Flat foot is a massive health problem within our society. Treating it takes great measures, and often the methods are way too complicated, needing surgical intervention, or just complicated. Treating kids is another challenge which doctors must overcome, and they must invent modern solutions for them.
Treating flat feet with Games. A Controlled, and measurable method, using a simple device as the bridge between the physical foot exercises and the games.
This repository shows a simple and quick method of classifying EMG data coming from Myo Armband using neural networks. The classified data represents user input from the keyboard, which can control a simple game character movement. The input keys can be selected as user wishes.
The EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. The nervous system always controls the muscle activity (contraction/relaxation). Hence, the EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles. Different methods have been used by researchers to extract the EMG signals, from painful needle insertion techniques to attaching multiple surface EMG sensors on hand. In this project, a surface EMG based armband is used called "Myo Gesture Control Armband". It was designed by a Canadian company "Thalmic Labs". This armband has 8 sensors that measure EMG signals at 200 Hz frequency.
Special thanks to Niklas Rosenstein for his Myo-Python API (https://github.com/NiklasRosenstein/Myo-python)
And to Shayan Ali Bhatti for the inspiration for the gesture recognition algorithm using ANN. Check out his work at https://github.com/shayanalibhatti/Finger-Movement-Classification-via-Machine-Learning-using-EMG-Armband-for-3D-Printed-Robotic-Hand/