VR controllers are generally used by hand. But they are not suitable for Handicapped with amputated arm joint. Controllors for them are needed because virtual reality technology can become close to our real life. This study aims to implement it throug...
VR controllers are generally used by hand. But they are not suitable for Handicapped with amputated arm joint. Controllors for them are needed because virtual reality technology can become close to our real life. This study aims to implement it through electromyography signal and Deep learning. Data sets are created through digital filtering and time series feature extraction. Using 1d CNN-LSTM, CNN is used to create a feature map of electromyogram signal and LSTM is used to analyze time series associations. This model takes about 370ms to calculate the predicted data. Loss of prediction through train data is 0.00164, and 0.008458 for test data and 0.008688 for validation data.