Interaction Design Strategies of AI Smart Glasses for Older Workers: An Embodied Cognition Perspective and Usability Evaluation

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests.

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Publication Details

Journal
Applied Sciences
Published
2026-03-13
DOI
https://doi.org/10.3390/app16062768
Primary Topic
Technology Use by Older Adults
Type
article
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article

Interaction Design Strategies of AI Smart Glasses for Older Workers: An Embodied Cognition Perspective and Usability Evaluation

D. Y. Li, Yan Guo
Applied Sciences
Technology Use by Older Adults
article

Interaction Design Strategies of AI Smart Glasses for Older Workers: An Embodied Cognition Perspective and Usability Evaluation

D. Y. Li, Yan Guo
article en

Abstract

Given the global aging of the population and the rising retirement age, the development of cross-generational technologies is crucial for a sustainable workforce supply. While AI-powered smart glasses can provide continuous cognitive support, current industrial solutions often prioritize work efficiency at the expense of the physical, cognitive, and socio-emotional needs of older workers. This study employed a mixed-methods approach grounded in embodied cognition. First, semi-structured interviews with ten participants were analyzed using grounded theory to develop a four-dimensional model of embodied experience: Perceived Pressure, Action Feedback, Collaboration Embedding, and Belonging. Subsequently, four interaction strategies—Rhythm Control, Transparent Feedback, Non-intrusive Assistance, and Legible Privacy & Social Signaling—were formulated and implemented. A high-fidelity prototype was developed to embody these strategies. Finally, a team of eight multidisciplinary experts evaluated the device using the System Usability Scale (SUS) and a proprietary twelve-item questionnaire. The results showed that the device’s overall usability was borderline acceptable (SUS = 68.13 ± 8.94). While the devices received stronger ratings for Control & Safety, the ratings for dignity and social acceptance were comparatively low. These findings contribute to the development of wearable device operation strategies suitable for users of different generations, and underline the importance of social and emotional compatibility as a prerequisite for future practice tests.

Applied SciencesVol. 16(6)
Jiangnan University (CN)
Openalex Percentile: Top 36%
Technology Use by Older Adults
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Interaction Design Strategies of AI Smart Glasses for Older Workers: An Embodied Cognition Perspective and Usability Evaluation — D. Y. Li, Yan Guo · Applied Sciences (2026) | TGRS Research Map | TGRS