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Welcome to the pygloves wiki! This repo contains the culmination of my undergraduate dissertation. A year was spent on trying to predict finger positions from EMG data using the Myo armband.
Over the last year I have made:
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pyomyo
Pure Python, multithreaded, cross platform driver for the Thalmic Myo, the first to use all three Myo Modes, including the secret one. -
NeuroBreakout
An example showing how to use the Myo for 1D regression. -
MatplotLeap
Higher dimension regression needs good labels, the Leap Motion was one I tried. MatPlotLeap shows how to use the Leap with Python. -
NeuroLeap (Not yet open source)
Models and tools for predicting LeapMotion data from EMG. -
pygloves / NeuroGlove (This repo)
Models and tools for predicting 5 finger curl using EMG and LucidGloves for labelling. -
pygloves-utils
Parts of pygloves I thought would be useful for LucidGloves users.
Using a dataglove:
A. Pallotti, G. Orengo, and G. Saggio, “Measurements comparison of finger joint angles in hand postures between an sEMG armband and a sensory glove,” Biocybernetics and Biomedical Engineering, vol. 41, no. 2, pp. 605–616, Apr. 2021. - Paper link
Using a Leap Motion:
NeuroPose: 3D Hand Pose Tracking using EMG Wearables - Paper link
F. Quivira, T. Koike-Akino, Y. Wang, and D. Erdogmus, “Translating sEMG signals to continuous hand poses using recurrent neural networks,” in 2018 IEEE EMBS International Conference on Biomedical Health Informatics (BHI), Mar. 2018, pp. 166–169. - Paper link
I. Sosin, D. Kudenko, and A. Shpilman, “Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation,” in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Nov. 2018, pp. 1436–1441. - Paper link