Hand Gesture Recognition with Electrical Impedance Tomography (Dataset)

This dataset consists of measurements using Electrical Impedance Tomography (EIT). An EIT system with 16 electrodes configured to a feed in signal of 50 kHz frequency and a 1 mA alternating current was used for the data acquisition. 12 Gestures were recorded from a total of 6 subjects, targeting different aspects of the data. For more information about the data structure and the recorded gestures refer to the `README.md` file included in the archive. An example of data extraction and classifier training with TensorFlow is available at [https://github.com/DavidPL1/eit_data_analysis](https://github.com/DavidPL1/eit_data_analysis). The data were recorded within the German Research Society project DEEP-HAND: deep sensing + deep learning for myocontrol of the upper limb, DFG project number 272314643.