Care-receivers with physical disabilities' perceptions on having humanoid assistive robots as assistants: a qualitative study.

BACKGROUND: People with physical disabilities due to disease or injury face barriers to their daily activities and participation in society. Many depend on formal or informal caregivers for assistance to live independently. However, future healthcare challenges due to demographic changes threaten access to home care and assistants. Assistive technologies, such as robots for physical assistance, can support the independence and autonomy of people with physical disabilities. This study explore Norwegian care-receivers' perceptions of using robot assistance in their homes, including preferences for tasks acceptable or unacceptable for robot assistance and the underlying reasons. METHOD: Purposive sampling was employed to recruit 18 participants, aged between 18 and 77 years, with differences in physical function including diagnoses such as stroke, spinal cord injury, amputations, and muscular dystrophy. Qualitative data were gathered through four focus group interviews wherein participants watched videos featuring a humanoid assistive robot, EVEr3. The collected data underwent analysis using reflexive thematic analysis. RESULTS: Three themes with associated sub-themes were constructed: (a) How a robot could assist in daily life, (b) The robot's appearance and functionality, and (c) Concerns about having a robot as an assistant. The participants welcomed the idea of a future robotic assistant in areas that may contribute to an increased feeling of independence and autonomy. CONCLUSION: A robot assisting in activities of daily living would need to be individually customized to meet the needs of each user in terms of which tasks to assist with, how to assist in these defined tasks, and how it is controlled.

Navigating autonomy, privacy, and ageism in robot home care with aged users: A preliminary analysis of ROB-IN.

In this article, I propose an ethical analysis of assistive domestic robots for older users. In doing so, I illustrate my inquiry with the example of ROB-IN assistive robot. ROB-IN is a Spanish project which is devoted to developing a robot that will perform in the private home of nondependent, aged users. It is aimed to help people in their daily activities and contribute to appropriate health monitoring. One of their potentially most useful features is related to data gathering and sharing. For the inquiry on the ethical underpinnings of this case, I develop a framework for domestic assistive robots for competent older adults drawn on the ethics of care. I assess that this type of robots could be ethically appraised attending to their impact on the well-being and autonomy of users. I approach autonomy from a relational perspective, and I delve into the relationship between autonomy and well-being through the concept of paternalism. I argue that this type of assistive robots should never act paternalistically. Given ROB-IN great implications regarding privacy, I subsequently explore the ways in which the privacy of users should be respected in their interaction with assistive robots, focusing on the relation with autonomy and well-being. Lastly, I highlight the need for avoiding ageism. This investigation focuses on aged users, but it is suggested that the situation of caregivers should be also the object of further investigations.

Barriers and facilitators to technology acceptance of socially assistive robots in older adults - A qualitative study based on the capability, opportunity, and motivation behavior model (COM-B) and stakeholder perspectives.

This study aimed to identify barriers and facilitators to older adults' acceptance of socially assistive robots from a stakeholder perspective. We enlisted 36 distinct stakeholders, including older adult, nurses, retirement home managers, and employees from robotics companies. Data collection was conducted through semi-structured interviews. The research findings were mapped onto the Capability, Opportunity, Motivation-Behavior (COM-B) model. We obtained a total of 14 facilitators and barriers. (1) Capability: High technological familiarity (Facilitator); insufficient technical experience and low level of education (Barriers). (2) Motivation: Strong interest in new things, perceived convenience usefulness, and emotional support (Facilitators); concerns about technical reliability, perceived lack of ease of use, inability to establish emotional connection, and low level of need (Barriers). (3) Opportunity: Insufficient policy support and economic capacity, robotics technical problems (Barriers). Collaborative efforts among stakeholders are vital for fostering an environment conducive to socially assistive robot adoption, maximizing its potential to improve older adults' well-being.

My Precious Friend: Human-Robot Interactions in Home Care for Socially Isolated Older Adults.

