Assistive Device Selection for Operators with Disabilities in Industry 5.0 Manufacturing: A Kansei Engineering Approach

Human-centricity, a cornerstone of Industry 5.0, calls for accessible manufacturing environments. Inclusive workstations comprise assistive devices which facilitate information reception, task execution and inspection for operators with various disabilities. Nonetheless, state-of-the-art literature fails to acknowledge the negative impacts of such assistive devices on operators with disability. Additionally, whilst one assistive device can be used to enhance physical accessibility, it could cause adverse effects on one’s cognitive or sensory abilities. This study therefore addresses these gaps by adopting Kansei Engineering to capture the emotions and perceptions of 48 persons with disabilities towards assistive devices in manufacturing workstations. Both positive and negative emotions and perceptions are elicited, addressing another gap. A unique physical, cognitive and sensory accessibility index for eight assistive devices was created, with each index then employed in a Pareto optimisation approach which maximises accessibility whilst reducing the number of devices. The most optimal combination (achieving an 88% total accessibility score) comprised a microphone, a keyboard and mouse, a touch screen, projected instructions and a monitor. This study has contributed an innovative approach towards guiding which and how assistive devices can be chosen carefully to address diverse abilities, ensuring a more inclusive manufacturing shopfloor in the age of Industry 5.0.

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Publication Details

Journal
Procedia Computer Science
Published
2026-01-01
DOI
https://doi.org/10.1016/j.procs.2026.02.105
Primary Topic
Color perception and design
Type
article
Field-Weighted Citation Impact
0.00
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article

Assistive Device Selection for Operators with Disabilities in Industry 5.0 Manufacturing: A Kansei Engineering Approach

Amberlynn Bonello, Paul Refalo, Maria Victoria Gauci, Emmanuel Francalanza
Procedia Computer Science
Color perception and design
article

Assistive Device Selection for Operators with Disabilities in Industry 5.0 Manufacturing: A Kansei Engineering Approach

Amberlynn Bonello, Paul Refalo, Maria Victoria Gauci, Emmanuel Francalanza
article en

Abstract

Human-centricity, a cornerstone of Industry 5.0, calls for accessible manufacturing environments. Inclusive workstations comprise assistive devices which facilitate information reception, task execution and inspection for operators with various disabilities. Nonetheless, state-of-the-art literature fails to acknowledge the negative impacts of such assistive devices on operators with disability. Additionally, whilst one assistive device can be used to enhance physical accessibility, it could cause adverse effects on one’s cognitive or sensory abilities. This study therefore addresses these gaps by adopting Kansei Engineering to capture the emotions and perceptions of 48 persons with disabilities towards assistive devices in manufacturing workstations. Both positive and negative emotions and perceptions are elicited, addressing another gap. A unique physical, cognitive and sensory accessibility index for eight assistive devices was created, with each index then employed in a Pareto optimisation approach which maximises accessibility whilst reducing the number of devices. The most optimal combination (achieving an 88% total accessibility score) comprised a microphone, a keyboard and mouse, a touch screen, projected instructions and a monitor. This study has contributed an innovative approach towards guiding which and how assistive devices can be chosen carefully to address diverse abilities, ensuring a more inclusive manufacturing shopfloor in the age of Industry 5.0.

Procedia Computer ScienceVol. 277
University of Malta (MT)
Industry, innovation and infrastructure
Openalex Percentile: Top 17%
Color perception and design
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Assistive Device Selection for Operators with Disabilities in Industry 5.0 Manufacturing: A Kansei Engineering Approach — Amberlynn Bonello, Paul Refalo, et al. · Procedia Computer Science (2026) | TGRS Research Map | TGRS