Human-like Behavior Research Articles

Quality Assessment of Virtual Human Assistants for Elder Users

Virtual humans (VHs) are gaining increasing attention in various fields, including games and cultural heritage and technological contexts including virtual reality and augmented reality. Recently, since VHs can simulate human-like behavior, VHs have been proposed as virtual assistants (VAs) for all sorts of education and training applications, including applications focused on the improvement of quality of life (QoL) and well-being. In this research work, we consider the quality and efficiency of VHs implemented as part of the MyHealthWatcher project, which focuses on the monitoring of health-related parameters of elder users to improve their QoL and self-management of chronic conditions. To validate our hypothesis that the increased quality of the VH has a positive effect on user satisfaction and user quality of interaction with the system, we developed and integrated into the MyHealthWatcher system two VH variations. The first was developed with mainstream technologies and the second was developed using a professional pipeline. The two variations developed were assessed by representative target users through a between-subject focus group study. The development and validation process of the two variations allowed us to draw valuable conclusions, which are discussed in this paper.

Bio-inspired cognitive architecture of episodic memory

Memory and learning are essential functions in human beings as they allow us to acquire and store in the brain representations of thoughts, experiences, and behaviors, which are required for problem-solving in our daily life and mainly for survival. Episodic memory is a type of memory that provides the ability to re-experience events in one’s life, and it is associated with their conscious recollection. Since episodic memory can represent our experiences about the environment, similar to a mental journey, it is desired in systems that attempt to create human-like behavior. Currently, the main problem is that state of the art proposals do not consider neuroscientific evidence like memory dynamics for forgetting or bottom-up inputs, and most of them do not consider episodic memory as a different memory but as part of general declarative memory. We consider these omissions to limit the generation of human-like behavior. In this work, we propose a bio-inspired cognitive architecture of episodic memory. Neuroscientific evidence provides us with the brain structures associated with this type of memory, the connections, and the operations these areas perform. We hypothesize that virtual entities endowed with our episodic memory cognitive architecture will plan and make decisions in a more human-like fashion. To test the capabilities of the proposal, we endowed a virtual creature with a distributed and concurrent implementation of our architecture, and it was given two tasks. The first task validated the functions of the memory independently, and in the second task, the creature used episodic memory to solve a planning problem. From the results of these experiments, we validate our proposal and show that it is possible to create a system that behaves as the human brain does.

Modeling Driver’s Visual Fixation Behavior Using White-Box Representations

Driving is a highly dynamic task requiring extensive and fast processing of visuospatial information in brief time intervals, while experienced drivers can efficiently perform this task. Therefore, investigating the human-like visual fixation behavior shows much potential in developing the advanced driving assistance systems and efficient autonomous vehicles. Literature shows that driver’s visual fixation behavior is guided by a combination of top-down and bottom-up mechanisms. Currently, existing excellent computational frameworks rely heavily on the deep neural network’s strong feature extraction ability, which is inherently task-unaware. These black-box models are limited in strengthening the guidance of task-aware top-down factors in governing the fixation behavior. Furthermore, using the black-box model to fit the training data directly decreases its interpretability. In this paper, we encourage to adopt the white-box method. By researching the findings in the intersection of neuroscience and psychology, we propose to identify two white-box representations for driving separately: the information selection representation including salience, effort, expectancy, and value, and the driving task-aware representation highlighting sub-task and speed. We present a novel computational framework to incorporate all the above factors to predict driver’s visual fixation systematically. On the BDDA dataset, we have conducted the extensive quantitative evaluation and qualitative analysis to validate the effectiveness. Its performance surpasses the previous state-of-the-art by +11.3% in the Pearson’s Correlation Coefficient, especially by a large margin of +24.5% and +28.8% in the turning operations. The ablation study further demonstrates the influence of each component in our framework. Moreover, the trained model has been deployed on the DR(eye)VE dataset to verify the generalization ability, demonstrating the potential of domain adaptation.

Yapay Zekâ Teknolojisinin Perinatal Dönem Bakımına Entegrasyonu ve Uygulama Örnekleri

