Risk identification and safety assessment of human-computer interaction in integrated avionics based on STAMP

To solve the problem of risk identification and quantitative assessment for human-computer interaction(HCI)in complex avionics systems,an HCI safety analysis framework based on system-theoretical process analysis(STPA)and cognitive reliability and error analysis method(CREAM)is proposed.STPACREAM can identify unsafe control actions and find the causal path during the interaction of avionics systems and pilot with the help of formal verification tools automatically.The common performance conditions(CPC)of avionics systems in the aviation environment is established and a quantitative analysis of human failure is carried out.Taking the head-up display(HUD)system interaction process as an example,a case analysis is carried out,the layered safety control structure and formal model of the HUD interaction process are established.For the interactive behavior“Pilots approaching with HUD”,four unsafe control actions and35 causal scenarios are identified and the impact of common performance conditions at different levels on the pilot decision model are analyzed.The results show that HUD's HCI level gradually improves as the scores of CPC increase,and the quality of crew member cooperation and time sufficiency of the task is the key to its HCI.Through case analysis,it is shown that STPACREAM can quantitatively assess the hazards in HCI and identify the key factors that impact safety.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides.However,the northwestern margin of this region,characterised by limited human activities and challenging transportation,remains insufficiently explored concerning landslide occurrence and dispersion.With the planning and construction of the Xinjiang-Tibet Railway,a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies.By using the human-computer interaction interpretation approach,the authors established a landslide database encompassing 13003 landslides,collectively spanning an area of 3351.24 km^(2)(36°N-40°N,73°E-78°E).The database incorporates diverse topographical and environmental parameters,including regional elevation,slope angle,slope aspect,distance to faults,distance to roads,distance to rivers,annual precipitation,and stratum.The statistical characteristics of number and area of landslides,landslide number density(LND),and landslide area percentage(LAP)are analyzed.The authors found that a predominant concentration of landslide origins within high slope angle regions,with the highest incidence observed in intervals characterised by average slopes of 20°to 30°,maximum slope angle above 80°,along with orientations towards the north(N),northeast(NE),and southwest(SW).Additionally,elevations above 4.5 km,distance to rivers below 1 km,rainfall between 20-30 mm and 30-40 mm emerge as particularly susceptible to landslide development.The study area’s geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops.Both fault and human engineering activities have different degrees of influence on landslide development.Furthermore,the significance of the landslide database,the relationship between landslide distribution and environmental factors,and the geometric and morphological characteristics of landslides are discussed.The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64.It means the landslides mobility in the region is relatively low,and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Modulatory role of plant-derived metabolites on host-microbiota interactions:personalized therapeutics outlook

A diverse array of microbes in and on the human body constitute the microbiota.These micro-residents continuously interact with the human host through the language of metabolites to dictate the host’s physiology in health and illnesses.Any biotic and abiotic component ensuring a balanced host-microbiota interaction are potential microbiome therapeutic agents to overcome human diseases.Plant metabolites are continually being used to treat various illnesses.These metabolites target the host’s metabolic machinery and host-gut microbiota interactions to overcome human diseases.Despite the paramount therapeutic significance of the factors affecting host-microbiota interactions,a comprehensive overview of the modulatory role of plant-derived metabolites in host-microbiota interactions is lacking.The current review puts an effort into comprehending the role of medicinal plants in gut microbiota modulation to mitigate various human illnesses.It would develop a holistic understanding of hostmicrobiota interactions and the role of effectors in health and diseases.

Effect of a Thermal Spring Water on Carbohydrate-Protein Interactions in In-Vitro Models Implicating Normal Human Keratinocytes and Recombinant Lectins

