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Interaction Modalities Used on Serious Games for Upper Limb Rehabilitation: A Systematic Review
Carlos Alberto Aguilar-Lazcano1, Ericka Janet Rechy-Ramirez1, Huosheng Hu2
1Research Center for Artificial Intelligence, Universidad Veracruzana, Xalapa, Mexico.
This review analyzes serious games for upper limb rehabilitation, finding vision systems most common. Limitations include lighting for vision systems and accuracy for no-vision systems.
Area of Science:
- Rehabilitation Medicine
- Human-Computer Interaction
- Game Design
Background:
- Serious games offer engaging platforms for physical therapy.
- Upper limb rehabilitation is crucial for recovery from various injuries and conditions.
- Interaction modalities significantly influence the effectiveness of serious games in rehabilitation.
Purpose of the Study:
- To systematically review and analyze the state-of-the-art interaction modalities used in serious games for upper limb rehabilitation.
- To identify trends, common practices, and limitations of current interaction methods.
- To suggest future research directions based on identified gaps.
Main Methods:
- Systematic literature search in IEEE Xplore and Web of Science databases.
- Application of PRISMA and QualSyst protocols for article selection and quality assessment.
- Inclusion criteria: English language, ≥4 pages, serious games, upper limb rehabilitation focus, published 2007-2017.
Main Results:
- 33 articles met inclusion criteria from 121 retrieved.
- Three main interaction modalities identified: vision systems (42.4%), complementary vision systems (30.3%), and no-vision systems (27.2%).
- Vision systems (using Kinect) and no-vision systems showed similar quality scores (86%); shoulder rehabilitation was most common (19%).
Conclusions:
- Vision systems are the most prevalent interaction modality in serious games for upper limb rehabilitation.
- Limitations include environmental sensitivity (lighting) for vision systems and accuracy issues for no-vision systems.
- Future research should explore finger rehabilitation, adaptive difficulty, and multi-sensor data fusion.
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