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Combining measurements of different sensors are a crucial step to achieve better precision in many applications, such as gesture recognition, etc.
Sensor fusion is an efficient estimation method, which is used in several disciplines.
Using sensor fusion, the information from many sensors and the characteristics of each sensor can be used together to improve the estimate and decrease the error.
(A Kalman filter has been used previously for the fusion of the measurements of the different sensors.)
The goal of this thesis is to Study Sensor-Fusion on EMG Signal Processing and Classification towards the Development of Adaptive Robot-Arm (i.e. artificial/prosthetic hand).
| Date | Task |
| ☑Nov 1 ~ Dec 7, 2020 | Resize MNIST dataset |
| Dec 21 ~ TBD | Update paper "Hand Gesture Processing with Sensor Fusion based on Neuromorphic System" |
| ☑Dec 24 ~ Jan 8, 2021 | EMG dataset collection using Myo |
| Jan 9 ~ 16, 2021 | EMG offline learning using Ageishi's NN on FPGA |
| Jan 17 ~ 31, 2021 | Help to revise ETLTC20210 papers and set up to communicate FPGA and Myo |
| Feb 1~ Mar 1, 2020 | Implementation of EMG classification with traditional neuromorphic processor |
| Mar 2 ~ Apr 1, 2021 | Implement new classification algorithms |
| Apr 2 ~ May 1, 2021 | Evaluate new classification algorithms |
