01On-device ML
MoodScope
Health Hacks @ USF 2025 · 2025
Real-time facial-emotion recognition, quantized for the edge
~33 FPSon-device on a Raspberry Pi 4
Reads emotion from a face in real time on a Raspberry Pi 4, using INT8 quantization to run entirely on the edge so nothing ever leaves the device.
An on-device facial-emotion detector built for edge hardware: INT8-quantized TensorFlow Lite models hit ~30 ms per frame (~33 FPS) on a Raspberry Pi 4, and threaded dual detection backends cut frame stalls by ~45%. A local Llama 3.2 turns the readings into plain-language insight without any data leaving the device. Built at Health Hacks @ USF 2025.
Stack
- TensorFlow Lite (INT8)
- Llama 3.2
- OpenCV
- Raspberry Pi 4
- Python
01
How it's built
SPECIFICATION
- Device
- Raspberry Pi 4 (edge)
- Models
- TensorFlow Lite, INT8 quantized
- Throughput
- ~30 ms/frame (~33 FPS)
- Latency
- ~45% fewer frame stalls (threaded dual backends)
- Insight
- local Llama 3.2, on-device
- Vision
- OpenCV
MY ROLE
- 01Quantized the detection models to INT8 TFLite for real-time inference on a Pi 4.
- 02Built threaded dual detection backends, cutting frame stalls ~45%.
- 03Integrated a local Llama 3.2 for private, on-device insight.