Birdhouse: Data-driven Senior
Care Monitoring
Led the data architecture and analytics for Birdhouse, a winning solution at Build For Good Hackathon that addresses unnoticed senior deaths in Singapore through IoT-based monitoring.
Task
Designed and implemented an end-to-end data infrastructure for real-time senior monitoring, integrating IoT sensor data with predictive analytics.
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Role
Data Scientist
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Organization
Build For Good Hackathon
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Date
2024
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Tools
PostgreSQL, Tableau, Python
CHALLENGE
The Growing Crisis
Singapore faces a critical challenge with its aging population. By 2030, 1 in 4 citizens will be aged 65+, with a projected 83,000 seniors living alone - a nearly 6x increase from 2000. In 2023 alone, 37 cases of unnoticed senior deaths were reported, highlighting an urgent need for better monitoring solutions.
The current ecosystem, including Active Aging Centres (AACs) and volunteer programs, struggles with limited resources and fragmented communication systems.
SOLUTION
Technical Implementation
Birdhouse is an innovative IoT-based monitoring system that uses motion sensors to track senior activity patterns and alert caregivers of potential emergencies. As the Data Scientist on the team, I led the development of a comprehensive data infrastructure that processes real-time sensor data and transforms it into actionable insights for caregivers.
The heart of our solution lies in its data architecture. I designed a PostgreSQL database optimized for time-series IoT data, implementing efficient data partitioning for historical pattern analysis. Using Python, I developed robust ETL pipelines that handle sensor data ingestion, cleaning, and validation.
- Designed and implemented PostgreSQL database architecture
- Created interactive Tableau dashboards
- Developed data pipeline connecting IoT sensors
- Conducted user research for data-driven personas
- Led user testing sessions for optimization
IMPACT
Results & User Feedback
Understanding our users was crucial for success. I conducted extensive interviews with 8 Active Aging Centre staff members to understand their workflow challenges. These insights informed the creation of data-driven personas and guided our dashboard design.
Through three rounds of usability testing, we iteratively refined the interface to ensure it seamlessly integrated with staff operations while providing critical monitoring capabilities. The system successfully monitored 4 seniors during pilot phase, collecting over 1,500 check-ins across 11 days. Active Aging Centre staff reported significant workflow improvements and are planning to expand to 50 more seniors.
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