Deep Reinforcement Learning algorithm for Pattern Recognition
Deep Reinforcement Learning algorithm
Node Device
Intelligence
v1.0
February 5, 2025
RELATED ITEMS
Reinforcement learning
Total structure
The electromyography(EMG) signals were measured by Myo Arm band.
Time domain features were extracted by various feature extraion algorithm.
While agent is training, human’s feedback which is SSVEP were used as reward.
Data sets were splited into training, test set, and 5-fold validation were used.
Using SSVEP estimation, human can give the right answer to reinforcement learning agent
Each Motion was mapped into phillips Hue smart light bulb.
Human feedback put into replay memory.
Result
RL agent predicts the human arm motion, and control the light bulb.
If the control was wrong, SSVEP result will be used as a human feedback to train again the agent.
Deep Reinforcement Learning algorithm for Pattern Recognition
Introduction
Conventional methods need the label(ground truth) to train the supervisd learning model. By utilizing the SSVEP(Steady State Visually Evoked Potential), it is possible to get a human feedback as a reward.
By using human’s feedback, human arm motion pattern recognition accuracy will be increased.
Reinforcement learning agent chooses the optimal features for human motion pattern recognition, and classifies the human motion patterns.
The project aims to develop and distribute an open source Reinforcement learning Pattern recognition project and associated developer guide.
It is working base on Myo-python library, Python 3.6, PyTorch .