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Hands play an important role in human life. We use our hands to do day-to-day tasks as well as to work. Unfortunately, there are those who lose their hands or arms due to accidents or diseases. It is estimated that at least 1.5 million people are living with an absence of upper limbs. The progress of Prosthetic arms and human-like robot hands research and development have been improved incessantly. There are many approaches to realize a prosthetic hand. The most recent approaches have been using electromyographic (EMG) signals as a source to control the prosthetic hands. With the help of machine learning, the numbers of controls and movements have been increasing compared to only close and open movements in the past.
With EMG, biological data taken from the human body, as the input source, Spiking Neural Network (SNN) has become a favorable solution. When a movement command is sent from the brain to the nerve of the arm, muscle contraction will occur. Using that very moment of EMG signal, the prosthetic arm will move according to the signal inputs.
To develop a neuroprosthetic hand control based on Neural Network.
| Date | Task |
| ☑️May 1~31 2021 | Survey the available 3D printed hand models and robot/prosthetic hand research |
| ☑️June 1~30, 2021 | Study about the control mechanism of a hand model |
| ☑️July 1~31, 2021 | Survey about EMG classification(learn TCN, RNN, and SNN) and print the 3D hand |
| ☑️August 1~31, 2021 | sEMG feature extractions |
| September 1 ~ October 31, 2021 | Implementation with TCN and CNN-RNN or RNN |
| November 1, 2021 ~ Febuary 28 2022 | Implementation with SNN |
