR environments.Methods The glove design incorporates a potentiometer coupled with a flexible rack and pinion gear system,facilitating precise and natural hand gestures for interaction with VR applications.Initially,we calibrated the potentiometer to align with the actual finger bending angle,and verified the accuracy of angle measurements recorded by the data glove.To verify the precision and reliability of our data glove,we conducted repeatability testing for flexion(grip test)and extension(flat test),with 250 measurements each,across five users.We employed the Gage Repeatability and Reproducibility to analyze and interpret the repeatable data.Furthermore,we integrated the gloves into a SteamVR home environment using the OpenGlove auto-calibration tool.Conclusions The repeatability analysis revealed an aggregate error of 1.45 degrees in both the gripped and flat hand positions.This outcome was notably favorable when compared with the findings from assessments of nine alternative data gloves that employed similar protocols.In these experiments,users navigated and engaged with virtual objects,underlining the glove's exact tracking of finger motion.Furthermore,the proposed data glove exhibited a low response time of 17-34 ms and back-drive force of only 0.19 N.Additionally,according to a comfort evaluation using the Comfort Rating Scales,the proposed glove system is wearable,placing it at the WL1 level.展开更多
Progress is described regarding the development of a new electrotactile feedback glove designed for application to dexterous robot. The sensitivity of operator's finger against electrical stimulus pulse is conside...Progress is described regarding the development of a new electrotactile feedback glove designed for application to dexterous robot. The sensitivity of operator's finger against electrical stimulus pulse is considered. It is found that frequency, duty ratio, and voltage amplitude of electrical stimulus pulse determine the sensitivity of finger. The effects of materials, sizes, arrangements and shapes of electrodes on sensitivity of finger are analyzed. Finally, the tactile tele presence system is designed to experimentally confirm that the robot with electrotactile feedback glove can manipulate dexterous robotic multi fingered hand and identify and classify three sorts of objects.展开更多
The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is t...The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is the emerging technology. Skilled forgers try to attempt the genuine signatures in many numbers of trials. The wide gap in the Euclidian distances between forgers and the genuine template features prohibits them from successful forging. This has been proved by our repeated experiments on various subjects using the above combinational features. In addition the intra trial features captured during the forge attempts also differs widely in the case of forgers and are not consistent that of a genuine signature. This is caused by the pulse characteristics and degree of bilateral hand dimensional similarity, and the degrees of pulse delay. Since this economical and simple optical-based technology is offering an improved biometric security, it is essential to look for other reinforcements such the variability factor considerations which we proved of worth considering.展开更多
基金supported by the National Key R&D Program of China(2021YFB3600502,2022YFB3603403)the National Natural Science Foundation of China(62075040,623B2021)the Start-up Research Fund of Southeast University(RF1028623164).
文摘Smart data gloves capable of monitoring finger activities and inferring hand gestures are of significance to human-machine interfaces,robotics,healthcare,and Metaverse.Yet,most current smart data gloves present unstable mechanical contacts,limited sensitivity,as well as offline training and updating of machine learning models,leading to uncomfortable wear and suboptimal performance during practical applications.Herein,highly sensitive and mechanically stable textile sensors are developed through the construction of loose MXene-modified textile interface structures and a thermal transfer printing method with the melting-infiltration-solidification adhesion procedure.Then,a smart data glove with adaptive gesture recognition is reported,based on the integration of 10-channel MXene textile bending sensors and a near-sensor adaptive machine learning model.The near-sensor adaptive machine learning model achieves a 99.5%accuracy using the proposed post-processing algorithm for 14 gestures.Also,the model features the ability to locally update model parameters when gesture types change,without additional computation on any external device.A high accuracy of 98.1%is still preserved when further expanding the dataset to 20 gestures,where the accuracy is recovered by 27.6%after implementing the model updates locally.Lastly,an auto-recognition and control system for wireless robotic sorting operations with locally trained hand gestures is demonstrated,showing the great potential of the smart data glove in robotics and human-machine interactions.
基金Supported by the Sirindhorn International Institute of Technology,Thammasat University,EFS-G(Excellent foreign Student-Graduate)research fund.
文摘Background With the increasing prominence of hand and finger motion tracking in virtual reality(VR)applications and rehabilitation studies,data gloves have emerged as a prevalent solution.In this study,we developed an innovative,lightweight,and detachable data glove tailored for finger motion tracking in VR environments.Methods The glove design incorporates a potentiometer coupled with a flexible rack and pinion gear system,facilitating precise and natural hand gestures for interaction with VR applications.Initially,we calibrated the potentiometer to align with the actual finger bending angle,and verified the accuracy of angle measurements recorded by the data glove.To verify the precision and reliability of our data glove,we conducted repeatability testing for flexion(grip test)and extension(flat test),with 250 measurements each,across five users.We employed the Gage Repeatability and Reproducibility to analyze and interpret the repeatable data.Furthermore,we integrated the gloves into a SteamVR home environment using the OpenGlove auto-calibration tool.Conclusions The repeatability analysis revealed an aggregate error of 1.45 degrees in both the gripped and flat hand positions.This outcome was notably favorable when compared with the findings from assessments of nine alternative data gloves that employed similar protocols.In these experiments,users navigated and engaged with virtual objects,underlining the glove's exact tracking of finger motion.Furthermore,the proposed data glove exhibited a low response time of 17-34 ms and back-drive force of only 0.19 N.Additionally,according to a comfort evaluation using the Comfort Rating Scales,the proposed glove system is wearable,placing it at the WL1 level.
文摘Progress is described regarding the development of a new electrotactile feedback glove designed for application to dexterous robot. The sensitivity of operator's finger against electrical stimulus pulse is considered. It is found that frequency, duty ratio, and voltage amplitude of electrical stimulus pulse determine the sensitivity of finger. The effects of materials, sizes, arrangements and shapes of electrodes on sensitivity of finger are analyzed. Finally, the tactile tele presence system is designed to experimentally confirm that the robot with electrotactile feedback glove can manipulate dexterous robotic multi fingered hand and identify and classify three sorts of objects.
文摘The novel reinforcement to the data glove based dynamic signature verification system, using the Photometric measurement values collected simultaneously from photo plethysmography (PPG) during the signing process is the emerging technology. Skilled forgers try to attempt the genuine signatures in many numbers of trials. The wide gap in the Euclidian distances between forgers and the genuine template features prohibits them from successful forging. This has been proved by our repeated experiments on various subjects using the above combinational features. In addition the intra trial features captured during the forge attempts also differs widely in the case of forgers and are not consistent that of a genuine signature. This is caused by the pulse characteristics and degree of bilateral hand dimensional similarity, and the degrees of pulse delay. Since this economical and simple optical-based technology is offering an improved biometric security, it is essential to look for other reinforcements such the variability factor considerations which we proved of worth considering.
