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.

本数据集包含电阻抗断层成像（Electrical Impedance Tomography，EIT）测量数据。本次数据采集采用搭载16个电极的EIT系统，该系统配置为输出50 kHz频率、1 mA的交变电流作为激励信号。共招募6名受试者，记录了12种手势动作，覆盖该数据集的多维度考察目标。有关数据结构与已记录手势的详细说明，请参阅归档文件中附带的`README.md`文档。基于TensorFlow实现的数据提取与分类器训练示例可访问链接[https://github.com/DavidPL1/eit_data_analysis](https://github.com/DavidPL1/eit_data_analysis)。本数据集采集自德国研究协会（Deutsche Forschungsgemeinschaft，DFG）资助的DEEP-HAND项目：面向上肢肌控的深度感知与深度学习研究，项目编号为272314643。