Human Factors Considerations and Metrics in Shared Space Human-Robot Collaboration: A Systematic Review

Socially Assistive Robots for patients with Alzheimer's Disease: A scoping review

BACKGROUND

The most common form of dementia, Alzheimer's Disease (AD), is challenging for both those affected as well as for their care providers, and caregivers. Socially assistive robots (SARs) offer promising supportive care to assist in the complex management associated with AD.

OBJECTIVES

To conduct a scoping review of published articles that proposed, discussed, developed or tested SAR for interacting with AD patients.

METHODS

We performed a scoping review informed by the methodological framework of Arksey and O'Malley and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. At the identification stage, an information specialist performed a comprehensive search of 8 electronic databases from the date of inception until January 2022 in eight bibliographic databases. The inclusion criteria were all populations who recive or provide care for AD, all interventions using SAR for AD and our outcomes of inteerst were any outcome related to AD patients or care providers or caregivers. All study types published in the English language were included.

RESULTS

After deduplication, 1251 articles were screened. Titles and abstracts screening resulted to 252 articles. Full-text review retained 125 included articles, with 72 focusing on daily life support, 46 on cognitive therapy, and 7 on cognitive assessment.

CONCLUSION

We conducted a comprehensive scoping review emphasizing on the interaction of SAR with AD patients, with a specific focus on daily life support, cognitive assessment, and cognitive therapy. We discussed our findings' pertinence relative to specific populations, interventions, and outcomes of human-SAR interaction on users and identified current knowledge gaps in SARs for AD patients.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task

OBJECTIVE

The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots.

BACKGROUND

Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated.

METHODS

Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics.

RESULTS

HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7).

CONCLUSION

HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation.

APPLICATION

These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing.

Trust in Shared-Space Collaborative Robots: Shedding Light on the Human Brain

BACKGROUND

Industry 4.0 is currently underway allowing for improved manufacturing processes that leverage the collective advantages of human and robot agents. Consideration of trust can improve the quality and safety in such shared-space human-robot collaboration environments.

OBJECTIVE

The use of physiological response to monitor and understand trust is currently limited due to a lack of knowledge on physiological indicators of trust. This study examines neural responses to trust within a shared-workcell human-robot collaboration task as well as discusses the use of granular and multimodal perspectives to study trust.

METHODS

Sixteen sex-balanced participants completed a surface finishing task in collaboration with a UR10 collaborative robot. All participants underwent robot reliability conditions and robot assistance level conditions. Brain activation and connectivity using functional near infrared spectroscopy, subjective responses, and performance were measured throughout the study.

RESULTS

Significantly, increased neural activation was observed in response to faulty robot behavior within the medial and right dorsolateral prefrontal cortex (PFC). A similar trend was observed for the anterior PFC, primary motor cortex, and primary visual cortex. Faulty robot behavior also resulted in reduced functional connectivity strengths throughout the brain.

DISCUSSION

These findings implicate regions in the prefrontal cortex along with specific connectivity patterns as signifiers of distrusting conditions. The neural response may be indicative of how trust is influenced, measured, and manifested for human-robot collaboration that requires active teaming.

APPLICATION

Neuroergonomic response metrics can reveal new perspectives on trust in automation that subjective responses alone are not able to provide.

A Literature Review on Safety Perception and Trust during Human-Robot Interaction with Autonomous Mobile Robots That Apply to Industrial Environments

Occupational ApplicationsAutonomous mobile robots are used in manufacturing and warehousing industries, to transport material across the facility and deliver parts to work cells. Human workers might encounter or interact with these robots in aisle ways or at their workstation. It is important to consider factors that impact worker safety and trust when implementing autonomous mobile robots in the workplace. This paper reviews prior research that aims to improve the safety of human-robot interaction with autonomous mobile robots and identifies needs for future research. Researchers used a variety of questionnaires and behavioral assessment methods to measure perceived safety. Factors such as robot appearance, approach speed, and approach direction, significantly affect perceived safety. Additionally, projection of signals on the floor, turn signals, and haptic communication devices, can improve the predictability and overall safety of robot navigation.

Physiological Indicators of Fluency and Engagement during Sequential and Simultaneous Modes of Human-Robot Collaboration

OCCUPATIONAL APPLICATIONSAn understanding of fluency in human-robot teaming from a physiological standpoint is still incomplete. In our experimental study involving 24 participants, we designed a scenario for shared-space human-robot collaboration (HRC) for a material sorting task. When compared to a sequential mode of interaction, the simultaneous mode resulted in significantly higher perceptions of fluency and engagement, primarily by reducing human idle time. These observations were complemented by significant changes in physiological responses, such as ECG entropy and low frequency power. These responses could predict fluency and engagement with accuracies of 90 and 97%, respectively. Notably, the perception of fluency and preferred mode of interaction were influenced by individual preferences. Hence, it is crucial to consider both physiological responses and user preferences when designing HRC systems, to ensure a positive experience with the robot teammate and to foster engagement in long-term teamwork. Furthermore, these signals can be obtained using a single robust, low-cost, and comfortable sensor.

