ROBIN, a Telepresence Robot to Support Older Users Monitoring and Social Inclusion: Development and Evaluation

Cortellessa, Gabriella; Fracasso, Francesca; Sorrentino, Alessandra; Orlandini, Andrea; Bernardi, Giulio; Coraci, Luca; Benedictis, Riccardo De; Cesta, Amedeo

doi:10.1089/tmj.2016.0258

Cited by 48 publications

(40 citation statements)

References 22 publications

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“…Randomized trial [2,3,11,48,55,56] Pilot study [12,13,14,21,26,31,35,39,40,43,44,45,46,47,53,54] Observational study [28,30] Preliminary studies for assessing usability or codesign choices [1,5,9,15,18,20,23,24,27,34,49] System proposal (no user study) [6,16,17,19,22,25,29,36,52] Study protocol [4,7,8,32,33,41] Field study [10,…”

Section: Type Of Study Papersmentioning

confidence: 99%

“…Hence, because of a limited understanding of how to design and implement e-coaching interventions for older adults, as well as limited evidence regarding their effectiveness in promoting healthrelated outcomes, we refrained from excluding further papers and kept all 56 for our analysis. In fact, among the 56 papers selected as eligible for the analysis, five reported results of randomized controlled trials [2,3,11,55,56] and one study was a clinical trial [48], six reported protocols for future randomized trials [4, 7, 8, 32, 33, 41], 16 presented results of pilot studies [12,13,14,21,26,31,35,39,40,43,44,45,46,47,53,54] and two were observational studies [28,30], while [38] presented a pre-post intervention study. Eleven presented the results of a preliminary assessment of a system in terms of usability or co-design choice [1,5,9,15,18,20,23,24,27,34,49], four [10,42,50,51] presented a field study for deriving the requirements of a coaching system, and nine presented only a proposal of a coaching system or an architectural implementation, without any user study [6,…”

Section: Type Of Study Papersmentioning

confidence: 99%

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Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

et al. 2020

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Virtual Coaches, also known as e-coaches, are a disruptive technology in healthcare. Indeed, among other usages, they might provide cost-effective solutions for increasing human wellbeing in different domains, such as physical, nutritional, cognitive, social, and emotional. This paper presents a systematic review of virtual coaches specifically aimed at improving or maintaining older adults' health in the aforementioned domains. Such digital systems assume various forms, from classic apps, to more advanced conversational agents or robots. Fifty-six articles describing a virtual coach for older adults and aimed at improving their wellbeing were identified and further analyzed. In particular, we presented how previous studies defined their virtual coaches, which behavioral change models and techniques they adopted and the overall system architecture, in terms of monitoring solutions, processing methods and modalities for intervention delivery. Our results show that few thorough evaluations of e-coaching systems have been conducted, especially regarding multi-domain coaching approaches. Through our analysis, we identified the wellbeing domains that should be addressed in future studies as well as the most promising behavior change models and techniques and coaching interfaces. Previous work illustrates that older adults often appreciate conversational agents and robots. However, the lack of a multidomain intervention approach in the current literature motivates us to seek to define future solutions.

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Section: Type Of Study Papersmentioning

confidence: 99%

Section: Type Of Study Papersmentioning

confidence: 99%

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

et al. 2020

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“…A robot named ROBIN is integrated into a sensorrich environment and continuously monitors the physical and psychological wellbeing of older persons living alone, with caregivers communicating through it to their assisted persons. This enhanced telepresence robot, as assessed by users for its suitability for supporting social interaction and providing motivational feedback on health-related aspects, was judged to be usable and interactively pleasing [37]. It is thus not surprising that a robot-administered cognitive testing has been well accepted by community-dwelling older adults [38].…”

Section: Assisted Technology In Geriatric Care and Rehabilitation: Romentioning

confidence: 99%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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“…A direct relationship was found between estimated emotional arousal and robot velocity. ROBIN, a telepresence robot, design by Cortellessa et al [118] to measure physical and psychological health of elderly people. The evaluation showed that the interaction of ROBIN with elderly people was pleasant and usable.…”

Section: Human Robot Interaction (Hri) Studiesmentioning

confidence: 99%

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

Baig¹,

Kavakli²

2019

MTI

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Psycho-physiological analysis has gained greater attention in the last few decades in various fields including multimodal systems. Researchers use psychophysiological feedback devices such as skin conductance (SC), Electroencephalography (EEG) and Electrocardiography (ECG) to detect the affective states of the users during task performance. Psycho-physiological feedback has been successful in detection of the cognitive states of users in human-computer interaction (HCI). Recently, in game studies, psycho-physiological feedback has been used to capture the user experience and the effect of interaction on human psychology. This paper reviews several psycho-physiological, cognitive, and affective assessment studies and focuses on the use of psychophysiological signals in estimating the user's cognitive and emotional states in multimodal systems. In this paper, we review the measurement techniques and methods that have been used to record psycho-physiological signals as well as the cognitive and emotional states in a variety of conditions. The aim of this review is to conduct a detailed study to identify, describe and analyze the key psycho-physiological parameters that relate to different mental and emotional states in order to provide an insight into key approaches. Furthermore, the advantages and limitations of these approaches are also highlighted in this paper. The findings state that the classification accuracy of >90% has been achieved in classifying emotions with EEG signals. A strong correlation between self-reported data, HCI experience, and psychophysiological data has been observed in a wide range of domains including games, human-robot interaction, mobile interaction, and simulations. An increase in β and γ-band activity have been observed in high intense games and simulations.

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ROBIN, a Telepresence Robot to Support Older Users Monitoring and Social Inclusion: Development and Evaluation

Cited by 48 publications

References 22 publications

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

Virtual Coaches for Older Adults’ Wellbeing: A Systematic Review

Artificial Intelligence in the healthcare of older people

A Survey on Psycho-Physiological Analysis & Measurement Methods in Multimodal Systems

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