Healthcare Robotics

Robots have the potential to be a game changer in healthcare: improving health and well-being, filling care gaps, supporting care givers, and aiding health care workers. However, before robots are able to be widely deployed, it is crucial that both the research and industrial communities work together to establish a strong evidence-base for healthcare robotics, and surmount likely adoption barriers. This article presents a broad contextualization of robots in healthcare by identifying key stakeholders, care settings, and tasks; reviewing recent advances in healthcare robotics; and outlining major challenges and opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201

Technology-assisted stroke rehabilitation in Mexico: a pilot randomized trial comparing traditional therapy to circuit training in a Robot/technology-assisted therapy gym

Background Stroke rehabilitation in low- and middle-income countries, such as Mexico, is often hampered by lack of clinical resources and funding. To provide a cost-effective solution for comprehensive post-stroke rehabilitation that can alleviate the need for one-on-one physical or occupational therapy, in lower and upper extremities, we proposed and implemented a technology-assisted rehabilitation gymnasium in Chihuahua, Mexico. The Gymnasium for Robotic Rehabilitation (Robot Gym) consisted of low- and high-tech systems for upper and lower limb rehabilitation. Our hypothesis is that the Robot Gym can provide a cost- and labor-efficient alternative for post-stroke rehabilitation, while being more or as effective as traditional physical and occupational therapy approaches. Methods A typical group of stroke patients was randomly allocated to an intervention (n = 10) or a control group (n = 10). The intervention group received rehabilitation using the devices in the Robot Gym, whereas the control group (n = 10) received time-matched standard care. All of the study subjects were subjected to 24 two-hour therapy sessions over a period of 6 to 8 weeks. Several clinical assessments tests for upper and lower extremities were used to evaluate motor function pre- and post-intervention. A cost analysis was done to compare the cost effectiveness for both therapies. Results No significant differences were observed when comparing the results of the pre-intervention Mini-mental, Brunnstrom Test, and Geriatric Depression Scale Test, showing that both groups were functionally similar prior to the intervention. Although, both training groups were functionally equivalent, they had a significant age difference. The results of all of the upper extremity tests showed an improvement in function in both groups with no statistically significant differences between the groups. The Fugl-Meyer and the 10 Meters Walk lower extremity tests showed greater improvement in the intervention group compared to the control group. On the Time Up and Go Test, no statistically significant differences were observed pre- and post-intervention when comparing the control and the intervention groups. For the 6 Minute Walk Test, both groups presented a statistically significant difference pre- and post-intervention, showing progress in their performance. The robot gym therapy was more cost-effective than the traditional one-to-one therapy used during this study in that it enabled therapist to train up to 1.5 to 6 times more patients for the approximately same cost in the long term. Conclusions The results of this study showed that the patients that received therapy using the Robot Gym had enhanced functionality in the upper extremity tests similar to patients in the control group. In the lower extremity tests, the intervention patients showed more improvement than those subjected to traditional therapy. These results support that the Robot Gym can be as effective as traditional therapy for stroke patients, presenting a more cost- and labor-efficient option for countries with scarce clinical resources and funding. Trial registration ISRCTN98578807

Can We Agree on What Robots Should be Allowed to Do? An Exercise in Rule Selection for Ethical Care Robots

Future Care Robots (CRs) should be able to balance a patient’s, often conflicting, rights without ongoing supervision. Many of the trade-offs faced by such a robot will require a degree of moral judgment. Some progress has been made on methods to guarantee robots comply with a predefined set of ethical rules. In contrast, methods for selecting these rules are lacking. Approaches departing from existing philosophical frameworks, often do not result in implementable robotic control rules. Machine learning approaches are sensitive to biases in the training data and suffer from opacity. Here, we propose an alternative, empirical, survey-based approach to rule selection. We suggest this approach has several advantages, including transparency and legitimacy. The major challenge for this approach, however, is that a workable solution, or social compromise, has to be found: it must be possible to obtain a consistent and agreed-upon set of rules to govern robotic behavior. In this article, we present an exercise in rule selection for a hypothetical CR to assess the feasibility of our approach. We assume the role of robot developers using a survey to evaluate which robot behavior potential users deem appropriate in a practically relevant setting, i.e., patient non-compliance. We evaluate whether it is possible to find such behaviors through a consensus. Assessing a set of potential robot behaviors, we surveyed the acceptability of robot actions that potentially violate a patient’s autonomy or privacy. Our data support the empirical approach as a promising and cost-effective way to query ethical intuitions, allowing us to select behavior for the hypothetical CR

RoboChain: A Secure Data-Sharing Framework for Human-Robot Interaction

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy.Comment: 7 pages, 6 figure

The CARESSES study protocol: testing and evaluating culturally competent socially assistive robots among older adults residing in long term care homes through a controlled experimental trial

