Research on Innovative Design of Smart Water Dispensers Based on User Behavior Data
Abstract
Objective: To address the issues of homogenized interactions and static services in smart drinking water devices, this study explores a data-driven design methodology integrating qualitative and quantitative needs analysis with artificial intelligence technologies. Methods: User requirements from 285 participants were systematically organized and quantified using the KJ method (Kawakita Jiro method) and AHP (Analytic Hierarchy Process) hierarchical analysis, establishing design objectives centered on water purity (weight 0.218) and single-use completion time (weight 0.172). Based on this, a context-aware intelligent service framework was constructed. It integrates Internet of Things (IoT) sensors and Radio Frequency Identification (RFID) technology for seamless recognition, employs a Long Short-Term Memory (LSTM) model to predict user drinking behavior, and utilizes reinforcement learning (RL) strategies to generate adaptive services. Results: Testing of the developed prototype system with 35 users revealed: System Usability Scale (SUS) score of 82.5, reaching an excellent level; Task completion time decreased by approximately 40%; User Experience Questionnaire (UEQ) scores showed significant improvements across dimensions including efficiency and reliability. Conclusion: This study demonstrates the effectiveness of integrating design research methodologies with computational intelligence technologies. The proposed “demand quantification-behavior modeling-intelligent…
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