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers' mental workload in assembly tasks

Introduction: As a result of Industry 5.0's technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers' trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view. Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers' perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants' perceived workload. Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels. Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

Human Factors Considerations for Quantifiable Human States in Physical Human-Robot Interaction: A Literature Review

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing the studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together, and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on the measurable data for each human factor. We also formed a map of the common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI studies (when the physical contact is mediated via a third item) often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on the human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address the quantification approaches based on measurable data.

Human-Robot Interaction in Agriculture: A Systematic Review

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human-robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human-robot interactive systems.

Introducing a healthcare-assistive robot in primary care: a preliminary questionnaire survey

A Healthcare-assistive Infection-control RObot (HIRO) is a healthcare-assistive robot that is deployed in an outpatient primary care clinic to sanitise the premises, monitor people in its proximity for their temperature and donning of masks, and usher them to service points. This study aimed to determine the acceptability, perceptions of safety, and concerns among the patients, visitors, and polyclinic healthcare workers (HCWs) regarding the HIRO. A cross-sectional questionnaire survey was conducted from March to April 2022 when the HIRO was at Tampines Polyclinic in eastern Singapore. A total of 170 multidisciplinary HCWs serve approximately 1,000 patients and visitors daily at this polyclinic. The sample size of 385 was computed using a proportion of 0.5, 5% precision, and 95% confidence interval. Research assistants administered an e-survey to gather demographic data and feedback from 300 patients/visitors and 85 HCWs on their perceptions of the HIRO using Likert scales. The participants watched a video on the HIRO's functionalities and were given the opportunity to directly interact with it. Descriptive statistics was performed and figures were presented in frequencies and percentages. The majority of the participants viewed the HIRO's functionalities favourably: sanitising (96.7%/91.2%); checking proper mask donning (97%/89.4%); temperature monitoring (97%/91.7%); ushering (91.7%/81.1%); perceived user friendliness (93%/88.3%), and improvement in the clinic experience (96%/94.2%). A minority of the participants perceived harm from the HIRO's liquid disinfectant (29.6%/31.5%) and that its voice-annotated instructions may be upsetting (14%/24.8%). Most of the participants accepted the HIRO's deployment at the polyclinic and perceived it to be safe. The HIRO used ultraviolet irradiation for sanitisation during after-clinic hours instead of disinfectants due to the perceived harm.

The Relationships of Human-Cobot Interaction Fluency with Job Performance and Job Satisfaction among Cobot Operators-The Moderating Role of Workload

Modern factories are subject to rapid technological changes, including the advancement of robotics. A key manufacturing solution in the fourth industrial revolution is the introduction of collaborative robots (cobots), which cooperate directly with human operators while executing shared tasks. Although collaborative robotics has tangible benefits, cobots pose several challenges to human-robot interaction. Proximity, unpredictable robot behavior, and switching the operator's role from a co-operant to a supervisor can negatively affect the operator's cognitive, emotional, and behavioral responses, resulting in their lower well-being and decreased job performance. Therefore, proper actions are necessary to improve the interaction between the robot and its human counterpart. Specifically, exploring the concept of human-robot interaction (HRI) fluency shows promising perspectives. However, research on conditions affecting the relationships between HRI fluency and its outcomes is still in its infancy. Therefore, the aim of this cross-sectional survey study was twofold. First, the relationships of HRI fluency with job performance (i.e., task performance, organizational citizenship behavior, and creative performance) and job satisfaction were investigated. Second, the moderating role of the quantitative workload in these associations was verified. The analyses carried out on data from 200 male and female cobot operators working on the shop floor showed positive relationships between HRI fluency, job performance, and job satisfaction. Moreover, the study confirmed the moderating role of the quantitative workload in these relations. The results showed that the higher the workload, the lower the relationships between HRI fluency and its outcomes. The study findings are discussed within the theoretical framework of the Job Demands-Control-Support model.

A Framework and Algorithm for Human-Robot Collaboration Based on Multimodal Reinforcement Learning

Despite the emergence of various human-robot collaboration frameworks, most are not sufficiently flexible to adapt to users with different habits. In this article, a Multimodal Reinforcement Learning Human-Robot Collaboration (MRLC) framework is proposed. It integrates reinforcement learning into human-robot collaboration and continuously adapts to the user's habits in the process of collaboration with the user to achieve the effect of human-robot cointegration. With the user's multimodal features as states, the MRLC framework collects the user's speech through natural language processing and employs it to determine the reward of the actions made by the robot. Our experiments demonstrate that the MRLC framework can adapt to the user's habits after repeated learning and better understand the user's intention compared to traditional solutions.