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<p>This study introduces a novel product design strategy tailored for older people, premised on the physiological and psychological transformations associated with aging. It further develops an algorithm for designing age-appropriate products, utilizing a multilevel behavioral modeling approach. The algorithm employs a self-attention network and a recurrent neural network for serial data modeling and model training, respectively. Subsequently, this algorithm is harmonized with the initial design strategy to create a comprehensive product design scheme tailored to the user model. The efficacy of the proposed model is evaluated by comparing it with six other models using a designated test dataset. As a practical application, an online shopping application was redesigned specifically for older users. Comparative analysis between the original “Mobile Taobao” and the redesigned “Mobile Taobao Senior Edition” revealed that task completion times were significantly reduced from 162.64 seconds to 98.895 seconds and further to 63.745 seconds post-improvement. Additionally, user experience metrics such as flow, page layout, graphic harmony, page color, and text readability were enhanced by increments of 1.195, 0.47, 1.44, 1.07, 0.545, and 1.755, respectively. These results substantiate the effectiveness of the proposed design approach in enhancing the usability of age-friendly products. Ultimately, this research aims to pioneer new concepts and methodologies for the design of internet-based products that cater to the aging population, thereby enhancing their accessibility and user experience.</p>
</abstract>

This study introduces a novel product design strategy tailored for older people, premised on the physiological and psychological transformations associated with aging. It further develops an algorithm for designing age-appropriate products, utilizing a multilevel behavioral modeling approach. The algorithm employs a self-attention network and a recurrent neural network for serial data modeling and model training, respectively. Subsequently, this algorithm is harmonized with the initial design strategy to create a comprehensive product design scheme tailored to the user model. The efficacy of the proposed model is evaluated by comparing it with six other models using a designated test dataset. As a practical application, an online shopping application was redesigned specifically for older users. Comparative analysis between the original “Mobile Taobao” and the redesigned “Mobile Taobao Senior Edition” revealed that task completion times were significantly reduced from 162.64 seconds to 98.895 seconds and further to 63.745 seconds post-improvement. Additionally, user experience metrics such as flow, page layout, graphic harmony, page color, and text readability were enhanced by increments of 1.195, 0.47, 1.44, 1.07, 0.545, and 1.755, respectively. These results substantiate the effectiveness of the proposed design approach in enhancing the usability of age-friendly products. Ultimately, this research aims to pioneer new concepts and methodologies for the design of internet-based products that cater to the aging population, thereby enhancing their accessibility and user experience.

Research on the Integration of User Modeling and Design Strategies in the Design of Aging-Ready Products

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study introduces a novel product design strategy tailored for older people, premised on the physiological and psychological transformations associated...

Research on the Integration of User Modeling and Design Strategies in the Design of Aging-Ready Products

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 08 mars 2024

Accepté: 06 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1650

Mots clés
Recurrent neural network, LSTM, Self-attention, Aging adaptation

© 2024 Qiongyao Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Integration of User Modeling and Design Strategies in the Design of Aging-Ready Products

Publié en ligne: 05 juil. 2024

Pages: -

Reçu: 08 mars 2024

Accepté: 06 juin 2024

DOI: https://doi.org/10.2478/amns-2024-1650

Mots clésRecurrent neural network, LSTM, Self-attention, Aging adaptation

© 2024 Qiongyao Wu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Recurrent neural network, LSTM, Self-attention, Aging adaptation