Background: Sugar moiety of macromolecules is today very well known for its implications in many biological recognition mechanisms including cell-cell, extracellular matrix-cell and/or bacteria-cell interactions. In this context lectins, which are carbohydrate-binding proteins displaying a high affinity for sugar groups of other molecules, are of a great importance, notably in immune response involving bacteria, viruses and fungi. As protein-carbohydrate interactions are often mediated by ions such as calcium, zinc or magnesium, we were prompted to study the effect of a thermal spring water (which contains this type of component) on interactions existing between: 1) osidic receptors of human normal keratinocytes and 2) two lectins greatly implicated in the immune response mechanisms (i.e. the dectin-1 and the langerin), and their ligands. Materials and Methods: In a first series of experiments, we studied the effect of increasing concentrations of a thermal spring water on interactions existing between glycosylated molecules and the osidic receptors expressed at the normal human keratinocytes surface. In a second step, and in order to better understand the putative effect of our thermal spring water on the immune response, we analyzed its effect on the interactions existing between the dectin-1 (implicated in the recognition of bacteria, viruses and fungi) and the langerin (expressed by Langerhans cells, the immune cells of the cutaneous tissue), and their ligands in a model using recombinant human lectins and appropriate binding molecules. Results: We showed here that our thermal spring water was able to reinforce interactions between keratinocytes osidic receptors and some of their ligands, in a dose-related manner: From 8% to 55% of increase with 10% to 30% (v/v) of thermal spring water. In the second part of our studies, we also showed that our thermal spring water was able to modulate interactions between dectin-1 and langerin and their ligands through a biphasic effect: Interactions were enhanced by more than 40% and 20% respectively with 10% of thermal spring water, and return to their basal level or lower for higher concentrations. Conclusion: The tested thermal spring water, probably due to its ionic composition, could significantly affect interactions of osidic receptors with their ligands. This property could be of a great interest to help immune system to maintain an appropriate “vigilance state” by using the thermal water at up to a concentration of 10%, and by avoiding any runaway reaction in case of aggression, by using concentrations higher than 10%. .

Fusion of Convolutional Self-Attention and Cross-Dimensional Feature Transformationfor Human Posture Estimation

Human posture estimation is a prominent research topic in the fields of human-com-puter interaction,motion recognition,and other intelligent applications.However,achieving highaccuracy in key point localization,which is crucial for intelligent applications,contradicts the lowdetection accuracy of human posture detection models in practical scenarios.To address this issue,a human pose estimation network called AT-HRNet has been proposed,which combines convolu-tional self-attention and cross-dimensional feature transformation.AT-HRNet captures significantfeature information from various regions in an adaptive manner,aggregating them through convolu-tional operations within the local receptive domain.The residual structures TripNeck and Trip-Block of the high-resolution network are designed to further refine the key point locations,wherethe attention weight is adjusted by a cross-dimensional interaction to obtain more features.To vali-date the effectiveness of this network,AT-HRNet was evaluated using the COCO2017 dataset.Theresults show that AT-HRNet outperforms HRNet by improving 3.2%in mAP,4.0%in AP75,and3.9%in AP^(M).This suggests that AT-HRNet can offer more beneficial solutions for human posture estimation.

Exploiting Human Pose and Scene Information for Interaction Detection

Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social robotics.It enhances systems’ability to interpret and respond to human behavior precisely.This research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse actions.The overall purpose of this study is to develop a robust and accurate system for human interaction recognition.This research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out scenes.Gaussian filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical method.Feature extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)techniques.The application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification process.It ensures that the final features loaded into the HMM classifier accurately represent the relevant human activities.The impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed technique.The proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM classification.This enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

Shaping a New Level of Bus Service under a Novel Concept of Bus Interaction: A Meta-Review

Public transport services, particularly bus services, play an important role in a sustainable transportation system. However, despite various efforts, bus ridership has decreased. The appearance of shared and on-demand vehicle services is one of the main reasons for this issue. In addition, bus tourism services have been successfully developed to meet the exigent needs of tourists. Therefore, a new level of daily bus service is necessary to adapt to the changing demands of customers. Bus interaction (BI) plays an important role in bus services. Nevertheless, the conventional concept of BI mainly refers to users, physical interaction, and safety, but it does not address non-users, non-physical interactions, service quality, and other aspects. This study aims to elaborate on a new concept of bus services. Based on this, we developed a theoretical framework for BI. A meta-analysis was then conducted to identify the achievements and untouched aspects. The results of this study provide three main contributions. First, an unprecedented novel concept of BI is defined, including 13 types of interactions. Second, a comprehensive theoretical framework of BI is established based on the relationships between eight sustainable bus system sub-aspects and 13 BI types. Third, based on the theoretical framework and findings of the reviewed studies, a common finding comprehensive framework of BI is completed, which is accompanied by 1) key findings of the 13 BI types, 2) conclusions of traffic conditions affecting BI research, 3) BI research gaps, and 4) 16 main suggestions for future BI research.