Yapay Zekâ (YZ) kavramı, insan gibi davranışlar sergileme, sayısal mantık yürütme, hareket, konuşma ve ses tanıma gibi birçok yeteneğe sahip yazılımsal ve donanımsal sistemler bütünüdür. YZ konusundaki gelişmeler, sağlık profesyonellerinin tanı, tedavi, bakım hizmetlerini daha verimli ve güvenli bir şekilde sunmalarına destek olmaktadır. Yirmi birinci yüzyılda robot teknolojisi ve yapay zekânın gelişmesi perinatal dönem uygulamalarını da etkilemektedir.
 Perinatal dönemde YZ teknolojisi; gebelik, doğum ve doğum sonu dönemlerindeki sağlık ve hastalık kavramlarını kapsamaktadır. Perinatal dönemde oldukça sık kullanılan yapay zekâ teknolojisinin temel amaçları; anne ve fetüsün sağlığının geliştirilmesi, gebelik takiplerinin düzenli yapılması, prenatal tarama testlerinde maliyetin azaltılması olarak belirlenmektedir. Bu amaçları desteklemek için geliştirilen YZ teknolojileri; gebelikte riskli durumların taranması ve yönetimini, genetik tarama süreçlerini, uzaktan gebelik takibini, erken teşhis- tedavi uygulamalarını ve postpartum dönem takip süreçlerini kapsamaktadır. 
 Perinatal dönemde geliştirilen YZ teknolojilerinin etkin ve verimli olabilmesi için bu alandaki sağlık profesyonellerinin, teknolojilerin geliştirilmesi ve kullanımında aktif rol almaları önemlidir. Günümüzde bu alanda rol alan sağlık profesyonelleri, yapay zekâ teknolojilerinin hizmet sürecine entegrasyonunda kimi zaman zorluklarla karşı karşıya kalmaktadır. 
 Bu çalışmada, yapay zekâ teknolojisinin perinatal dönemde kullanımına, hizmet sürecinde yapay zekâ temelli uygulama örneklerine ve bu teknolojilerin kullanılması ile ortaya çıkabilecek etik sorunlara ilişkin güncel bilgilere yer vermek amaçlanmıştır.

Young children’s interactions with a social robot during a drawing task

ABSTRACT Social robots communicate through human-like behaviours and are being used in preschool settings. However, the ways in which young children interact with social robots and how these impact upon early educational experiences are not fully known. This observational study explored preschoolers’ (Mean age = 4.58 years; N = 40) interactions with a social robot called NAO during a drawing activity, in South East Queensland, Australia. Most children (83%) engaged in the drawing activity and 60% talked to the social robot. A smaller proportion (10%) of children did not talk to the social robot or attempt the drawing task. Overall, the social robot successfully engaged and instructed preschool children during the drawing task however, not all children interacted with the social robot. It is recommended that a thoughtful approach to introducing social robots to preschools is needed to meet the individual and personal learning needs of young children.

Will artificial intelligence help us in predicting outcomes in cardiac surgery?

Will artificial intelligence help us in predicting outcomes in cardiac surgery?

Optimum trajectory learning in musculoskeletal systems with model predictive control and deep reinforcement learning

From the computational point of view, musculoskeletal control is the problem of controlling high degrees of freedom and dynamic multi-body system that is driven by redundant muscle units. A critical challenge in the control perspective of skeletal joints with antagonistic muscle pairs is finding methods robust to address this ill-posed nonlinear problem. To address this computational problem, we implemented a twofold optimization and learning framework to be specialized in addressing the redundancies in the muscle control . In the first part, we used model predictive control to obtain energy efficient skeletal trajectories to mimick human movements. The second part is to use deep reinforcement learning to obtain a sequence of stimulus to be given to muscles in order to obtain the skeletal trajectories with muscle control. We observed that the desired stimulus to muscles is only efficiently constructed by integrating the state and control input in a closed-loop setting as it resembles the proprioceptive integration in the spinal cord circuits. In this work, we showed how a variety of different reference trajectories can be obtained with optimal control and how these reference trajectories are mapped to the musculoskeletal control with deep reinforcement learning. Starting from the characteristics of human arm movement to obstacle avoidance experiment, our simulation results confirm the capabilities of our optimization and learning framework for a variety of dynamic movement trajectories. In summary, the proposed framework is offering a pipeline to complement the lack of experiments to record human motion-capture data as well as study the activation range of muscles to replicate the specific trajectory of interest. Using the trajectories from optimal control as a reference signal for reinforcement learning implementation has allowed us to acquire optimum and human-like behaviour of the musculoskeletal system which provides a framework to study human movement in-silico experiments. The present framework can also allow studying upper-arm rehabilitation with assistive robots given that one can use healthy subject movement recordings as reference to work on the control architecture of assistive robotics in order to compensate behavioural deficiencies. Hence, the framework opens to possibility of replicating or complementing labour-intensive, time-consuming and costly experiments with human subjects in the field of movement studies and digital twin of rehabilitation.

