Designing interactive glazing through an engineering psychology approach:Six augmented reality scenarios that envision future car human-machine interface

Background With an increasing number of vehicles becoming autonomous,intelligent,and connected,paying attention to the future usage of car human-machine interface with these vehicles should become more relevant.Several studies have addressed car HMI but were less attentive to designing and implementing interactive glazing for every day(autonomous)driving contexts.Methods Reflecting on the literature,we describe an engineering psychology practice and the design of six novel future user scenarios,which envision the application of a specific set of augmented reality(AR)support user interactions.Additionally,we conduct evaluations on specific scenarios and experiential prototypes,which reveal that these AR scenarios aid the target user groups in experiencing a new type of interaction.The overall evaluation is positive with valuable assessment results and suggestions.Conclusions This study can interest applied psychology educators who aspire to teach how AR can be operationalized in a human-centered design process to students with minimal pre-existing expertise or minimal scientific knowledge in engineering psychology.

Deep Learning for EMG-based Human-Machine Interaction:A Review

Electromyography(EMG)has already been broadly used in human-machine interaction(HMI)applications.Determining how to decode the information inside EMG signals robustly and accurately is a key problem for which we urgently need a solution.Recently,many EMG pattern recognition tasks have been addressed using deep learning methods.In this paper,we analyze recent papers and present a literature review describing the role that deep learning plays in EMG-based HMI.An overview of typical network structures and processing schemes will be provided.Recent progress in typical tasks such as movement classification,joint angle prediction,and force/torque estimation will be introduced.New issues,including multimodal sensing,inter-subject/inter-session,and robustness toward disturbances will be discussed.We attempt to provide a comprehensive analysis of current research by discussing the advantages,challenges,and opportunities brought by deep learning.We hope that deep learning can aid in eliminating factors that hinder the development of EMG-based HMI systems.Furthermore,possible future directions will be presented to pave the way for future research.

BLIND SPEECH SEPARATION FOR ROBOTS WITH INTELLIGENT HUMAN-MACHINE INTERACTION

Speech recognition rate will deteriorate greatly in human-machine interaction when the speaker's speech mixes with a bystander's voice. This paper proposes a time-frequency approach for Blind Source Seperation (BSS) for intelligent Human-Machine Interaction(HMI). Main idea of the algorithm is to simultaneously diagonalize the correlation matrix of the pre-whitened signals at different time delays for every frequency bins in time-frequency domain. The prososed method has two merits: (1) fast convergence speed; (2) high signal to interference ratio of the separated signals. Numerical evaluations are used to compare the performance of the proposed algorithm with two other deconvolution algorithms. An efficient algorithm to resolve permutation ambiguity is also proposed in this paper. The algorithm proposed saves more than 10% of computational time with properly selected parameters and achieves good performances for both simulated convolutive mixtures and real room recorded speeches.

A Robotic Teleoperation System for Precise Robotic Manipulation by Human-Machine Interaction

Teleoperation is of great importance in the area of robotics,especially when people are unavailable in the robot workshop.It provides a way for people to control robots remotely using human intelligence.In this paper,a robotic teleoperation system for precise robotic manipulation is established.The data glove and the 7-degrees of freedom(DOFs)force feedback controller are used for the remote control interaction.The control system and the monitor system are designed for the remote precise manipulation.The monitor system contains an image acquisition system and a human-machine interaction module,and aims to simulate and detect the robot running state.Besides,a visual object tracking algorithm is developed to estimate the states of the dynamic system from noisy observations.The established robotic teleoperation systemis applied to a series of experiments,and high-precision results are obtained,showing the effectiveness of the physical system.

Skin‑Inspired Ultra‑Tough Supramolecular Multifunctional Hydrogel Electronic Skin for Human–Machine Interaction

Multifunctional supramolecular ultra-tough bionic e-skin with unique durability for human–machine interaction in complex scenarios still remains challenging.Herein,we develop a skininspired ultra-tough e-skin with tunable mechanical properties by a physical cross-linking salting-freezing-thawing method.The gelling agent(β-Glycerophosphate sodium:Gp)induces the aggregation and binding of PVA molecular chains and thereby toughens them(stress up to 5.79 MPa,toughness up to 13.96 MJ m^(−3)).Notably,due to molecular self-assembly,hydrogels can be fully recycled and reprocessed by direct heating(100°C for a few seconds),and the tensile strength can still be maintained at about 100%after six recoveries.The hydrogel integrates transparency(>60%),super toughness(up to 13.96 MJ m^(−3),bearing 1500 times of its own tensile weight),good antibacterial properties(E.coli and S.aureus),UV protection(Filtration:80%–90%),high electrical conductivity(4.72 S m^(−1)),anti-swelling and recyclability.The hydrogel can not only monitor daily physiological activities,but also be used for complex activities underwater and message encryption/decryption.We also used it to create a complete finger joint rehabilitation system with an interactive interface that dynamically presents the user’s health status.Our multifunctional electronic skin will have a profound impact on the future of new rehabilitation medical,human–machine interaction,VR/AR and the metaverse fields.

