Minimizing task-induced stress in cognitively stimulating activities using an intelligent socially assistive robot

Chan, Jeanie; Nejat, Goldie

doi:10.1109/roman.2011.6005275

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…One of those studies reported a high dropout rate (65%) [ 22 ], making it difficult to interpret the study’s reported positive outcomes. One study reported preliminary evidence supporting limited efficacy of a social robot in reducing patients’ stress [ 29 ]. Some studies reported mixed results for feasibility and satisfaction, with some participants reporting that they liked the AI technology but others reporting that they did not [ 23 , 26 , 47 , 49 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review

Xie¹,

Tao²,

Li³

et al. 2020

JMIR Med Inform

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Background Artificial intelligence (AI) has great potential for improving the care of persons with Alzheimer’s disease and related dementias (ADRD) and the quality of life of their family caregivers. To date, however, systematic review of the literature on the impact of AI on ADRD management has been lacking. Objective This paper aims to (1) identify and examine literature on AI that provides information to facilitate ADRD management by caregivers of individuals diagnosed with ADRD and (2) identify gaps in the literature that suggest future directions for research. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for conducting systematic literature reviews, during August and September 2019, we performed 3 rounds of selection. First, we searched predetermined keywords in PubMed, Cumulative Index to Nursing and Allied Health Literature Plus with Full Text, PsycINFO, IEEE Xplore Digital Library, and the ACM Digital Library. This step generated 113 nonduplicate results. Next, we screened the titles and abstracts of the 113 papers according to inclusion and exclusion criteria, after which 52 papers were excluded and 61 remained. Finally, we screened the full text of the remaining papers to ensure that they met the inclusion or exclusion criteria; 31 papers were excluded, leaving a final sample of 30 papers for analysis. Results Of the 30 papers, 20 reported studies that focused on using AI to assist in activities of daily living. A limited number of specific daily activities were targeted. The studies’ aims suggested three major purposes: (1) to test the feasibility, usability, or perceptions of prototype AI technology; (2) to generate preliminary data on the technology’s performance (primarily accuracy in detecting target events, such as falls); and (3) to understand user needs and preferences for the design and functionality of to-be-developed technology. The majority of the studies were qualitative, with interviews, focus groups, and observation being their most common methods. Cross-sectional surveys were also common, but with small convenience samples. Sample sizes ranged from 6 to 106, with the vast majority on the low end. The majority of the studies were descriptive, exploratory, and lacking theoretical guidance. Many studies reported positive outcomes in favor of their AI technology’s feasibility and satisfaction; some studies reported mixed results on these measures. Performance of the technology varied widely across tasks. Conclusions These findings call for more systematic designs and evaluations of the feasibility and efficacy of AI-based interventions for caregivers of people with ADRD. These gaps in the research would be best addressed through interdisciplinary collaboration, incorporating complementary expertise from the health sciences and computer science/engineering–related fields.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review

Xie¹,

Tao²,

Li³

et al. 2020

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…The studies using Q-learning are [ 3 , 13 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. These comprise studies using standard Q-learning [ 3 , 54 , 55 , 58 , 60 , 62 ], studies modify Q-learning for dealing with delayed reward [ 52 ], tuning the parameters for Q-learning such as

[ 13 , 34 , 52 ], dealing with decreasing human feedback over time [ 52 ], comparing their proposed algorithm with Q-learning [ 33 , 49 , 61 , 63 , 64 ], variation of Q-learning called Object Q-learning [ 64 , 65 , 66 ], combining Q-learning with fuzzy inference [ 67 ], SARSA [ 68 , 69 ], TD(

) [ 70 ], MAXQ [ 33 , 71 , 72 ], R-learning [ 32 ], and Deep Q-learning [ 35 , 36 , 73 , 74 ].…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

“…Social robots can facilitate their learning process through sensing and building representations of affective responses. This idea was used in [ 33 , 71 , 72 ]. In these studies, the socially assistive robot Brian 2.0 was employed as a social motivator by giving assistance, encouragement, and celebration in a memory game scenario.…”

Section: Categorization Of Rl Approaches In Social Robotics Based mentioning

confidence: 99%

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Reinforcement Learning Approaches in Social Robotics

Akalın

Loutfi

2021

Sensors

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This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

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“…The socially assistive robot Brian 2.0 was employed as a social motivator by giving assistance, encouragement, and celebration in a memory game scenario [18][19][20]. In the scenario, the participants interacted with the robot one-to-one with an objective to find the matching pictures on the memory card game (4x4 grid, 16 picture cards).…”

Section: Ensemblementioning

confidence: 99%

Reinforcement Learning Approaches in Social Robotics

Akalın¹,

Loutfi²

2020

Preprint

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There is a growing body of literature that formulates social human-robot interactions as sequential decision-making tasks. In such cases, reinforcement learning arises naturally since the interaction is a key component in both reinforcement learning and social robotics. This article surveys reinforcement learning approaches in social robotics. We propose a taxonomy that categorizes reinforcement learning methods in social robotics according to the nature of the reward function. We discuss the benefits and challenges of such methods and outline possible future directions.

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Minimizing task-induced stress in cognitively stimulating activities using an intelligent socially assistive robot

Cited by 5 publications

References 25 publications

Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review

Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review

Reinforcement Learning Approaches in Social Robotics

Reinforcement Learning Approaches in Social Robotics

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