Imitating the Human. New Human–Machine Interactions in Social Robots

Social robots are designed to perform intelligent, emotional, and autonomous behavior in order to establish intimate relationships with humans, for instance, in the context of elderly care. However, the imitation of qualities usually assumed to be necessary for human reciprocal interaction may impact our understanding of social interaction. Against this background, we compare the technical operations based on which social robots imitate human-like behavior with the concepts of emotionality, intelligence, and autonomy as usually attached to humans. In doing so, we aim at making a conceptual contribution to the intuition that the technical imitation of emotional, intelligent, and autonomous behavior differs qualitatively from human forms of emotionality, intelligence, and autonomy. We conclude that the increasing tendency of social robots to imitate human characteristics and the thus increased human tendency of anthropomorphizing technical systems put forth new forms of human–machine interactions (HMI) that call for a critical examination. Thus, the emerging forms of interaction may lead, for instance, to false, reinforced, and perplexing assumptions regarding reciprocity. Therefore, studies are needed that critically examine the potential consequences and psychological effects that may be associated with the use of social robots, especially with regard to vulnerable groups such as children or the elderly.

User Profiling to Enhance Clinical Assessment and Human–Robot Interaction: A Feasibility Study

Socially Assistive Robots (SARs) are designed to support us in our daily life as a companion, and assistance but also to support the caregivers’ work. SARs should show personalized and human-like behavior to improve their acceptance and, consequently, their use. Additionally, they should be trustworthy by caregivers and professionals to be used as support for their work (e.g. objective assessment, decision support tools). In this context the aim of the paper is dual. Firstly, this paper aims to present and discuss the robot behavioral model based on sensing, perception, decision support, and interaction modules. The novel idea behind the proposed model is to extract and use the same multimodal features set for two purposes: (i) to profile the user, so to be used by the caregiver as a decision support tool for the assessment and monitoring of the patient; (ii) to fine-tune the human–robot interaction if they can be correlated to the social cues. Secondly, this paper aims to test in a real environment the proposed model using a SAR robot, namely ASTRO. Particularly, it measures the body posture, the gait cycle, and the handgrip strength during the walking support task. Those collected data were analyzed to assess the clinical profile and to fine-tune the physical interaction. Ten older people (65.2 ± 15.6 years) were enrolled for this study and were asked to walk with ASTRO at their normal speed for 10 m. The obtained results underline a good estimation (p < 0.05) of gait parameters, handgrip strength, and angular excursion of the torso with respect to most used instruments. Additionally, the sensory outputs were combined in the perceptual model to profile the user using non-classical and unsupervised techniques for dimensionality reduction namely T-distributed Stochastic Neighbor Embedding (t-SNE) and non-classic multidimensional scaling (nMDS). Indeed, these methods can group the participants according to their residual walking abilities.

Benefits of the Cybathlon 2020 experience for a prosthetic hand user: a case study on the Hannes system

BackgroundCybathlon championship aims at promoting the development of prosthetic and assistive devices capable to meet users’ needs. This paper describes and analyses possible exploitation outcomes of our team’s (REHAB TECH) experience into the Powered Arm Prosthesis Race of the Cybathlon 2020 Global Edition, with the novel prosthetic system Hannes. In detail, we present our analysis on a concurrent evaluation conducted to verify if the Cybathlon training and competition positively influenced pilot’s performance and human-technology integration with Hannes, with respect to a non-runner Hannes user.MethodsTwo transradial amputees were recruited as pilots (Pilot 1 and Pilot 2) for the Cybathlon competition and were given the polyarticulated myoelectric prosthetic hand Hannes. Due to COVID-19 emergency, only Pilot 1 was trained for the race. However, both pilots kept Hannes for Home Use for seven weeks. Before this period, they both participated to the evaluation of functionality, embodiment, and user experience (UX) related to Hannes, which they repeated at the end of the Home Use and right after the competition. We analysed Pilot 1’s training and race outcomes, as well as changes in the concurrent evaluation, and compared these results with Pilot 2’s ones.ResultsThe Cybathlon training gradually improved Pilot 1’s performances, leading to the sixth place with a single error in task 5. In the parallel evaluation, both pilots had an overall improvement over time, whereas Pilot 2 experienced a deterioration of embodiment. In detail, Pilot 1, who followed the training and raced the Cybathlon, improved in greater way.ConclusionHannes demonstrated to be a valuable competitor and to perform grasps with human-like behaviors. The higher improvements of Pilot 1, who actively participated in the Cybathlon, in terms of functionality, embodiment and UX, may depend on his training and engagement in the effort of achieving a successful user-prosthesis interaction during the competition. Tasks based on Cybathlon’s ones could improve the training phase of a prosthetic user, stimulating dexterity, prosthetic integration, and user perception towards the prosthesis. Likewise, timed races or competitions could facilitate and accelerate the learning phase, improving the efficiency and efficacy of the process.