Flexible and multifunctional triboelectric nanogenerator based on liquid metal/polyvinyl alcohol hydrogel for energy harvesting and self-powered wearable human-machine interaction

Hydrogel-based triboelectric nanoge nerator(TENG)has a promising applied prospect in wearable electronic devices.However,its low performance,poor stability,insufficient recyclability and inferior self-healing seriously hinder its development.Herein,we report a robust route to a liquid metal(LM)/polyvinyl alcohol(PVA)hydrogel-based TENG(LP-TENG).Owing to the intrinsically liquid feature of conductive LM within the flexible PVA hydrogel,the as-prepared LP-TENG exhibited comprehensiye advantages of adaptability,biocompatibility,outstanding electrical performance,superior stability,recyclability and diverse applications,which were unattainable by traditional systems.Concretely,the LP-TENG delivered appealing open circuit voltage of 250 V,short circuit current of 4μA and transferred charge of 120 nC with high stability,outperforming most advanced TENG systems.The LP-TENG was successfully employed for versatile applications with multifunctionality,including human motion detection,handwriting recognition,energy collection,message transmission and human-machine interaction.This work presents significant prospects for crafting advanced materials and devices in the fields of wearable electronics,flexible skin and smart robots.

Magnetic Array Assisted Triboelectric Nanogenerator Sensor for Real‑Time Gesture Interaction

In human-machine interaction,robotic hands are useful in many scenarios.To operate robotic hands via gestures instead of handles will greatly improve the convenience and intuition of human-machine interaction.Here,we present a magnetic array assisted sliding triboelectric sensor for achieving a real-time gesture interaction between a human hand and robotic hand.With a finger’s traction movement of flexion or extension,the sensor can induce positive/negative pulse signals.Through counting the pulses in unit time,the degree,speed,and direction of finger motion can be judged in realtime.The magnetic array plays an important role in generating the quantifiable pulses.The designed two parts of magnetic array can transform sliding motion into contact-separation and constrain the sliding pathway,respectively,thus improve the durability,low speed signal amplitude,and stability of the system.This direct quantization approach and optimization of wearable gesture sensor provide a new strategy for achieving a natural,intuitive,and real-time human-robotic interaction.

Human Interaction Dynamics for Its Use in Mobile Robotics:Impedance Control for Leader-follower Formation

A complete characterization of the behavior in human-robot interactions(HRI) includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields.As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure.Finally, with the best fictitious force and its identified impedance,an impedance control is designed for a mobile robot Pioneer 3AT,which is programmed to follow a human in a structured scenario.According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

Interaction and the Genesis of Experience: A Phenomenological Contribution for Meaningful Embodied Artificial Intelligence

In this article I will address the issue of the meaning of Embodied Artificial Intelligence(EAI)as it is configured today.My starting point is the refined interactive perspective on the semantics of EAI,as was recently suggested by Froese and colleagues.This perspective rests on the assumption that the concept of human bodily subjectivity must be extended to include meaning-making processes,which are enabled by advanced AI systems that may be incorporated in the human biological body.After having clarified the technical background,I will introduce the genetic component of the phenomenological method as a suitable tool to face the aforementioned issue.Towards this end,I will place the genetic method in the context of the so-called New Human-Machine Interaction(New HMI).I will further outline a genetic phenomenology of visual embodiment,suggesting a futuristic application based on the thesis of the“technological supplementation of phenomenological methodology”through the synthetic method.The case at stake is that of patients with a severe clinical picture characterised by the loss of corneal function,who in the near future could be treated with synthetic corneal prosthetic implants produced by a 3D bio-printing process by using an advanced EAI technique.I will conclude this article with a brief review of the main problems that still remain open.