Background : This article describes the design of an intervention study that focuses on whether and to what degree culturally competent social robots can improve health and well-being related outcomes among older adults residing long-term care homes. The trial forms the final stage of the international, multidisciplinary CARESSES project aimed at designing, developing and evaluating culturally competent robots that can assist older people according to the culture of the individual they are supporting. The importance of cultural competence has been demonstrated in previous nursing literature to be key towards improving health outcomes among patients. Method : This study employed a mixed-method, single-blind, parallel-group controlled before-and-after experimental trial design that took place in England and Japan. It aimed to recruit 45 residents of long-term care homes aged ≥65 years, possess sufficient cognitive and physical health and who self-identify with the English, Indian or Japanese culture (n = 15 each). Participants were allocated to either the experimental group, control group 1 or control group 2 (all n = 15). Those allocated to the experimental group or control group 1 received a Pepper robot programmed with the CARESSES culturally competent artificial intelligence (experimental group) or a limited version of this software (control group 1) for 18 h across 2 weeks. Participants in control group 2 did not receive a robot and continued to receive care as usual. Participants could also nominate their informal carer(s) to participate. Quantitative data collection occurred at baseline, after 1 week of use, and after 2 weeks of use with the latter time-point also including qualitative semi-structured interviews that explored their experience and perceptions further. Quantitative outcomes of interest included perceptions of robotic cultural competence, health-related quality of life, loneliness, user satisfaction, attitudes towards robots and caregiver burden. Discussion : This trial adds to the current preliminary and limited pool of evidence regarding the benefits of socially assistive robots for older adults which to date indicates considerable potential for improving outcomes. It is the first to assess whether and to what extent cultural competence carries importance in generating improvements to well-being

Designing Robots for Care: Care Centered Value-Sensitive Design

The prospective robots in healthcare intended to be included within the conclave of the nurse-patient relationship—what I refer to as care robots—require rigorous ethical reflection to ensure their design and introduction do not impede the promotion of values and the dignity of patients at such a vulnerable and sensitive time in their lives. The ethical evaluation of care robots requires insight into the values at stake in the healthcare tradition. What’s more, given the stage of their development and lack of standards provided by the International Organization for Standardization to guide their development, ethics ought to be included into the design process of such robots. The manner in which this may be accomplished, as presented here, uses the blueprint of the Value-sensitive design approach as a means for creating a framework tailored to care contexts. Using care values as the foundational values to be integrated into a technology and using the elements in care, from the care ethics perspective, as the normative criteria, the resulting approach may be referred to as care centered value-sensitive design. The framework proposed here allows for the ethical evaluation of care robots both retrospectively and prospectively. By evaluating care robots in this way, we may ultimately ask what kind of care we, as a society, want to provide in the futur

A double-blinded randomised controlled trial exploring the effect of anodal transcranial direct current stimulation and uni-lateral robot therapy for the impaired upper limb in sub-acute and chronic stroke

BACKGROUND:Neurorehabilitation technologies such as robot therapy (RT) and transcranial Direct Current Stimulation (tDCS) can promote upper limb (UL) motor recovery after stroke. OBJECTIVE:To explore the effect of anodal tDCS with uni-lateral and three-dimensional RT for the impaired UL in people with sub-acute and chronic stroke. METHODS:A pilot randomised controlled trial was conducted. Stroke participants had 18 one-hour sessions of RT (Armeo®Spring) over eight weeks during which they received 20 minutes of either real tDCS or sham tDCS during each session. The primary outcome measure was the Fugl-Meyer assessment (FMA) for UL impairments and secondary were: UL function, activities and stroke impact collected at baseline, post-intervention and three-month follow-up. RESULTS:22 participants (12 sub-acute and 10 chronic) completed the trial. No significant difference was found in FMA between the real and sham tDCS groups at post-intervention and follow-up (p = 0.123). A significant ‘time’ x ‘stage of stroke’ was found for FMA (p = 0.016). A higher percentage improvement was noted in UL function, activities and stroke impact in people with sub-acute compared to chronic stroke. CONCLUSIONS:Adding tDCS did not result in an additional effect on UL impairment in stroke. RT may be of more benefit in the sub-acute than chronic phase

Intelligent Physical Robots in Health Care: Systematic Literature Review

Background: Intelligent physical robots based on artificial intelligence have been argued to bring about dramatic changes in health care services. Previous research has examined the use of intelligent physical robots in the health care context from different perspectives; however, an overview of the antecedents and consequences of intelligent physical robot use in health care is lacking in the literature. Objective: In this paper, we aimed to provide an overview of the antecedents and consequences of intelligent physical robot use in health care and to propose potential agendas for future research through a systematic literature review. Methods: We conducted a systematic literature review on intelligent physical robots in the health care field following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Literature searches were conducted in 5 databases (PubMed, Scopus, PsycINFO, Embase, and CINAHL) in May 2021, focusing on studies using intelligent physical robots for health care purposes. Subsequently, the quality of the included studies was assessed using the Mixed Methods Appraisal Tool. We performed an exploratory content analysis and synthesized the findings extracted from the included articles. Results: A total of 94 research articles were included in the review. Intelligent physical robots, including mechanoid, humanoid, android, and animalistic robots, have been used in hospitals, nursing homes, mental health care centers, laboratories, and patients’ homes by both end customers and health care professionals. The antecedents for intelligent physical robot use are categorized into individual-, organization-, and robot-related factors. Intelligent physical robot use in the health care context leads to both non–health-related consequences (emotional outcomes, attitude and evaluation outcomes, and behavioral outcomes) and consequences for (physical, mental, and social) health promotion for individual users. Accordingly, an integrative framework was proposed to obtain an overview of the antecedents and consequences of intelligent physical robot use in the health care context. Conclusions: This study contributes to the literature by summarizing current knowledge in the field of intelligent physical robot use in health care, by identifying the antecedents and the consequences of intelligent physical robot use, and by proposing potential future research agendas in the specific area based on the research findings in the literature and the identified knowledge gaps.publishedVersionPeer reviewe