Implementation of Level-3 Autonomous Patient-Specific Quality Assurance with Automated Human Interactive Devices

Purpose: Patient-specific quality assurance (PSQA) requires manual operation of different workstations, which is time-consuming and error-prone. Therefore, developing automated solutions to improve efficiency and accuracy is a priority. The purpose of this study was to develop a general software interface with scripting on a human interactive device (HID) for improving the efficiency and accuracy of manual quality assurance (QA) procedures. Methods: As an initial application, we aimed to automate our PSQA workflow that involves Varian Eclipse treatment planning system, Elekta MOSAIQ oncology information system and PTW Verisoft application. A general platform, the AutoFrame interface with two imbedded subsystems—the AutoFlow and the PyFlow, was developed with a scripting language for automating human operations of aforementioned systems. The interface included three functional modules: GUI module, UDF script interpreter and TCP/IP communication module. All workstations in the PSQA process were connected, and most manual operations were automated by AutoFrame sequentially or in parallel. Results: More than 20 PSQA tasks were performed both manually and using the developed AutoFrame interface. On average, 175 (±12) manual operations of the PSQA procedure were eliminated and performed by the automated process. The time to complete a PSQA task was 8.23 (±0.78) minutes for the automated workflow, in comparison to 13.91 (±3.01) minutes needed for manual operations. Conclusion: We have developed the AutoFrame interface framework that successfully automated our PSQA procedure, and significantly reduced the time, human (control/clicking/typing) errors, and operators’ stress. Future work will focus on improving the system’s flexibility and stability and extending its operations to other QA procedures.

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 of 14-3-3σ with KCMF1 suppresses the proliferation and colony formation of human colon cancer stem cells

AIM: To investigate the biological function of 14-3-3σ protein and to look for proteins that interact with 14-3-3σ protein in colon cancer stem cells. METHODS: Reverse transcription polymerase chain reaction was performed to amplify the 14-3-3σ gene from the mRNA of colon cancer stem cells. The gene was then cloned into the pGEM-T vector. After being sequenced, the target gene 14-3-3σ was cut from the pGEM-T vector and cloned into the pGBKT7 yeast expression plasmid. Then, the bait plasmid pGBKT7-14-3-3σ was transformed into the yeast strain AH109. After the expression of the pGBKT7-14-3-3σ fusion protein in the AH109 yeast strain was accomplished, a yeast two-hybrid screening assay was performed by mating AH109 with Y187 that contained a HeLa cDNA library plasmid. The interaction between the 14-3-3σ protein and the proteins obtained from positive colonies was further confirmed by repeating the yeast two-hybridscreen. After extracting and sequencing the plasmids from the positive colonies, we performed a bioinformatics analysis. A coimmunoprecipitation assay was performed to confirm the interaction between 14-3-3σ and the proteins obtained from the positive colonies. Finally, we constructed 14-3-3σ and potassium channel modulatory factor 1 (KCMF1) siRNA expression plasmids and transfected them into colon cancer stem cells. RESULTS: The bait plasmid pGBKT7-14-3-3σ was constructed successfully, and the 14-3-3σ protein had no toxic or autonomous activation effect on the yeast. Nineteen true-positive colonies were selected and sequenced, and their full-length sequences were obtained. We searched for homologous DNA sequences for these sequences from GenBank. Among the positive colonies, four coding genes with known functions were obtained, including KCMF1 , quinone oxidore-ductase (NQO2 ), hydroxyisobutyrate dehydrogenase (HIBADH ) and 14-3-3σ . For the subsequent coimmu-noprecipitation assay, the plasmids PCDEF-Flag-14-3-3σ, PCDEF-Myc-KCMF1, PCDEF-Myc-NQO2 and PCDEF-Myc-HIBADH were successfully constructed, and the sequences were further confirmed by DNA sequencing. The Fugene 6 reagent was used to transfect the plasmids, and fluorescence-activated cell sorting analysis showed the transfection efficiency was 97.8% after 48 h. The HEK 293FT cells showed the stable expression of the PCDEF-Flag-14-3-3σ, PCDEF-Myc-KCMF1, PCDEF-Myc-NQO2 and PCDEF-Myc-HIBADH plasmids. After anti-Myc antibody immunoprecipitation with Myc-KCMF1, Myc-NQO2 and Myc-HIBADH from cell lysates, the presence of Flag-14-3-3σ protein in the immuno-precipitated complex was determined by western blot analysis. The knock-down expression of the 14-3-3σ and KCMF1 proteins significantly inhibited cell proliferation and colony formation of SW1116csc. CONCLUSION: Genes of the proteins that interactedwith 14-3-3σ were successfully screened from a HeLa cDNA library. KCMF1 and 14-3-3σ protein may affect the proliferation and colony formation of human colon cancer stem cells.

