Design and Evaluation of a Self Adaptive Architecture for Upper-Limb Rehabilitation

Héloir, Alexis; Nunnari, Fabrizio; Haudegond, Sylvain; Havrez, Clémentine; Lebrun, Yoann; Kolski, Christophe

doi:10.1007/978-3-662-48645-0_17

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…The former requires therapist intervention which is time-consuming, burdensome, and requires specialized training. The latter modifies the therapeutic content based on predefined rules, which are brittle and cannot account for all possible individuals (Abdessalem and Frasson 2017;Heloir et al 2014). In contrast, an EDPCG framework could be used in computer-assisted therapy as an automatic content generation tool that adapts to satisfying individuals' therapeutic needs.…”

Section: Introductionmentioning

confidence: 99%

Arachnophobia Exposure Therapy Using Experience-Driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)

Mahmoudi-Nejad¹,

Guzdial²,

Boulanger³

2021

AIIDE

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Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. While there exist approaches to automatically adapt therapeutic content to a patient, they rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (EDPCGRL) to generate virtual spiders to match an individual patient. In this initial implementation, and due to the ongoing pandemic, we make use of virtual or artificial humans implemented based on prior arachnophobia psychology research. Our EDPCGRL method is able to more quickly adapt to these virtual humans with high accuracy in comparison to existing, search-based EDPCG approaches.

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Section: Introductionmentioning

confidence: 99%