In the last decade, socially assistive robots have been used in therapeutic treatments for individuals diagnosed with Autism Spectrum Disorders (ASDs). Preliminary studies have demonstrated positive results using the Penguin for Autism Behavioral Intervention (PABI) developed by the AIM Lab at WPI to assist individuals diagnosed with ASDs in Applied Behavioral Analysis (ABA) therapy treatments. In recent years, power-efficient embedded AI computing devices have emerged as a powerful technology by reducing the complexity of the hardware platforms while providing support for parallel models of computation. This new hardware architecture seems to be an important step in the improvement of socially assistive robots in ABA therapy. In this thesis, we explore the use of a power-efficient embedded AI computing device and pre-trained deep learning models to improve PABI’s performance. Five main contributions are made in this work. First, a robot-enhanced ABA therapy framework is designed. Second, a multilayer pattern software architecture for a robot-enhanced ABA therapy framework is explored. Third, a multifactorial experiment is completed in order to benchmark the performance of three popular deep learning frameworks over the AI computing device. Experimental results demonstrate that some deep learning frameworks utilize the resources of GPU power while others utilize the multicore ARM-CPU system of the device for its parallel model of computation. Fourth, the robustness of state-of-the-art pre-trained deep learning models for feature extraction is analyzed and contrasted with the previous approach used by PABI. Experimental results indicate that pre-trained deep learning models overcome the traditional approaches in some fields; however, combining different pre-trained models in a process reduces its accuracy. Fifth, a patient-tracking algorithm based on an identity verification approach is developed to improve the autonomy, usability, and interactions of patients with the robot. Experimental results show that the developed algorithm has the potential to perform as well as the previous algorithm used by PABI based on a deep learning classifier approach.

Bakgrund: Demens är en sjukdom som drabbar en stor del av en alltmer åldrande befolkning i världen. Patienter som lider av demens kan drabbas av olika symtom som t.ex. kommunikationssvårigheter, personlighetsförändringar samt emotionell avtrubbning. Dessa symtom kan vara svåra att behandla och påverkar patienter, anhöriga och vårdpersonal. Tidigare forskning har visat att socialt assisterande robotar kan ha en god inverkan på människor som lider av demenssjukdom. Syfte: Syftet med denna litteraturstudie är att beskriva vilken inverkan på hälsan socialt assisterande robotar har hos patienter som lider av demenssjukdom.Metod: En litteraturstudie grundat på tio vetenskapliga artiklar, som tagits fram utifrån syftet. Resultat: Resultatet presenterades utifrån två kategorier, Psykosocial hälsa och Livskvalitet. Interventioner med socialt assisterande robotar hade en god inverkan på den psykosociala hälsan, medan livskvalitet påverkades med varierat resultat beroende på vilket stadie av demens som den drabbade befinner sig i. Slutsats: Sammanfattningsvis ges ett intryck av att socialt assisterande robotar kan hjälpa vårdpersonal i deras omvårdnadsarbete för patienter med demens, och att dessa robotar har en övervägande god inverkan på demensdrabbades hälsa. Betydelse: Kunskap om vad socialt assisterande robotar har för inverkan på hälsa hos demensdrabbade är angeläget, för att i framtiden kunna implementera omvårdnadsåtgärder med denna typ av robotar. / Background: Dementia is a disease which affects a large part of an increasingly aging population in the world. Patients suffering from dementia may suffer from various symptoms such as communication difficulties, personality changes and emotional blunting. These symptoms can be difficult to treat and affects patients, relatives and health professionals. Previous research has shown that socially assistive robots could have a good effect on people suffering from dementia. Aim: The purpose of this study is to describe the impact on health of socially assistive robots in patients suffering from dementia. Method: A literature study based on ten scientific articles, which has been devised based on the purpose. Results: The results are presented on the basis of two categories, psychosocial health and quality of life. Interventions with socially assistive robots had a good impact on the psychosocial health, while quality of life was affected with varied results depending on the stage of dementia that the patient is in. Conclusion: In conclusion, the given impression is that socially assistive robots could help health professionals in their nursing work for patients with dementia, and that these robots have a predominantly good effect on demented people’s health. Significance: The knowledge of how socially assistive robots impact the health of people who suffers from dementia is imperative, to eventually be able to implement nursing interventions with this type of robots.

Due to the world’s rapidly growing elderly population, dementia is becoming increasingly prevalent. This poses considerable health, social, and economic concerns as it impacts individuals, families and healthcare systems. Current research has shown that cognitive interventions may slow the decline of or improve brain functioning in older adults. This research investigates the use of intelligent socially assistive robots to engage individuals in person-centered cognitively stimulating activities. Specifically, in this thesis, a novel learning-based control architecture is developed to enable socially assistive robots to act as social motivators during an activity. A hierarchical reinforcement learning approach is used in the architecture so that the robot can learn appropriate assistive behaviours based on activity structure and personalize an interaction based on the individual’s behaviour and user state. Experiments show that the control architecture is effective in determining the robot’s optimal assistive behaviours for a memory game interaction and a meal assistance scenario.

Due to the world’s rapidly growing elderly population, dementia is becoming increasingly prevalent. This poses considerable health, social, and economic concerns as it impacts individuals, families and healthcare systems. Current research has shown that cognitive interventions may slow the decline of or improve brain functioning in older adults. This research investigates the use of intelligent socially assistive robots to engage individuals in person-centered cognitively stimulating activities. Specifically, in this thesis, a novel learning-based control architecture is developed to enable socially assistive robots to act as social motivators during an activity. A hierarchical reinforcement learning approach is used in the architecture so that the robot can learn appropriate assistive behaviours based on activity structure and personalize an interaction based on the individual’s behaviour and user state. Experiments show that the control architecture is effective in determining the robot’s optimal assistive behaviours for a memory game interaction and a meal assistance scenario.

When using socially assistive robots (SAR), it is important that their personality is personalised so that it suits their user. This work investigated how the customisation of the personality of a SAR health coach is perceived when done by the users themselves or their friends via ChatGPT. Therefore, the research question in this study is: How is personalised dialogue for a social robot perceived when generated via ChatGPT, by users and their friends? This study uses a mixed method approach, where participants got to test their own and their friend’s personalised version. The qualitative data was analysed using a thematic analysis. Sixteen participants were recruited.The result from this study showed that it does not matter who is customising the SAR, nor does one make a more persuasive version than the other, and when customising the personality, participants explained what they or their friend preferred. However, it is important to remember that the individual’s preference matters.