감정에 기반한 가상인간의 대화 및 표정 실시간 생성 시스템 구현

Virtual humans are implemented with dedicated modeling tools like Unity 3D Engine in virtual space (virtual reality, mixed reality, metaverse, etc.). Various human modeling tools have been introduced to implement virtual human-like appearance, voice, expression, and behavior similar to real people, and virtual humans implemented via these tools can communicate with users to some extent. However, most of the virtual humans so far have stayed unimodal using only text or speech. As AI technologies advance, the outdated machine-centered dialogue system is now changing to a human-centered, natural multi-modal system. By using several pre-trained networks, we implemented an emotion-based multi-modal dialogue system, which generates human-like utterances and displays appropriate facial expressions in real-time.

Human-Robot Variable Impedance Skills Transfer Learning Based on Dynamic Movement Primitives

Endowing robots with human-like abilities to perform motor skills smoothly and naturally is one of the important goals of robotics. Learning from demonstration (LfD) has been successfully applied for learning tasks on robots, for which the human tutor can demonstrate a successful execution. Learning human stiffness schedule strategy is one of the promising approaches for lightweight collaborative robots to execute physical in-contact tasks while remaining compliant when possible, as it enables robots to show human-like adaptive impedance behavior in an effective and efficient manner. In this letter, we develop a robot skill learning framework considering both movement and impedance features. Electromyography (EMG)-based method is used to estimate human upper limb stiffness such that the information of impedance can be obtained. Dynamic movement primitives (DMPs) model is employed to model movement and impedance features simultaneously. Throughout this procedure, the robot skills learning from a human tutor can be achieved. Besides, the learned skills can be generalized. An impedance controller imitating the human impedance mechanism is utilized in the task reproduction phase to achieve both task successful execution and safe physical interaction. The effectiveness of the proposed methods is verified in a water pumping task on a Kinova robot.

Building Metric-Topological Map to Efficient Object Search for Mobile Robot

In this article, an effective solution is presented for efficient robotic object search by leveraging metric-topological map. Based on the reference of objects, we construct a novel metric-topological map that is particularly suitable for object search, where the topological nodes represent the object-related locations in the environment. To effectively determine the creation of nodes on the map, a rapid adjustment method of the robot viewing angle is introduced. Compared with traditional maps, the map built in this article not only removes redundant nodes irrelevant to objects, but also facilitates the mobile robot to locate the target object. Besides, inspired by human search behavior, a search strategy is proposed by considering the tradeoff between the current position of the robot and the distance from the robot to the promising location of finding the target object, in order to further improve the search efficiency. The effectiveness and efficiency of our solution are verified through comparative experiments in both simulation and real-world environments. Results show that the presented approach always allows the mobile robot to find the target object efficiently and reliably while achieving humanlike search behavior.

How Human-like Behavior of Service Robot Affects Social Distance: A Mediation Model and Cross-Cultural Comparison.

Previous studies on the human likeness of service robots have focused mainly on their human-like appearance and used psychological constructs to measure the outcomes of human likeness. Unlike previous studies, this study focused on the human-like behavior of the service robot and used a sociological construct, social distance, to measure the outcome of human likeness. We constructed a conceptual model, with perceived competence and warmth as mediators, based on social-identity theory. The hypotheses were tested through online experiments with 219 participants from China and 180 participants from the US. Similar results emerged for Chinese and American participants in that the high (vs. low) human-like behavior of the service robot caused the participants to have stronger perceptions of competence and warmth, both of which contributed to a smaller social distance between humans and service robots. Perceptions of competence and warmth completely mediated the positive effect of the human-like behavior of the service robot on social distance. Furthermore, Chinese participants showed higher anthropomorphism (perceived human-like behavior) and a stronger perception of warmth and smaller social distance. The perception of competence did not differ across cultures. This study provides suggestions for the human-likeness design of service robots to promote natural interaction between humans and service robots and increase human acceptance of service robots.

AI anthropomorphism and its effect on users' self-congruence and self–AI integration: A theoretical framework and research agenda

This paper examines how users of anthropomorphised artificially intelligent (AI) agents, which possess capabilities to mimic humanlike behaviour, relate psychologically to such agents in terms of their self-concept. The proposed conceptual framework specifies different levels of anthropomorphism of AI agents and, drawing on insights from psychology, marketing and human–computer interaction literature, establishes a conceptual link between AI anthropomorphism and self-congruence. The paper then explains how this can lead to self–AI integration, a novel concept that articulates the process of users integrating AI agents into their self-concept. However, these effects can depend on a range of moderating factors, such as consumer traits, situational factors, self-construal and social exclusion. Crucially, the conceptual framework specifies how these processes can lead to specific personal-, group- and societal-level consequences, such as emotional connection and digital dementia. The research agenda proposed on the basis of the conceptual framework identifies key areas of interest that should be tackled by future research concerning this important phenomenon.

