Towards Effective Robot-Assisted Photo Reminiscence: Personalizing Interactions Through Visual Understanding and Inferring

Gamborino, Edwinn; Ruiz, Alberto Herrera; Wang, Jing-Fen; Tseng, Tsung-Yuan; Yeh, Su‐Ling; Fu, Li‐Chen

doi:10.1007/978-3-030-77080-8_27

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…For instance, AI could generate relevant questions and images to assist older adults in recalling specific life events. However, to the best of our knowledge, only a few studies have investigated AI-based solutions such as chatbots to support reminiscence [15,28], which, however, have mainly focused on technical solutions and do not consider user perspectives. Building upon prior work, in this study, we seek to investigate older adults' perspectives on generative AI for music reminiscence and further explore the design of generative AI to enhance their reminiscence experiences.…”

Section: Generative Aimentioning

confidence: 99%

“…In recent decades, HCI researchers have investigated the use of technology for supporting reminiscence [14,45,64,81,91], and different types of technology-based tools have been designed to enable people to re-experience their personal digital data like social media content [63], emails [32], photos [2,20,64], sound [37,42] and music [72], to trigger their memories. Moreover, several researchers have proposed AI-driven solutions like chatbots that aim to offer human-like and personalized interactions [15,28,58], for realizing the potential of AI technologies to aid the reminiscing process.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older Adults

Jin,

Cai,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Music-based reminiscence has the potential to positively impact the psychological well-being of older adults. However, the aging process and physiological changes, such as memory decline and limited verbal communication, may impede the ability of older adults to recall their memories and life experiences. Given the advanced capabilities of generative artificial intelligence (AI) systems, such as generated conversations and images, and their potential to facilitate the reminiscing process, this study aims to explore the design of generative AI to support music-based reminiscence in older adults. This study follows a user-centered design approach incorporating various stages, including detailed interviews with two social workers and two design workshops (involving ten older adults). Our work contributes to an in-depth understanding of older adults' attitudes toward utilizing generative AI for supporting music-based reminiscence and identifies concrete design considerations for the future design of generative AI to enhance the reminiscence experience of older adults. CCS CONCEPTS• Human-centered computing → User studies; Accessibility design and evaluation methods.

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Section: Generative Aimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Design of Generative AI in Supporting Music-based Reminiscence for Older Adults

Jin,

Cai,

Chen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…In the meantime, social robots are expected to have multiple roles for their multi-functionalities despite interacting with the same individual [83]. For instance, the social robot in our study, RoBoHon, is expected to be applied under various HRI scenarios, including providing emotional comfort for children [84] and eliciting memory retrieval for seniors [85] with its wide range of social abilities (e.g., chatting, joking, and dancing). Therefore, future research is warranted regarding how different social features affect the impressions and expectations toward a robot with various roles.…”

Section: Future Directionsmentioning

confidence: 99%

Social Robots for Evaluating Attention State in Older Adults

Chen

Yeh

Huang

et al. 2021

Sensors

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Sustained attention is essential for older adults to maintain an active lifestyle, and the deficiency of this function is often associated with health-related risks such as falling and frailty. The present study examined whether the well-established age-effect on reducing mind-wandering, the drift to internal thoughts that are seen to be detrimental to attentional control, could be replicated by using a robotic experimenter for older adults who are not as familiar with online technologies. A total of 28 younger and 22 older adults performed a Sustained Attention to Response Task (SART) by answering thought probes regarding their attention states and providing confidence ratings for their own task performances. The indices from the modified SART suggested a well-documented conservative response strategy endorsed by older adults, which were represented by slower responses and increased omission errors. Moreover, the slower responses and increased omissions were found to be associated with less self-reported mind-wandering, thus showing consistency with their higher subjective ratings of attentional control. Overall, this study demonstrates the potential of constructing age-related cognitive profiles with attention evaluation instruction based on a social companion robot for older adults at home.

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