Data-Driven Design of an Automatic Shower for the Elderly: Integrating the Kano Model and K-Means Clustering

Automated devices designed for elderly users have become increasingly important in supporting independent living and addressing age-related challenges. Among these technologies, automatic shower devices play a key role in enhancing personal hygiene and reducing safety risks associated with conventional showering. This study applied the Kano model to identify factors influencing customer satisfaction with an automated shower device designed for older adults. Expert input was used to define and evaluate 25 quality elements across six dimensions, including washing, cleaning, safety, customer service, product-friendliness, and software–hardware integration. The results indicate that safety- and cleaning-related features—such as automatic disinfection, machine self-cleaning, automated emergency calls, emergency stop functions, and fall detection—exhibit high satisfaction coefficients, highlighting their importance in meeting elderly users’ expectations. To further explore variation in user preferences, K-means clustering was used to segment respondents based on their Kano response patterns. Three distinct user clusters were identified, each demonstrating different feature prioritization strategies. One cluster emphasized comfort-enhancing features, such as body massage and automatic warm-air drying, while another placed greater importance on essential safety functions, including fall detection and emergency alerts. By integrating the Kano model with K-Means clustering, this study proposes a data-driven, customer-centric design framework that supports informed decision-making in assistive technology development. The findings enable designers and manufacturers to balance core safety requirements with differentiated features tailored to diverse elderly user segments, ultimately enhancing usability, independence, and overall user satisfaction.