RFHUI:an RFID based human-unmanned aerial vehicle interaction system in an indoor environment

In this paper,we present an RFID based human and Unmanned Aerial Vehicle(UAV)Interaction system,termed RFHUI,to provide an intuitive and easy-to-operate method to navigate a UAV in an indoor environment.It relies on the passive Radio-Frequency IDentification(RFID)technology to precisely track the pose of a handheld controller,and then transfer the pose information to navigate the UAV.A prototype of the handheld controller is created by attaching three or more Ultra High Frequency(UHF)RFID tags to a board.A Commercial Off-The-Shelf(COTS)RFID reader with multiple antennas is deployed to collect the observations of the tags.First,the precise positions of all the tags can be obtained by our proposed method,which leverages a Bayesian filter and Channel State Information(CSI)phase measurements collected from the RFID reader.Second,we introduce a Singular Value Decomposition(SVD)based approach to obtain a 6-DoF(Degrees of Freedom)pose of the controller from estimated positions of the tags.Furthermore,the pose of the controller can be precisely tracked in a real-time manner,while the user moves the controller.Finally,control commands will be generated from the controller's pose and sent to the UAV for navigation.The performance of the RFHUI is evaluated by several experiments.The results show that it provides precise poses with 0.045m mean error in position and 2.5∘mean error in orientation for the controller,and enables the controller to precisely and intuitively navigate the UAV in an indoor environment.

Review of human-animal interactions and their impact on animal productivity and welfare

Humans and animals are in regular and at times close contact in modern intensive farming systems.The quality of human-animal interactions can have a profound impact on the productivity and welfare of farm animals.Interactions by humans may be neutral,positive or negative in nature.Regular pleasant contact with humans may result in desirable alterations in the physiology,behaviour,health and productivity of farm animals.On the contrary,animals that were subjected to aversive human contact were highly fearful of humans and their growth and reproductive performance could be compromised.Farm animals are particularly sensitive to human stimulation that occurs early in life,while many systems of the animals are still developing.This may have long-lasting impact and could possibly modify their genetic potential.The question as to how human contact can have a positive impact on responses to stressors,and productivity is not well understood.Recent work in our laboratory suggested that pleasant human contact may alter ability to tolerate various stressors through enhanced heat shock protein（hsp） 70 expression.The induction of hsp is often associated with increased tolerance to environmental stressors and disease resistance in animals.The attitude and consequent behaviour of stockpeople affect the animals＇ fear of human which eventually influence animals＇ productivity and welfare.Other than attitude and behaviour,technical skills,knowledge,job motivation,commitment and job satisfaction are prerequisites for high job performance.

A Facial Expression Emotion Recognition Based Human-robot Interaction System

A facial expression emotion recognition based human-robot interaction(FEER-HRI) system is proposed, for which a four-layer system framework is designed. The FEERHRI system enables the robots not only to recognize human emotions, but also to generate facial expression for adapting to human emotions. A facial emotion recognition method based on2D-Gabor, uniform local binary pattern(LBP) operator, and multiclass extreme learning machine(ELM) classifier is presented,which is applied to real-time facial expression recognition for robots. Facial expressions of robots are represented by simple cartoon symbols and displayed by a LED screen equipped in the robots, which can be easily understood by human. Four scenarios,i.e., guiding, entertainment, home service and scene simulation are performed in the human-robot interaction experiment, in which smooth communication is realized by facial expression recognition of humans and facial expression generation of robots within 2 seconds. As a few prospective applications, the FEERHRI system can be applied in home service, smart home, safe driving, and so on.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.