OBJECTIVES: Using a friendship framework, we explored interactions between a multi-functional companion robot and older adults residing in a low-resource community in South Korea. METHODS: We conducted in-depth interviews with 12 older adults who kept a doll-shaped companion robot called Hyodol for 18 months on average. We applied the Framework Analysis Method to explore three types of friendship (i.e., friendships of utility, pleasure, and the good) that participants cultivated with the robot. RESULTS: The most common aspect of utility companionship reported by all participants was Hyodol's role as their health coach who reminded them to take medication and to exercise. Participants also found pleasure in playing with Hyodol and reported reduced feelings of loneliness. In the absence of other social supports, all participants also regarded Hyodol as a surrogate family member or human-friend, and interacted with Hyodol as such. CONCLUSIONS: Findings illustrated high acceptability of Hyodol among these socially isolated older adults especially during the global COVID-19 pandemic, suggesting that a humanoid like Hyodol could be complementary to homecare services for solo-living older adults. CLINICAL IMPLICATIONS: Well-designed robot interventions, as complements to existing aging service and clinical interventions, have a potential to improve health behaviors among socially isolated older adults.

Can Socially Assistive Robots Be Accepted by Older People Living Alone in the Community?: Empirical Findings from a Social Work Project in China.

A pilot study was undertaken between March 2019 and September 2021, loaning socially assistive robots (SARs) for a 7-day trial to older people living alone in China. Quantitative assessments of participants' acceptance of technology and loneliness were conducted before and after the intervention, supplemented with qualitative interviews. Unexpectedly, participants' intention to use SARs decreased significantly, largely due to emotional anxiety. Meanwhile, participants' level of loneliness remained unchanged. Follow-up interviews revealed anxious emotion, hesitant attitudes, unreal social presence, usability difficulties as contributing factors. The study provides social workers with valuable insights into introducing SARs into community care of older people.

Socially Assistive Robots for People Living with Dementia in Long-Term Facilities: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND AND OBJECTIVE: The purpose of our study was to explore the immediate and long-term effects of socially assistive robots (SARs) on neuropsychiatric symptoms (NPSs), behavioral and psychological symptoms of dementia (BPSD), positive emotional experiences, and social interaction in older people living with dementia. METHODS: We set keywords and used Boolean operators to search the CINAHL, Cochrane Library, EMBASE, IEEE Digital Library, MEDLINE, PsycINFO, PubMed, Web of Science, Scopus, and Chinese Electronic Periodical Service from inception to February 2022 for randomized controlled trials. The Cochrane Collaboration bias assessment tool was used to assess article quality, and RevMan 5.4.1 software was used to conduct the meta-analysis. RESULTS: A total of 14 studies were included in the meta-analysis. SARs can help people living with dementia reduce their NPS of depression and anxiety, provide happiness from positive emotional experiences, and improve their social interaction through conversation. However, there was no significant improvement in agitation behavior, overall BPSD, or quality of life in people living with dementia. In follow-up, it was found that the effect of SRT was limited. CONCLUSION: SARs can reduce depression and increase positive emotions in people living with dementia. They may also reduce the burden on healthcare workers during the COVID-19 pandemic. This research was registered on PROSPERO CRD42020169340.

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots.

The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study's detailed analysis can improve the usability of the assistive/rehabilitation robots.

Efficient Self-Attention Model for Speech Recognition-Based Assistive Robots Control.

Assistive robots are tools that people living with upper body disabilities can leverage to autonomously perform Activities of Daily Living (ADL). Unfortunately, conventional control methods still rely on low-dimensional, easy-to-implement interfaces such as joysticks that tend to be unintuitive and cumbersome to use. In contrast, vocal commands may represent a viable and intuitive alternative. This work represents an important step toward providing a viable vocal interface for people living with upper limb disabilities by proposing a novel lightweight vocal command recognition system. The proposed model leverages the MobileNet2 architecture, augmenting it with a novel approach to the self-attention mechanism, achieving a new state-of-the-art performance for Keyword Spotting (KWS) on the Google Speech Commands Dataset (GSCD). Moreover, this work presents a new dataset, referred to as the French Speech Commands Dataset (FSCD), comprising 4963 vocal command utterances. Using the GSCD as the source, we used Transfer Learning (TL) to adapt the model to this cross-language task. TL has been shown to significantly improve the model performance on the FSCD. The viability of the proposed approach is further demonstrated through real-life control of a robotic arm by four healthy participants using both the proposed vocal interface and a joystick.

Application of Foot Hallux Contact Force Signal for Assistive Hand Fine Control.

