Introduction

The global aging population presents significant challenges to healthcare systems, particularly in elderly care homes. The demand for caregivers is rising, yet manpower shortages hinder the ability to provide timely and efficient care. SAFERIN is an AI-powered elderly care monitoring system designed to address these critical issues by integrating intelligent safety, staffing, and healthcare solutions.

This proposal outlines how SAFERIN utilizes Artificial Intelligence (AI) to enhance elderly care, reduce response times, and alleviate the workload of caregivers.

The scholarship will provide crucial support in furthering AI research and implementation within SAFERIN, accelerating its impact on the healthcare industry.

Problem Statement

Elderly care homes in Hong Kong and worldwide face three major challenges:

• High Accident Rates: Falls and delayed responses contribute to severe injuries and high medical costs (HKD 100,000 per case on average).
• Manpower Shortages: With a 20% vacancy rate and a caregiver-to-elder ratio of 1:20 (compared to the recommended 1:5), timely care is compromised.
• Inefficient Resource Allocation: Traditional nursing call systems fail to prioritize emergency cases effectively, leading to longer wait times and inadequate responses.

Proposed Solution: SAFERIN

SAFERIN is a comprehensive AI-driven elderly care monitoring system that provides three core functionalities:

Smart Safety – AI-Powered Fall and Vital Sign Detection

• Uses mmWave technology and RFID to detect falls, wandering, and irregular heart rates.
• AI algorithms predict potential health risks and send instant alerts to caregivers.

Smart Staffing – AI-Enhanced Nurse-On-Demand System

• AI-powered voice recognition system classifies requests based on urgency.
• Automatically assigns caregivers, reducing wait times by over 50%.

Smart Care – Intelligent Health Predictive & Analytic System

• AI analyzes patient history and real-time data to generate health reports.
• Enables personalized care plans for elderly residents, reducing hospitalization rates.

AI Methodology and Innovation

SAFERIN employs cutting-edge AI technologies, including:

• Natural Language Processing (NLP): Enables elderly residents to communicate requests efficiently, even with varied accents.
• Machine Learning (ML) for Risk Prediction: Identifies high-risk individuals and prioritizes emergency cases.
• Computer Vision and Sensor Fusion: Enhances fall detection accuracy beyond traditional motion sensors.
• Cloud-Based AI Analytics: Provides predictive insights to optimize caregiving workflows.

Expected Outcomes and Impact

Through pilot trials conducted in elderly care homes, SAFERIN has demonstrated:

• 50% Reduction in Wait Times: Faster responses to emergencies, improving survival rates.
• 55% Cost Savings: Lower operational costs for elderly care homes through optimized staffing.
• Enhanced Caregiver Productivity: Handling capacity increased from 14 to 25 requests per hour.
• Improved Elderly Well-being: Increased safety, reducing fall-related injuries.

Implementation Plan and Timeline

Phase 1 (2024-2025): AI Model Enhancement & Pilot Expansion

• Optimize AI accuracy in fall detection and voice recognition.
• Expand pilot testing to additional residential care homes.

Phase 2 (2025-2026): Full-Scale Deployment & International Expansion

• Launch SAFERIN as a SaaS-based platform for elderly care homes and hospitals.
• Explore expansion to other regions facing elderly care challenges.

Phase 3 (2026+): Continuous AI Research & Product Diversification

• Develop AI-driven predictive analytics for chronic disease management.
• Expand applications to smart home and assisted living environments.

Conclusion

SAFERIN embodies the transformative power of AI in healthcare, addressing critical elderly care challenges with intelligent automation and predictive analytics. 

With the support of this scholarship, SAFERIN will continue to drive innovation in AI powered healthcare, improving the quality of life for the elderly while optimizing caregiving resources.