Activity Recognition with Edge AI
Classifying human physical activities such as walking, running, sitting, cycling, and climbing stairs from wearable IMU data. Uses time-series classification on sliding windows of accelerometer and gyroscope readings. Core technology for fitness trackers, health monitoring wearables, and rehabilitation devices. Models are typically small CNNs or dense networks trained on labeled activity datasets like UCI HAR.
Hardware Requirements
| Minimum RAM | 64 KB |
| Minimum Flash | 512 KB |
| Sensor Inputs | imu |
| Typical Model Size | 25 KB (quantized int8) |
Hardware Guides
No hardware guides for activity recognition yet. Use the MCU Checker to find compatible hardware.
Industry Applications
Orchestrate Activity Recognition with ForestHub
Your devices run activity recognition on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.