RESEARCH-ARTICLE
Design of Intelligent Platform for Home-based Elderly Care Services Based on Artificial Intelligence
With the acceleration of population aging and the development of intelligent technologies, smart home-based elderly care services have become an important approach to addressing elderly care challenges. This platform integrates IoT sensing, big data analysis, and cloud computing to provide multi-dimensional intelligent services for elderly physiological health, daily life, and safety protection. Experimental verification demonstrates excellent service effectiveness in physiological indicator monitoring, abnormal behavior recognition, and emergency event handling, significantly improving the intelligence level and service quality of home-based elderly care.
Executive Impact & Key Performance Highlights
This intelligent platform significantly enhances home-based elderly care through advanced AI and IoT integration, leading to measurable improvements in safety, efficiency, and user satisfaction.
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Physiological Data Accuracy
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Emergency Response Time
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False Alarm Reduction
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Average User Satisfaction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
System Architecture
Core AI Models
Performance & Validation
Platform Architecture & Data Flow
The intelligent home care service platform adopts a sophisticated layered architecture design based on microservices methodology. It consists of four interconnected layers: perception for data acquisition, network for hybrid transmission, platform for AI analysis, and application for user interfaces. Data flows from sensors through edge nodes to the platform's data management subsystem, then processed by parallel AI models, ensuring real-time response.
Enterprise Process Flow
Perception Layer
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Network Layer
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Platform Layer
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Application Layer
| Feature | Advanced Platform | Traditional Systems |
|---|---|---|
| Data Sources | Multi-source heterogeneous (physio, environmental, cameras) | Limited, often manual input |
| Preprocessing | Edge filtering, Kalman filtering, sliding windows | Basic, prone to noise |
| Transmission Protocol | MQTT, 5G/Wi-Fi hybrid, Kafka messaging | Less robust, higher latency |
| Latency (Sensor to AI) | <5s end-to-end | >45s (average) |
| Reliability | 99.97% with 4G backup | Prone to disruptions |
Intelligent Algorithms & Decision Making
The platform's intelligent analysis and decision engine employs state-of-the-art deep learning models for comprehensive data processing. This includes bidirectional LSTM networks for continuous health assessment, 3D-CNN with ResNet-50 for robust behavior recognition, and fuzzy control combined with reinforcement learning for environmental optimization. These models ensure real-time performance and accuracy.
94.3%
Health Status Assessment Accuracy
0.96
Behavior Recognition F1-Score
Case Study: AI-Driven Fall Detection Success
The platform's behavior analysis module, utilizing an advanced improved spatio-temporal convolutional network architecture and skeletal keypoint detection, achieved a 97.2% sensitivity in detecting falls. This led to immediate alerts via WebSocket channels, significantly reducing emergency response times to 3.8 seconds, a drastic improvement over traditional systems. This rapid intervention capability has demonstrably enhanced elderly safety and caregiver peace of mind.
System Performance & Robustness
Comprehensive performance testing across 200 elderly participants in Wuhan City demonstrated the platform's superior effectiveness and reliability. Key indicators such as physiological data accuracy, emergency response time, and user satisfaction showed statistically significant improvements. The system also proved robust across diverse scenarios and environmental conditions, maintaining high performance.
| Indicator | Expected Target | Actual Result | Status |
|---|---|---|---|
| Physiological Data Delay | <3s | 2.3s | Achieved |
| Abnormal Behavior Recognition Rate | ≥95% | 97.2% | Achieved |
| System Response Time | <5s | 3.8s | Achieved |
| Data Transmission Success Rate | ≥99.9% | 99.95% | Achieved |
| Concurrent Processing Capability | ≥1000 times/s | 1280 times/s | Achieved |
| Service Punctuality Rate | ≥98% | 98.7% | Achieved |
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Total Citations
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Total Downloads
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Avg. User Satisfaction
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System Uptime
Case Study: Robustness Across Challenging Scenarios
The platform underwent rigorous scenario-specific validation, proving its adaptability. In living-alone elderly contexts, it achieved a 98.8% fall detection accuracy. For chronic disease patients, health deterioration early warning accuracy reached 96.8%, leading to a 38% hospital readmission rate reduction. Even under varying environmental conditions like dim lighting or complete darkness, fall detection accuracy remained above 95.1% through multimodal sensor fusion. This robust performance ensures reliable care regardless of external factors.
Calculate Your Potential AI Impact
Estimate the potential savings and efficiency gains for your organization by leveraging our AI solutions, inspired by the principles outlined in this research.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrate intelligent platforms, leveraging insights from cutting-edge research to ensure sustainable success and measurable impact.
Phase 1: Discovery & Strategy
Conduct a comprehensive assessment of current elderly care service workflows, identify key pain points, and define strategic objectives. Develop a tailored AI integration roadmap, including technology stack selection, data governance strategy, and initial pilot scope.
Phase 2: Platform Development & Integration
Implement the intelligent platform, focusing on modular development of perception, network, platform, and application layers. Integrate IoT sensors, establish secure data transmission protocols, and deploy core AI models for health assessment and behavior analysis. Conduct rigorous unit and integration testing.
Phase 3: Pilot Deployment & Optimization
Launch the platform in a controlled pilot environment with a select group of elderly participants and caregivers. Collect performance data, gather user feedback, and iteratively refine AI models and system functionalities. Optimize for real-time response, accuracy, and resource efficiency based on pilot outcomes.
Phase 4: Scaling & Continuous Improvement
Expand platform deployment across multiple residential environments, ensuring scalability and robustness. Establish continuous monitoring, maintenance, and update protocols. Implement advanced personalization features and explore new AI applications based on evolving research and user needs to sustain long-term value.
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