Accurate recognition of disabled persons' behavioral intentions is the key to reconstructing hand function. Their intentions can be understood to some extent by electromyography (EMG), electroencephalogram (EEG), and arm movements, but they are not reliable enough to be generally accepted. In this paper, characteristics of foot contact force signals are investigated, and a method of expressing grasping intentions based on hallux (big toe) touch sense is proposed. First, force signals acquisition methods and devices are investigated and designed. By analyzing characteristics of signals in different areas of the foot, the hallux is selected. The peak number and other characteristic parameters are used to characterize signals, which can significantly express grasping intentions. Second, considering complex and fine tasks of the assistive hand, a posture control method is proposed. Based on this, many human-in-the-loop experiments are conducted using human-computer interaction methods. The results showed that people with hand disabilities could accurately express their grasping intentions through their toes, and could accurately grasp objects of different sizes, shapes, and hardness using their feet. The accuracy of the action completion for single-handed and double-handed disabled individuals was 99% and 98%, respectively. This proves that the method of using toe tactile sensation for assisting disabled individuals in hand control can help them complete daily fine motor activities. The method is easily acceptable in terms of reliability, unobtrusiveness, and aesthetics.

A rise-assisting robot extends life space and improves facial expressions of nursing home residents.

BACKGROUND: The introduction of nursing care-support devices using robotic technology is expected to reduce the task burden in long-term care facilities. OBJECTIVE: To investigate the use of the rise-assisting robot, Resyone, in extending and improving the life space of nursing home residents with severe care needs. METHODS: We performed a feasibility study in which Resyone was used to facilitate visits to additional sites in and around the nursing home as part of the care package of three residents. Two weeks before and four weeks after implementation of the new arrangements, the 30 caregivers involved were asked to record transfer times and destinations, while also checking the residents' facial expressions. RESULTS: Before implementation, participants had limited life spaces, but afterwards they regularly visited additional destinations including the garden, home entrance and corridors, which previously they had not visited frequently. The residents' facial expressions became more positive and less negative. This study demonstrates that Resyone can enrich care activities in severely disabled individuals. CONCLUSION: These findings suggest that the sustainable use of Resyone would improve the quality of care at care facilities. Moreover, the extension of otherwise limited life space has the potential to improve care receivers' quality of life. TRIAL REGISTRATION: UMIN Clinical Trials Registry No. UMIN000039204 (20/01/2020); retrospectively registered; interventional study; parallel, non-randomized, single blinded. URL of trial registry records: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000044709 .

SoftSAR: The New Softer Side of Socially Assistive Robots-Soft Robotics with Social Human-Robot Interaction Skills.

When we think of "soft" in terms of socially assistive robots (SARs), it is mainly in reference to the soft outer shells of these robots, ranging from robotic teddy bears to furry robot pets. However, soft robotics is a promising field that has not yet been leveraged by SAR design. Soft robotics is the incorporation of smart materials to achieve biomimetic motions, active deformations, and responsive sensing. By utilizing these distinctive characteristics, a new type of SAR can be developed that has the potential to be safer to interact with, more flexible, and uniquely uses novel interaction modes (colors/shapes) to engage in a heighted human-robot interaction. In this perspective article, we coin this new collaborative research area as SoftSAR. We provide extensive discussions on just how soft robotics can be utilized to positively impact SARs, from their actuation mechanisms to the sensory designs, and how valuable they will be in informing future SAR design and applications. With extensive discussions on the fundamental mechanisms of soft robotic technologies, we outline a number of key SAR research areas that can benefit from using unique soft robotic mechanisms, which will result in the creation of the new field of SoftSAR.

Investigating Methods for Cognitive Workload Estimation for Assistive Robots.

Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.

A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artificial vision and tactile and surface electromyography (sEMG). If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities. However, despite the advancements in sensing technologies, as well as excellent mechanical capabilities of the prosthetic devices, their control is often limited and usually requires a long time for training and adaptation of the users. The myoelectric prostheses use signals from residual stump muscles to restore the function of the lost limbs seamlessly. However, the use of the sEMG signals in robotic as a user control signal is very complicated due to the presence of noise, and the need for heavy computational power. In this article, we developed motion intention classifiers for transradial (TR) amputees based on EMG data by implementing various machine learning and deep learning models. We benchmarked the performance of these classifiers based on overall generalization across various classes and we presented a systematic study on the impact of time domain features and pre-processing parameters on the performance of the classification models. Our results showed that Ensemble learning and deep learning algorithms outperformed other classical machine learning algorithms. Investigating the trend of varying sliding window on feature-based and non-feature-based classification model revealed interesting correlation with the level of amputation. The study also covered the analysis of performance of classifiers on amputation conditions since the history of amputation and conditions are different to each amputee. These results are vital for understanding the development of machine learning-based classifiers for assistive robotic applications.

