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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 ~ 20, 2021 | Implementing signal processing part in Matlab |
| ☑Jan 20 ~ Feb 28, 2021 | Add source code and dataset into github |
| ☑Jan 17 ~ 31, 2021 | Help to revise ETLTC20210 papers |
| ☑Feb 20 ~ 26, 2021 | Test 3D printer |
| ☑Mar 2 ~ Mar 5, 2021 | Study AIRBiS project and write slide |
| ☑Mar 2 ~ Mar 12, 2021 | Inference of batch test on FPGA and communicate network with UI for 64x64 grayscale |
| Mar 12 ~ Mar 22, 2021 | Inference of batch test on FPGA and communicate network with UI for 128x128 grayscale |
| Mar 23 ~ Mar 22, 2021 | Inference of batch test on FPGA and communicate network with UI for 64x64 RGB |
| Mar 23 ~ Apr 3, 2021 | Inference of batch test on FPGA and communicate network with UI for 128x128 RGB |