Interpreting and Extracting Open Knowledge for Human-Robot Interaction

A more natural way for non-expert users to express their tasks in an open-ended set is to use natural language. In this case,a human-centered intelligent agent/robot is required to be able to understand and generate plans for these naturally expressed tasks. For this purpose, it is a good way to enhance intelligent robot's abilities by utilizing open knowledge extracted from the web, instead of hand-coded knowledge. A key challenge of utilizing open knowledge lies in the semantic interpretation of the open knowledge organized in multiple modes, which can be unstructured or semi-structured, before one can use it.Previous approaches used a limited lexicon to employ combinatory categorial grammar(CCG) as the underlying formalism for semantic parsing over sentences. Here, we propose a more effective learning method to interpret semi-structured user instructions. Moreover, we present a new heuristic method to recover missing semantic information from the context of an instruction. Experiments showed that the proposed approach renders significant performance improvement compared to the baseline methods and the recovering method is promising.

An Architecture Supporting Intelligent Mobile Healthcare Using Human-Computer Interaction HCI Principles

Recent advancements in the Internet of Things IoT and cloud computing have paved the way for mobile Healthcare(mHealthcare)services.A patient within the hospital is monitored by several devices.Moreover,upon leaving the hospital,the patient can be remotely monitored whether directly using body wearable sensors or using a smartphone equipped with sensors to monitor different user-health parameters.This raises potential challenges for intelligent monitoring of patient's health.In this paper,an improved architecture for smart mHealthcare is proposed that is supported by HCI design principles.The HCI also provides the support for the User-Centric Design(UCD)for smart mHealthcare models.Furthermore,the HCI along with IoT's(Internet of Things)5-layered architecture has the potential of improving User Experience(UX)in mHealthcare design and help saving lives.The intelligent mHealthcare system is supported by the IoT sensing and communication layers and health care providers are supported by the application layer for the medical,behavioral,and health-related information.Health care providers and users are further supported by an intelligent layer performing critical situation assessment and performing a multi-modal communication using an intelligent assistant.The HCI design focuses on the ease-of-use,including user experience and safety,alarms,and error-resistant displays of the end-user,and improves user's experience and user satisfaction.

Empathic Responses of Behavioral-Synchronization in Human-Agent Interaction

Artificial entities,such as virtual agents,have become more pervasive.Their long-term presence among humans requires the virtual agent’s ability to express appropriate emotions to elicit the necessary empathy from the users.Affective empathy involves behavioral mimicry,a synchronized co-movement between dyadic pairs.However,the characteristics of such synchrony between humans and virtual agents remain unclear in empathic interactions.Our study evaluates the participant’s behavioral synchronization when a virtual agent exhibits an emotional expression congruent with the emotional context through facial expressions,behavioral gestures,and voice.Participants viewed an emotion-eliciting video stimulus(negative or positive)with a virtual agent.The participants then conversed with the virtual agent about the video,such as how the participant felt about the content.The virtual agent expressed emotions congruent with the video or neutral emotion during the dialog.The participants’facial expressions,such as the facial expressive intensity and facial muscle movement,were measured during the dialog using a camera.The results showed the participants’significant behavioral synchronization(i.e.,cosine similarity≥.05)in both the negative and positive emotion conditions,evident in the participant’s facial mimicry with the virtual agent.Additionally,the participants’facial expressions,both movement and intensity,were significantly stronger in the emotional virtual agent than in the neutral virtual agent.In particular,we found that the facial muscle intensity of AU45(Blink)is an effective index to assess the participant’s synchronization that differs by the individual’s empathic capability(low,mid,high).Based on the results,we suggest an appraisal criterion to provide empirical conditions to validate empathic interaction based on the facial expression measures.

Teaching the User By Learning From the User:Personalizing Movement Control in Physical Human-robot Interaction

This paper proposes a novel approach for physical human-robot interactions(pHRI), where a robot provides guidance forces to a user based on the user performance. This framework tunes the forces in regards to behavior of each user in coping with different tasks, where lower performance results in higher intervention from the robot. This personalized physical human-robot interaction(p2HRI) method incorporates adaptive modeling of the interaction between the human and the robot as well as learning from demonstration(LfD) techniques to adapt to the users' performance. This approach is based on model predictive control where the system optimizes the rendered forces by predicting the performance of the user. Moreover, continuous learning of the user behavior is added so that the models and personalized considerations are updated based on the change of user performance over time. Applying this framework to a field such as haptic guidance for skill improvement, allows a more personalized learning experience where the interaction between the robot as the intelligent tutor and the student as the user,is better adjusted based on the skill level of the individual and their gradual improvement. The results suggest that the precision of the model of the interaction is improved using this proposed method,and the addition of the considered personalized factors to a more adaptive strategy for rendering of guidance forces.