Trust in socially assistive robots: Considerations for use in rehabilitation

Langer, Allison; Polak, Ronit Feingold; Mueller, Oliver; Kellmeyer, Philipp; Levy-Tzedek, Shelly

doi:10.1016/j.neubiorev.2019.07.014

Cited by 121 publications

(108 citation statements)

References 59 publications

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“…But, as the ageing population increases, it will be necessary (and this will almost be a compulsion in the future) to provide care through social robots because carer personals are limited. Studies have shown that prior experience with robots leads to higher levels of trust and more positive attitudes towards social robots [48][49][50][51][52]. In an experiment measuring older adults' acceptance of the social robot, NAO, participants tended to be neutral in their perceptions of NAO prior to interactions, but statistically significantly more positive after 30-60 min sessions with NAO [53].…”

Section: Effects Of Physical Appearance Of Robotsmentioning

confidence: 99%

What Makes a Social Robot Good at Interacting with Humans?

Onyeulo

Gandhi

2020

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This paper discusses the nuances of a social robot, how and why social robots are becoming increasingly significant, and what they are currently being used for. This paper also reflects on the current design of social robots as a means of interaction with humans and also reports potential solutions about several important questions around the futuristic design of these robots. The specific questions explored in this paper are: “Do social robots need to look like living creatures that already exist in the world for humans to interact well with them?”; “Do social robots need to have animated faces for humans to interact well with them?”; “Do social robots need to have the ability to speak a coherent human language for humans to interact well with them?” and “Do social robots need to have the capability to make physical gestures for humans to interact well with them?”. This paper reviews both verbal as well as nonverbal social and conversational cues that could be incorporated into the design of social robots, and also briefly discusses the emotional bonds that may be built between humans and robots. Facets surrounding acceptance of social robots by humans and also ethical/moral concerns have also been discussed.

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Section: Effects Of Physical Appearance Of Robotsmentioning

confidence: 99%

What Makes a Social Robot Good at Interacting with Humans?

Onyeulo

Gandhi

2020

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“…The latter is tightly associated with trust, in trusting that the device will not make a user fall [40,47]. Trust is one of the leading values in human-robot interaction (HRI) [48][49][50][51]. A lack of trust in robotics often relates to the technical functionality and the perception of safety the user has.…”

Section: mentioning

confidence: 99%

“…Age and cultural preferences also affect trust. For instance, there are situations where older adults were less receptive to robots as compared to younger adults [51,53]. Because of these multiple and multifaceted design considerations, it is not difficult to understand how design challenges could present a barrier, albeit surmountable, to commercial production.…”

Section: mentioning

confidence: 99%

Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access

Fosch‐Villaronga

Čartolovni

Pierce

2020

Paladyn, Journal of Behavioral Robotics

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Pediatric access to exoskeletons lags far behind that of adults. In this article, we promote inclusiveness in exoskeleton robotics by identifying and addressing challenges and barriers to pediatric access to this potentially life-changing technology. We first present available exoskeleton solutions for upper and lower limbs and note the variability in the absence of these. Next, we query the possible reasons for this variability in access, explicitly focusing on children, who constitute a categorically vulnerable population, and also stand to benefit significantly from the use of this technology at this critical point in their physical and emotional growth. We propose the use of a life-based design approach as a way to address some of the design challenges and offer insights toward a resolution regarding market viability and implementation challenges. We conclude that the development of pediatric exoskeletons that allow for and ensure access to health-enhancing technology is a crucial aspect of the responsible provision of health care to all members of society. For children, the stakes are particularly high, given that this technology, when used at a critical phase of a child’s development, not only holds out the possibility of improving the quality of life but also can improve the long-term health prospects.

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“…Alternatives to augment care with the use of robots can help reduce this stress. There are SARs: Robots that are trained to assist patients with AD by the means of social interactions are used [ 35 , 36 ], and the rehabilitation process that they follow involves the crucial factor of assessing the patient’s movement rate and intensity, associated with their involvement with the robots [ 35 , 37 – 39 ]. Besides SARs, there are some humanoid robots that have activity monitoring functions, which learn by matching algorithmic parameters with a gesture viewed through its camera and repeatedly executed by different persons to permit variations.…”

Section: Impacts Of Technology In Assisting Patients With Ad and Ad Dmentioning

confidence: 99%

Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

Naik

Mehta

Shah

2020

Vis. Comput. Ind. Biomed. Art

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Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

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Trust in socially assistive robots: Considerations for use in rehabilitation

Cited by 121 publications

References 59 publications

What Makes a Social Robot Good at Interacting with Humans?

What Makes a Social Robot Good at Interacting with Humans?

Promoting inclusiveness in exoskeleton robotics: Addressing challenges for pediatric access

Denouements of machine learning and multimodal diagnostic classification of Alzheimer’s disease

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