A Meta-Analysis on Remote HRI and In-Person HRI: What Is a Socially Assistive Robot to Do?

Recently, due to the COVID-19 pandemic and the related social distancing measures, in-person activities have been significantly reduced to limit the spread of the virus, especially in healthcare settings. This has led to loneliness and social isolation for our most vulnerable populations. Socially assistive robots can play a crucial role in minimizing these negative affects. Namely, socially assistive robots can provide assistance with activities of daily living, and through cognitive and physical stimulation. The ongoing pandemic has also accelerated the exploration of remote presence ranging from workplaces to home and healthcare environments. Human-robot interaction (HRI) researchers have also explored the use of remote HRI to provide cognitive assistance in healthcare settings. Existing in-person and remote comparison studies have investigated the feasibility of these types of HRI on individual scenarios and tasks. However, no consensus on the specific differences between in-person HRI and remote HRI has been determined. Furthermore, to date, the exact outcomes for in-person HRI versus remote HRI both with a physical socially assistive robot have not been extensively compared and their influence on physical embodiment in remote conditions has not been addressed. In this paper, we investigate and compare in-person HRI versus remote HRI for robots that assist people with activities of daily living and cognitive interventions. We present the first comprehensive investigation and meta-analysis of these two types of robotic presence to determine how they influence HRI outcomes and impact user tasks. In particular, we address research questions regarding experience, perceptions and attitudes, and the efficacy of both humanoid and non-humanoid socially assistive robots with different populations and interaction modes. The use of remote HRI to provide assistance with daily activities and interventions is a promising emerging field for healthcare applications.

Maximizing the Benefits of Participatory Design for Human-Robot Interaction Research With Older Adults.

OBJECTIVE: We reviewed human-robot interaction (HRI) participatory design (PD) research with older adults. The goal was to identify methods used, determine their value for design of robots with older adults, and provide guidance for best practices. BACKGROUND: Assistive robots may promote aging-in-place and quality of life for older adults. However, the robots must be designed to meet older adults' specific needs and preferences. PD and other user-centered methods may be used to engage older adults in the robot development process to accommodate their needs and preferences and to assure usability of emergent assistive robots. METHOD: This targeted review of HRI PD studies with older adults draws on a detailed review of 26 articles. Our assessment focused on the HRI methods and their utility for use with older adults who have a range of needs and capabilities. RESULTS: Our review highlighted the importance of using mixed methods and including multiple stakeholders throughout the design process. These approaches can encourage mutual learning (to improve design by developers and to increase acceptance by users). We identified key phases used in HRI PD workshops (e.g., initial interview phase, series of focus groups phase, and presentation phase). These approaches can provide inspiration for future efforts. CONCLUSION: HRI PD strategies can support designers in developing assistive robots that meet older adults' needs, capabilities, and preferences to promote acceptance. More HRI research is needed to understand potential implications for aging-in-place. PD methods provide a promising approach.

Social robots and therapeutic adherence: A new challenge in pediatric asthma?

Social Robots are used in different contexts and, in healthcare, they are better known as Socially Assistive Robots. In the context of asthma, the use of Socially Assistive Robots has the potential to increase motivation and engagement to treatment. Other positive roles proposed for Socially Assistive Robots are to provide education, training regarding treatments, and feedback to patients. This review evaluates emerging interventions for improving treatment adherence in pediatric asthma, focusing on the possible future role of social robots in the clinical practice.

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot-methodology and usability testing.

