NDLI: Exploring dynamic difficulty adjustment mechanism for rehabilitation tasks using physiological measures and subjective ratings

Content Provider	IET Digital Library
Author	Ozkul, Fatih Palaska, Yunus Masazade, Engin Barkana, Duygun Erol
Abstract	Task engagement is a key factor in sustaining patients’ participation in rehabilitation. Adaptive task difficulty level adjustment techniques are designed to determine the appropriate exercise difficulty level in where subjects are appropriately challenged and engaged without causing any distress. Such adaptive difficulty adjustment within rehabilitation tasks has the potential to individualise training. In this study, the authors have compared two dynamic difficulty level adjustment algorithms, partially ordered set master (POSM) and increment/decrement one level (IDOL), those change the difficulty levels for each individual adaptively based on his/her performance. These two algorithms are integrated into the robot-assisted rehabilitation system, RehabRoby, and their functionality is explored via a small user study with 20 healthy subjects. The subjects were asked to perform a computer-based fruit picker game using RehabRoby under these two algorithms. The impacts of the adaptation algorithms are evaluated in terms of engagement of the subjects by looking at their physiological signals, performance (score), and survey results. Experiments show that although POSM on the average suggests easier difficulty levels to the subjects than IDOL, the subjects experience a wider range of difficulty levels in POSM that may help them to become more engaged in the game, which can also be observed by lower skin temperatures of the subjects.
Starting Page	378
Ending Page	386
Page Count	9
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 3, May (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5241
Journal	IET Signal Processing
Publisher Date	2018-12-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	20 Healthy Subjects Adaptation Algorithm Adaptive Difficulty Adjustment Adaptive Task Difficulty Level Adjustment Technique Appropriate Exercise Difficulty Level Biological And Medical Control System Biology And Medical Computing Computer Games Computer-based Fruit Picker Game Dynamic Difficulty Adjustment Mechanism Dynamic Difficulty Level Adjustment Algorithm Easier Difficulty Levels Human Factors Increment/decrement One Level Individual Adaptively Knowledge Engineering Technique Learning in AI Medical Robotics Patient Care And Treatment Patient Rehabilitation Physiological Measures POSM Psychology Rehabilitation Task Robot-assisted Rehabilitation System Robotics Skin Statistics Subjective Ratings Task Engagement
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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