| Project Domains | Mentors | Project Difficulty |
|---|---|---|
| PCB Designing, Embedded Systems, Electronics, Machine Learning | Atharva Atre, Purva Yeshi, Aryan Nanda | Hard |
Project Description
Creating an EMG Armband is a multifaceted endeavor, engaging mentees in the intricacies of electronics engineering and IoT technology. At its core lies the meticulous crafting of a sophisticated Printed Circuit Board (PCB), carefully housing EMG (Electromyography) sensors, and seamlessly integrating them with the potent ESP-32 microcontroller.
This fusion of hardware and software prowess facilitates the real-time detection of intricate hand movements and orientation, achieved by capturing muscle impulses directly from the forearm. The EMG armband, as the conduit for these signals, plays a pivotal role in the process.
Once captured, these signals undergo swift and efficient processing within the ESP-32. Leveraging its computational capabilities, the microcontroller executes a series of basic machine learning algorithms. These algorithms, grounded in the principles of neural networks, meticulously analyze the incoming signals. Their task is twofold: to discern patterns indicative of various hand gestures and to accurately classify these gestures in real-time.
References
- EMG Signals and its Processing Video Explanation
- Neural Networks Overview | Video Explaination