Belief in sharing the same phenomenological experience increases the likelihood of adopting the intentional stance toward a humanoid robot.

Humans interpret and predict others’ behaviors by ascribing intentions or beliefs, or in other words, by adopting the intentional stance. Since artificial agents are increasingly populating our daily environments, the question arises whether (and under which conditions) humans would apply the “human model” to understand the behaviors of these new social agents. Thus, in a series of three experiments, we tested whether embedding humans in a social interaction with a humanoid robot either displaying a human-like or machine-like behavior would modulate their initial tendency to adopt the intentional stance. Results showed that indeed humans are more prone to adopt the intentional stance after having interacted with a more socially available and human-like robot, while no modulation of the adoption of the intentional stance emerged toward a mechanistic robot. We conclude that short experiences with humanoid robots presumably inducing a “like-me” impression and social bonding increase the likelihood of adopting the intentional stance.

Supplemental Material for Belief in sharing the same phenomenological experience increases the likelihood of adopting the intentional stance toward a humanoid robot.

Humans interpret and predict others’ behaviors by ascribing intentions or beliefs, or in other words, by adopting the intentional stance. Since artificial agents are increasingly populating our daily environments, the question arises whether (and under which conditions) humans would apply the “human model” to understand the behaviors of these new social agents. Thus, in a series of three experiments, we tested whether embedding humans in a social interaction with a humanoid robot either displaying a human-like or machine-like behavior would modulate their initial tendency to adopt the intentional stance. Results showed that indeed humans are more prone to adopt the intentional stance after having interacted with a more socially available and human-like robot, while no modulation of the adoption of the intentional stance emerged toward a mechanistic robot. We conclude that short experiences with humanoid robots presumably inducing a “like-me” impression and social bonding increase the likelihood of adopting the intentional stance.

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

Though significant efforts such as removing false claims and promoting reliable sources have been increased to combat COVID-19 misinfodemic, it remains an unsolved societal challenge if lacking a proper understanding of susceptible online users, i.e., those who are likely to be attracted by, believe and spread misinformation. This study attempts to answer who constitutes the population vulnerable to the online misinformation in the pandemic, and what are the robust features and short-term behavior signals that distinguish susceptible users from others. Using a 6-month longitudinal user panel on Twitter collected from a geopolitically diverse network-stratified samples in the US, we distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation. This work brings unique contributions: First, contrary to the prior studies on bot influence, our analysis shows that social bots' contribution to misinformation sharing was surprisingly low, and human-like users' misinformation behaviors exhibit heterogeneity and temporal variability. While the sharing of misinformation was highly concentrated, the risk of occasionally sharing misinformation for average users remained alarmingly high. Second, our findings highlight the political sensitivity activeness and responsiveness to emotionally-charged content among susceptible users. Third, we demonstrate a feasible solution to efficiently predict users' transient susceptibility solely based on their short-term news consumption and exposure from their networks. Our work has an implication in designing effective intervention mechanism to mitigate the misinformation dissipation.

Learning-based autonomous vascular guidewire navigation without human demonstration in the venous system of a porcine liver

PurposeThe navigation of endovascular guidewires is a dexterous task where physicians and patients can benefit from automation. Machine learning-based controllers are promising to help master this task. However, human-generated training data are scarce and resource-intensive to generate. We investigate if a neural network-based controller trained without human-generated data can learn human-like behaviors.MethodsWe trained and evaluated a neural network-based controller via deep reinforcement learning in a finite element simulation to navigate the venous system of a porcine liver without human-generated data. The behavior is compared to manual expert navigation, and real-world transferability is evaluated.ResultsThe controller achieves a success rate of 100% in simulation. The controller applies a wiggling behavior, where the guidewire tip is continuously rotated alternately clockwise and counterclockwise like the human expert applies. In the ex vivo porcine liver, the success rate drops to 30%, because either the wrong branch is probed, or the guidewire becomes entangled.ConclusionIn this work, we prove that a learning-based controller is capable of learning human-like guidewire navigation behavior without human-generated data, therefore, mitigating the requirement to produce resource-intensive human-generated training data. Limitations are the restriction to one vessel geometry, the neglected safeness of navigation, and the reduced transferability to the real world.

Digital Self: The Next Evolution of the Digital Human

Digital interactions are taking on a more human-like appearance and behavior, but could or should they become our digital selves?