Personalization of a Social Health Care Companion
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2023-07-01
Language
en
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Abstract
The objective of this thesis is to gain insights into the benefits, limitations, and future
directions of personalization techniques in social robotics. The study focuses specifically
on Lizz, a digital healthcare companion, within the context of exercise recommendation
during rehabilitation. The aim is to contribute to the understanding of this field
by exploring the various aspects of personalization in social robotics. Three different
prototypes are tested to showcase different mechanisms of personalization: a rule-based
system without user feedback, a combination of rule-based and reinforcement learning
with user feedback, and a combination of rule-based and reinforcement learning with
user feedback and other dynamic factors. The prototypes aim to demonstrate the advantages
and disadvantages of different personalization approaches and contribute to
improved well-being and recovery outcomes. The findings indicate that greater personalization
leads to exercise recommendations that better matches individual preferences
and needs. However, it is worth noting that higher levels of personalization require
additional effort for the model to reach optimal performance. Future studies could
explore the generalizability of personalization approaches in different application domains.
Additionally, conducting user studies in real-world settings is necessary to gain
deeper insights into practical implementation challenges.
Keywords: Socially Assistive Robotics; Personalization; User Profiling; Rule-based
systems; Reinforcement Learning.
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Faculteit der Sociale Wetenschappen