Embedded Systems Mini Project By Sunjay скачать в хорошем качестве

Embedded Systems Mini Project By Sunjay

📟 Project Spotlight: Human Activity Recognition (HAR) using Embedded AI At Semicore Technologies, we bridge the gap between hardware and intelligence. This project, developed by our Advanced Embedded Systems students, demonstrates how to transform raw sensor data into actionable human insights using Edge Computing. 🏗️ The Technical Framework Core Microcontroller: STM32F4 Series (ARM Cortex-M4) – utilized for its high-performance DSP capabilities and low power consumption. Sensor Fusion: LSM6DSR (6-Axis IMU) – integrated to capture precise 3D Accelerometer and 3D Gyroscope data. Communication: High-speed I2C Protocol for seamless data acquisition between the sensor and MCU. Real-time Interface: 16x2 LCD for instant visual feedback of detected activities. ⚙️ The Workflow: How it Works Signal Acquisition: The system continuously monitors body movement via the IMU sensor. Edge Processing: Raw signals are filtered and processed directly on the STM32 (No cloud required). Pattern Recognition: Using custom-coded mathematical algorithms (Threshold-based or Machine Learning logic), the system identifies patterns for Stationary, Walking, and Running states. Instant Execution: The classified activity is displayed on the LCD within milliseconds, simulating real-world responsiveness. 🌟 Industry Scope & Applications This project is a foundation for the Smart Wearables and Healthcare industries. By mastering this, our students are prepared to work on: Fitness Trackers (Step counting and calorie burn). Elderly Fall Detection systems. Industrial Worker Safety monitoring.