Recent advances in stretchable triboelectric nanogenerators for use in wearable bioelectronic devices

Wearable bioelectronic devices have the capacity for real-time human health monitoring,the provision of tailored services,and natural interaction with smart devices.However,these wearable bioelectronic devices rely on conventional rigid batteries that are frequently charged or replaced and are incompatible with the skin,leading to a discontinuity in complex therapeutic tasks related to human health monitoring and human-machine interaction.Stretchable triboelectric nanogenerator(TENG)is a high-efficiency energy harvesting technology that converts mechanical into electrical energy,effectively powering wearable bioelectronic devices.This study comprehensively overviews recent advances in stretchable TENG for use in wearable bioelectronic devices.The working mechanism of stretchable TENG is initially explained.A comprehensive discussion presents the approaches for fabricating stretchable TENG,including the design of stretchable structures and the selection of stretchable materials.Furthermore,applications of wearable bioelectronic devices based on stretchable TENG in human health monitoring(body movements,pulse,and respiration)and human-machine interaction(touch panels,machine control,and virtual reality)are introduced.Ultimately,the challenges and developmental trends regarding wearable bioelectronic devices based on stretchable TENG are elaborated.

A wave-shaped electrode flexible sensor capable of sensitively responding to wrinkle excitation for a multifunctional human-computer interaction system

Human-machine interactions(HMIs)have advanced rapidly in recent decades in the fields of healthcare,work,and life.However,people with disabilities and other mobility problems do not have corresponding high-tech aids for them to enjoy the convenience of HMIs.In this paper,we propose a sensor with a wave-shaped(corrugated)electrode embedded in a friction layer,which exhibits high sensitivity to skin fold excitation and enormous potential in HMIs.Attributing to the wave-shaped electrode design,it has no built-in cavities,and its small size allows it to flexibly cope with folds at different angles.By specifying the carbon nanotube hybrid silicone film as the electrode layer material and silicone film as the friction layer,good electrical output performance,tensile properties,and biocompatibility can be achieved.Then,the sensor is tested on various joints and skin folds of the human body,the output signals of which can be distinguished between normal physiological behavior and test behavior.Based on this sensor,we designed a medical alarm system,a robotic arm assistive system,and a cell phone application control system for the disabled to help them in the fields of healthcare,work,and life.In conclusion,our research presents a feasible technology to enhance HMIs and makes a valuable contribution to the development of high-tech aids for the disabled.

Machine learning and human‐machine trust in healthcare:A systematic survey

As human‐machine interaction(HMI)in healthcare continues to evolve,the issue of trust in HMI in healthcare has been raised and explored.It is critical for the development and safety of healthcare that humans have proper trust in medical machines.Intelligent machines that have applied machine learning(ML)technologies continue to penetrate deeper into the medical environment,which also places higher demands on intelligent healthcare.In order to make machines play a role in HMI in healthcare more effectively and make human‐machine cooperation more harmonious,the authors need to build good humanmachine trust(HMT)in healthcare.This article provides a systematic overview of the prominent research on ML and HMT in healthcare.In addition,this study explores and analyses ML and three important factors that influence HMT in healthcare,and then proposes a HMT model in healthcare.Finally,general trends are summarised and issues to consider addressing in future research on HMT in healthcare are identified.

Human-machine interactive streaming anomaly detection by online self-adaptive forest

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

Human-machine interaction force control: using a model-referenced adaptive impedance device to control an index finger exoskeleton

Exoskeleton robots and their control methods have been extensively developed to aid post-stroke rehabilitation. Most of the existing methods using linear controllers are designed for position control and are not suitable for human-machine interaction(HMI) force control, as the interaction system between the human body and exoskeleton is uncertain and nonlinear. We present an approach for HMI force control via model reference adaptive impedance control(MRAIC) to solve this problem in case of index finger exoskeleton control. First, a dynamic HMI model, which is based on a position control inner loop, is formulated. Second, the theoretical MRAC framework is implemented in the control system. Then, the adaptive controllers are designed according to the Lyapunov stability theory. To verify the performance of the proposed method, we compare it with a proportional-integral-derivative(PID) method in the time domain with real experiments and in the frequency domain with simulations. The results illustrate the effectiveness and robustness of the proposed method in solving the nonlinear HMI force control problem in hand exoskeleton.

Research of Natural Gesture Recognition and Interactive Technology Compatible with YCb Crand HSV Color Space

In view of the current gesture recognition algorithm based on skin color segmentation is not flexible and has weak resistance to the environment, this paper puts forward a new method of skin color modeling to improve the adaptability of gesture segmentation when it face to different states. The modeling built by double color space instead of only one is compatible both in YCbCr and HSV color space to training the Gaussian model which can update the threshold value for binarization. Finally, this paper designed a natural gesture recognition and interactive systems based on the double color space model. It has shown that the system has a good interactive experience in different environments.

