Human-Computer Interaction
Priority Analysis of Wandering Alarm Device Functions for Elderly Users Based on the KANO-QFD Model with Machine Learning Algorithm
This study addresses the critical need for improving anti-wandering devices for the elderly by integrating the KANO and QFD models with machine learning. It identifies core, expected, and attractive functionalities based on a large-scale survey (506 samples), overcoming limitations of traditional surveys and technology-driven innovations. The machine learning algorithm significantly reduces false alarms (from 30.56% to 10.02%) and improves classification accuracy (from 61.76% to 67.64%), leading to more precise, user-centric device development. This framework shifts product development from 'technology stacking' to 'precision services,' ensuring devices respect dignity and enhance safety in an aging society.
Executive Impact
Our analysis reveals profound implications for optimizing anti-wandering device development, driven by data-backed insights.
0
Accuracy Improvement
0
False Alarm Reduction
0
Survey Participants
0
Core Requirements Identified
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Human-Computer Interaction
Introduction & Core Insights
This section elaborates on the findings related to Human-Computer Interaction and presents specific enterprise applications derived from the research.
Integrated KANO-QFD Model
User-Centric DesignThe KANO-QFD model effectively translates elderly user needs into engineering characteristics, bridging the gap between technological innovation and user experience. It systematically prioritizes functionalities based on user satisfaction and feasibility.
Enterprise Process Flow
Raw Data Collection (Surveys, Feedback)
→
Data Cleaning (Missing Values, Duplicates)
→
Feature Engineering (Age, Gender, Location)
→
KANO-QFD-ML Classification
→
Dynamic Parameter Optimization
→
Improved Wandering Alarm Device
Performance Comparison: ML vs. Traditional
| Metric | Traditional Method | Machine Learning Algorithm |
|---|---|---|
| Accuracy (%) | 61.76 | 67.64 |
| Precision (%) | 61.05 | 67.65 |
| Recall (%) | 82.25 | 95.97 |
| F1-Score (%) | 74.36 | 80.70 |
| False Alarm Rate (%) | 30.56 | 10.02 |
Core Functional Requirements Identified
Problem: Traditional anti-wandering products suffer from homogeneity and lack of user-centric design, failing to meet the true needs of elderly users.
Solution: Through KANO-QFD, three core requirements were identified: real-time positioning (error <50m), one-touch SOS emergency calls, and waterproof/dustproof design. Other attractive demands include health monitoring and emotional interaction. Functions causing privacy intrusion or frequent false alarms were identified for removal.
Impact: This approach leads to 'precision services' by optimizing functionalities based on user priority, reducing unnecessary features, and enhancing user satisfaction and safety for elderly individuals.
Advanced AI ROI Calculator
Estimate the potential return on investment for integrating AI-driven user-centric design into your product development.
Estimated Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Roadmap
A structured approach to integrate KANO-QFD and Machine Learning into your product strategy.
Phase 1: Needs Assessment & Data Collection
Conduct extensive user surveys and interviews to gather qualitative and quantitative data on elderly needs for anti-wandering devices. Refine the KANO-QFD questionnaire.
Expected Duration: 1-2 Months
Phase 2: Model Integration & Training
Integrate KANO-QFD with a machine learning algorithm. Preprocess collected data for cleaning and feature engineering. Train the ML model to classify demands and optimize parameters.
Expected Duration: 2-3 Months
Phase 3: Prototype Development & Testing
Develop initial prototypes incorporating the prioritized functionalities. Conduct user acceptance testing (UAT) with target elderly users and caregivers. Iterate based on feedback.
Expected Duration: 3-4 Months
Phase 4: Refinement & Deployment
Refine the device based on testing, focusing on reducing false alarms and improving usability. Prepare for market deployment, ensuring privacy and ethical considerations are met.
Expected Duration: 2-3 Months
Ready to Transform Your Product Development?
Leverage AI and user-centric design to create innovative, highly effective solutions for an aging population.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat