- Problem Statement ID: SIH1580
- Title: Wearable Sensor with Artificial Intelligence for Fall Prevention in Elderly People
- Theme: MedTech / BioTech / HealthTech
- Category: Hardware
- Team ID: 18731
- Team Name: Tilchattaas
Eldicare is a wearable, AI-powered system designed to prevent falls among the elderly. This device uses advanced sensors, including a gyroscope, accelerometer, SPO2, and heart rate sensors, to provide real-time monitoring and alert caregivers during emergencies.
- Fall Prediction: Detects and predicts falls, immediately alerting caregivers.
- Vital Monitoring: Tracks heart rate and oxygen saturation (SPO2) in real-time.
- Emergency Button: Allows the user to send an alert manually if needed.
- Smart Home Integration: Connects with emergency alarms and smart home systems.
flowchart TD
Start[Wearable Device Startup]:::start
Start --> Init[Initialize Sensors]:::process
Init --> Check{Sensor Status Check}:::decision
Check -- "Normal" --> Monitor[Monitor User's Vital Signs]:::monitor
Check -- "Abnormal" --> Alert[Trigger Alert System]:::alert
Monitor --> Movement{Movement Detected?}:::decision
Movement -- "Yes" --> Analyze[Analyze Movement Pattern]:::process
Movement -- "No" --> Monitor
Analyze -- "Fall Detected" --> Alert
Analyze -- "Normal Movement" --> Monitor
Alert --> SendAlert[Send Alert to Caregiver]:::alert
SendAlert --> Location[Share Real-Time Data]:::output
Alert --> UserResponse{User Response?}:::decision
UserResponse -- "Emergency Button Pressed" --> SendAlert
UserResponse -- "No Response" --> Escalate[Escalate to Emergency Services]:::alert
Escalate --> Notify[Notify Family/Care Facility]:::output
Monitor -- "Healthy" --> Loop[Continue Monitoring]:::monitor
Loop --> Check
%% Color classes suitable for both themes
classDef start fill:#2E86C1,color:#FFFFFF,stroke:#333,stroke-width:2px; %% Dark blue for start
classDef process fill:#58D68D,color:#000000,stroke:#333,stroke-width:1px; %% Green for processes
classDef decision fill:#F4D03F,color:#000000,stroke:#333,stroke-width:1px; %% Yellow for decisions
classDef monitor fill:#AF7AC5,color:#FFFFFF,stroke:#333,stroke-width:1px; %% Purple for monitoring
classDef alert fill:#E74C3C,color:#FFFFFF,stroke:#333,stroke-width:1px; %% Red for alerts
classDef output fill:#5DADE2,color:#000000,stroke:#333,stroke-width:1px; %% Light blue for outputs
- Gyroscope & Accelerometer: Measures body orientation (X, Y, Z axis) and detects rapid movements or instability.
- Auto-Reboot: Prevents false readings by resetting the device periodically.
- GSM Module: Ensures connectivity and emergency alerts, even without a smartphone.
- Data Reliability: Custom dataset development for accurate fall detection.
- False Readings: Mitigated by periodic auto-reboots.
- Communication: GSM integration ensures connectivity without requiring a smartphone.
Screen.Recording.2024-09-30.at.11.21.14.PM.mp4
The Android app complements the wearable device by displaying real-time data, providing alerts to caregivers, and tracking the user's health status. Key app features include:
- Real-Time Monitoring: Displays current vital signs and movement data.
- Alert Notifications: Caregivers receive instant alerts in case of a fall or emergency.
- Emergency Contact Integration: Allows quick access to emergency contacts.
- History Tracking: Logs previous alerts and health data for review.
- Increased Safety: Reduces fall risk, increasing confidence in daily activities.
- Lower Healthcare Costs: Minimizes fall-related hospital visits.
- Caregiver Support: Automated alerts reduce monitoring strain on caregivers.
- Enhanced Independence: Enables elderly individuals to live independently.
- Health Monitoring: Early detection of potential health issues.
- Affordable and Scalable: Suitable for homes and care facilities.
- GitHub Repository: SIH2024-Tilchattaas
- Relevant Research:
- HealthResearch on Fall Risks
- NIH Study on Fall Prevention
- Google Books: Promoting Health and Wellness in the Geriatric Patient by David A Soto