Wearable System Recognizes Human Activities in Real Time
This project presents an end-to-end smart wearable system that recognizes and classifies human physical activities in real time. It combines an RT-Thread RT-Spark (STM32) device, Mango Cloud backend, a lightweight temporal machine-learning model for live inference, and a Progressive Web App for visualization and diagnostics. The design emphasizes low-latency streaming, timestamped logging, and UI transparency through motion-intensity and window-accumulation metrics.
Scoring Rationale
Practical, reproducible wearable architecture with real-time ML and transparent UI + limited novelty and single-source project description
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