SafeMotion v1.0 — Initial Release
SafeMotion is an Edge-AI powered Android fall detection system designed for real-time monitoring with energy-efficient adaptive sensor sampling.
Features
Real-time fall detection using on-device TensorFlow Lite inference
Runtime adaptive sensor sampling
Low-power idle mode
High-frequency wake mode during suspicious motion
Accelerometer and gyroscope based activity monitoring
Battery-optimized sensing pipeline
Real-time fall alert notifications
Event logging and monitoring support
Fully offline processing for improved privacy
Technical Highlights
Compact Edge-AI deep learning model optimized for smartphones
Adaptive sampling strategy:
Idle Mode: 5 Hz
Wake Mode: 50 Hz
Lightweight wake detector for suspicious motion detection
Reduced battery consumption compared to constant high-frequency sensing
Android foreground service implementation for continuous monitoring
Built With
Android
TensorFlow Lite
Java/Kotlin
Android Sensor APIs
Research Focus
This application was developed as part of the project:
“Edge-AI Fall Detection with Runtime Adaptive Sampling on Smartphones”
The system focuses on balancing:
Detection accuracy
Real-time responsiveness
Energy efficiency
Notes
Designed and tested for Android smartphones
Best performance when the device is carried consistently
Intended for research and educational purposes
Future Improvements
Emergency contact integration
Cloud synchronization
Personalized activity adaptation
Improved real-world fall generalization