BACKGROUND: Socially assistive robots (SARs) have been proposed as a tool to help individuals who have had a stroke to perform their exercise during their rehabilitation process. Yet, to date, there are no data on the motivating benefit of SARs in a long-term interaction with post-stroke patients. METHODS: Here, we describe a robot-based gamified exercise platform, which we developed for long-term post-stroke rehabilitation. The platform uses the humanoid robot Pepper, and also has a computer-based configuration (with no robot). It includes seven gamified sets of exercises, which are based on functional tasks from the everyday life of the patients. The platform gives the patients instructions, as well as feedback on their performance, and can track their performance over time. We performed a long-term patient-usability study, where 24 post-stroke patients were randomly allocated to exercise with this platform-either with the robot or the computer configuration-over a 5-7 week period, 3 times per week, for a total of 306 sessions. RESULTS: The participants in both groups reported that this rehabilitation platform addressed their arm rehabilitation needs, and they expressed their desire to continue training with it even after the study ended. We found a trend for higher acceptance of the system by the participants in the robot group on all parameters; however, this difference was not significant. We found that system failures did not affect the long-term trust that users felt towards the system. CONCLUSIONS: We demonstrated the usability of using this platform for a long-term rehabilitation with post-stroke patients in a clinical setting. We found high levels of acceptance of both platform configurations by patients following this interaction, with higher ratings given to the SAR configuration. We show that it is not the mere use of technology that increases the motivation of the person to practice, but rather it is the appreciation of the technology's effectiveness and its perceived contribution to the rehabilitation process. In addition, we provide a list of guidelines that can be used when designing and implementing other technological tools for rehabilitation. TRIAL REGISTRATION: This trial is registered in the NIH ClinicalTrials.gov database. Registration number NCT03651063, registration date 21.08.2018. https://clinicaltrials.gov/ct2/show/NCT03651063 .

Enabling Security Services in Socially Assistive Robot Scenarios for Healthcare Applications.

Today's IoT deployments are highly complex, heterogeneous and constantly changing. This poses severe security challenges such as limited end-to-end security support, lack of cross-platform cross-vertical security interoperability as well as the lack of security services that can be readily applied by security practitioners and third party developers. Overall, these require scalable, decentralized and intelligent IoT security mechanisms and services which are addressed by the SecureIoT project. This paper presents the definition, implementation and validation of a SecureIoT-enabled socially assisted robots (SAR) usage scenario. The aim of the SAR scenario is to integrate and validate the SecureIoT services in the scope of personalized healthcare and ambient assistive living (AAL) scenarios, involving the integration of two AAL platforms, namely QTrobot (QT) and CloudCare2U (CC2U). This includes risk assessment of communications security, predictive analysis of security risks, implementing access control policies to enhance the security of solution, and auditing of the solution against security, safety and privacy guidelines and regulations. Future perspectives include the extension of this security paradigm by securing the integration of healthcare platforms with IoT solutions, such as Healthentia with QTRobot, by means of a system product assurance process for cyber-security in healthcare applications, through the PANACEA toolkit.

Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review.

Research on affective communication for socially assistive robots has been conducted to enable physical robots to perceive, express, and respond emotionally. However, the use of affective computing in social robots has been limited, especially when social robots are designed for children, and especially those with autism spectrum disorder (ASD). Social robots are based on cognitive-affective models, which allow them to communicate with people following social behaviors and rules. However, interactions between a child and a robot may change or be different compared to those with an adult or when the child has an emotional deficit. In this study, we systematically reviewed studies related to computational models of emotions for children with ASD. We used the Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to the definition, interaction, and design of computational models supported by theoretical psychology approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children or those with ASD.

Socially Assistive Robots in Aged Care: Ethical Orientations Beyond the Care-Romantic and Technology-Deterministic Gaze.

Socially Assistive Robots (SARs) are increasingly conceived as applicable tools to be used in aged care. However, the use carries many negative and positive connotations. Negative connotations come forth out of romanticized views of care practices, disregarding their already established technological nature. Positive connotations are formulated out of techno-deterministic views on SAR use, presenting it as an inevitable and necessary next step in technological development to guarantee aged care. Ethical guidance of SAR use inspired by negative connotations tends to be over-restrictive whereas positive connotations tend to provide over-permissive guidance. To avoid these extremes, we report on the development and content of 21 ethical orientations regarding SAR use in aged care. These orientations resulted from a multi-phased project, which consisted of empirical-ethical research focusing on older adults' intuitions regarding SAR use and philosophical-ethical research focusing on philosophical-ethical argumentations regarding SAR use. This project led to the Socio-historical contextualization of the ethics of SAR use, in which the ethical impact of SAR use is localized on three interrelated analysis levels: societal, organizational, and individual-relational. The 21 novel orientations regarding SAR use are structured according to these levels and further categorized into foundational and applied orientations. The first category leads to critical reflection on SAR use while the latter category inspires decision-making processes regarding this use. While going beyond the care-romantic and techno-deterministic gaze of SAR use in aged care, the described orientations balance themselves between their over-restrictiveness and over-permissiveness.