High-performance optical noncontact controlling system based on broadband PtTex/Si heterojunction photodetectors for human-machine interaction

Noncontact interaction systems have attracted considerable research attention in recent years because of convenient operation,sterility,and injury prevention.However,the insufficient sensing distance and weak robustness of noncontact interaction systems for complex environments limit their practical applications.Here,we designed an integrated optical noncontact controlling system(ONCS)based on PtTe_(x)/Si optoelectronic heterojunction array.Broadband sensitive photoresponse is realized at zero bias voltage,with excellent detectivity and responsivity,boosting the noncontact sensing distance to at least 150 mm.Consequently,the system can perform noncontact detection,encoding,and control by recognizing shadow-induced spatiotemporal sequence changes in heterojunction array photocurrents.As a proof of concept,different interactive functions have been demonstrated with good accuracy and robustness by encoding finger movement above the ONCS.This study provides a new perspective for constructing high-performance noncontact interaction systems.

Transparent,Ultra-Stretching,Tough,Adhesive Carboxyethyl Chitin/Polyacrylamide Hydrogel Toward High-Performance Soft Electronics

To date,hydrogels have gained increasing attentions as a flexible conductive material in fabricating soft electronics.However,it remains a big challenge to integrate multiple functions into one gel that can be used widely under various conditions.Herein,a kind of multifunc-tional hydrogel with a combination of desirable characteristics,including remarkable transparency,high conductivity,ultra-stretchability,tough-ness,good fatigue resistance,and strong adhesive ability is presented,which was facilely fabricated through multiple noncovalent crosslinking strategy.The resultant versatile sensors are able to detect both weak and large deformations,which owns a low detection limit of 0.1%strain,high stretchability up to 1586%,ultrahigh sensitivity with a gauge factor up to 18.54,as well as wide pressure sensing range(0-600 kPa).Meanwhile,the fabrication of conductive hydrogel-based sensors is demonstrated for various soft electronic devices,including a flexible human-machine interactive system,the soft tactile switch,an integrated electronic skin for unprecedented nonplanar visualized pressure sensing,and the stretchable triboelectric nanogenerators with excellent biomechanical energy harvesting ability.This work opens up a simple route for multifunctional hydrogel and promises the practical application of soft and self-powered wearable electronics in various complex scenes.

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

In the past decade,the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare,Internet of Things,human–machine interfaces,artificial intelligence and soft robotics.Among them,flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change.This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring.Four categories of humidity sensors are highlighted based on resistive,capacitive,impedance-type and voltage-type working mechanisms.Furthermore,typical strategies including chemical doping,structural design and Joule heating are introduced to enhance the performance of humidity sensors.Drawing on the noncontact perception capability,human/plant healthcare management,human-machine interactions as well as integrated humidity sensor-based feedback systems are presented.The burgeoning innovations in this research field will benefit human society,especially during the COVID-19 epidemic,where cross-infection should be averted and contactless sensation is highly desired.

Challenges of human-machine collaboration in risky decision-making

The purpose of this paper is to delineate the research challenges of human-machine collaboration in risky decision-making.Technological advances in machine intelligence have enabled a growing number of applications in human-machine collaborative decisionmaking.Therefore,it is desirable to achieve superior performance by folly leveraging human and machine capabilities.In risky decision-making,a human decisionmaker is vulnerable to cognitive biases when judging the possible outcomes of a risky event,whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well.We first summarize features of risky decision-making and possible biases of human decision-makers therein.Then,we argue the necessity and urgency of advancing human-machine collaboration in risky decision-making.Afterward,we review the literature on human-machine collaboration in a general decision context,from the perspectives of human-machine organization,relationship,and collaboration.Lastly,we propose challenges of enhancing human-machine communication and teamwork in risky decisionmaking,followed by future research avenues.

Learning a Deep Predictive Coding Network for a Semi-Supervised 3D-Hand Pose Estimation

In this paper we present a CNN based approach for a real time 3 D-hand pose estimation from the depth sequence.Prior discriminative approaches have achieved remarkable success but are facing two main challenges:Firstly,the methods are fully supervised hence require large numbers of annotated training data to extract the dynamic information from a hand representation.Secondly,unreliable hand detectors based on strong assumptions or a weak detector which often fail in several situations like complex environment and multiple hands.In contrast to these methods,this paper presents an approach that can be considered as semi-supervised by performing predictive coding of image sequences of hand poses in order to capture latent features underlying a given image without supervision.The hand is modelled using a novel latent tree dependency model(LDTM)which transforms internal joint location to an explicit representation.Then the modeled hand topology is integrated with the pose estimator using data dependent method to jointly learn latent variables of the posterior pose appearance and the pose configuration respectively.Finally,an unsupervised error term which is a part of the recurrent architecture ensures smooth estimations of the final pose.Experiments on three challenging public datasets,ICVL,MSRA,and NYU demonstrate the significant performance of the proposed method which is comparable or better than state-of-the-art approaches.