Enhancing Hand Rehabilitation Through Interactive Design

The demand for hand rehabilitation services is increasing currently, and there are many choices of hand rehabilitation products available on the market. Here, we report a flexible curvature sensor-based data glove, equipped with a built-in light trigger system, which is expected to provide active signal feedback for better human machine interface experience. By analyzing user needs and leveraging advancements in human-computer interaction, this new design not only facilitates physical recovery but also fosters a sense of ownership and motivation among patients. The incorporation of real-time feedback mechanisms further amplifies the efficacy of the rehabilitation process, propelling patients toward greater independence and self-assurance. Specially from an emotional point of view, the elements, such as functionality, design, color, material, and human-computer interaction, based on user needs, would be analyzed to summarize some core requirements. Then the performance of hand movements is integrated into the rehabilitation process through visual feedback to address the problem of monotony during training. This design can leverage the feedback mechanism between visual cues and hand movements, enhancing the sensory interaction experience between individuals and the product. Finally, it is proved by experiment this proposed design is very potential to help improve patients’ motivation and self-efficacy in training, thereby enhancing the effectiveness of rehabilitation. By combining these elements, we believed that the design is expected to make up some emotional support for better human machine interface experience.

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Publication Details

Journal
CAAI Artificial Intelligence Research
Published
2024-11-07
DOI
https://doi.org/10.26599/air.2024.9150041
Citations
1
Primary Topic
Stroke Rehabilitation and Recovery
Type
article
Field-Weighted Citation Impact
0.25

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article

Enhancing Hand Rehabilitation Through Interactive Design

Jiaqi Fan, Xiaotian Sun, Sitong Lu
1 citations
CAAI Artificial Intelligence Research
Stroke Rehabilitation and Recovery
0.25
article

Enhancing Hand Rehabilitation Through Interactive Design

Jiaqi Fan, Xiaotian Sun, Sitong Lu
article en
1 citations

Abstract

The demand for hand rehabilitation services is increasing currently, and there are many choices of hand rehabilitation products available on the market. Here, we report a flexible curvature sensor-based data glove, equipped with a built-in light trigger system, which is expected to provide active signal feedback for better human machine interface experience. By analyzing user needs and leveraging advancements in human-computer interaction, this new design not only facilitates physical recovery but also fosters a sense of ownership and motivation among patients. The incorporation of real-time feedback mechanisms further amplifies the efficacy of the rehabilitation process, propelling patients toward greater independence and self-assurance. Specially from an emotional point of view, the elements, such as functionality, design, color, material, and human-computer interaction, based on user needs, would be analyzed to summarize some core requirements. Then the performance of hand movements is integrated into the rehabilitation process through visual feedback to address the problem of monotony during training. This design can leverage the feedback mechanism between visual cues and hand movements, enhancing the sensory interaction experience between individuals and the product. Finally, it is proved by experiment this proposed design is very potential to help improve patients’ motivation and self-efficacy in training, thereby enhancing the effectiveness of rehabilitation. By combining these elements, we believed that the design is expected to make up some emotional support for better human machine interface experience.

CAAI Artificial Intelligence Research
North China University of Technology (CN), University of Tsukuba (JP)
National Natural Science Foundation of China
Openalex Percentile: Top 37%
Stroke Rehabilitation and Recovery
0.25
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