Electromyography based handwriting recognition system using LM-BP Neural Network

With the development of technology, Human-Computer Interface (HCI) system is playing a more and more important role in our daily life. HCI is a way to set up connections and to transfer information between human and computer. Pattern recognition based on Surface Electromyography (SEMG) is one of the most important HCI technologies. To make the input device of electronic products more portable to satisfy the people' need of interacting with computer (especially disabled people), this research proposes an SEMG-based handwriting recognition system based on LM-BP (Back Propagation) Neural Network. In the aspect of signal preprocessing, this thesis tries to use some new features of signals to reflect the features of signals better. In the aspect of pattern recognition, LM-BP Neural Networking is applied to design a system that is suitable for SEMG-based model training and recognition. Compared with the existing system based on Dynamic Time Warping (DTW) algorithm and the system based on Hidden Markov Model (HMM), the training times and training time have been reduced a lot, which makes the SEMG-based handwriting recognition system more practical.