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Hand gesture recognition using data from electrical impedance tomography (EIT) systems offers many promising applications, for example in the field of human-computer interaction. Due to its real-time capability and the use of harmless currents for humans, it can be used in medicine, robotics, or virtual environments. As already shown in similar works, for example by Zhang et al. finding a good compromise between accuracy, precision, framerate and the size of the system is a challenge [1], [2]. This work presents a truly wearable compact EIT system on a single 24.9 mm × 22.5 mm circuit board consisting of standard components without application-specific integrated circuit. A neural network (NN) classifies two gestures. It is able to distinguish two different gestures with an accuracy of 78,33 % and a precision of 76,56 %. This work has a strong focus on the size of the system and provides a starting point for further research in compact wearable gesture recognition systems. It shows the challenge of the compromise between size and quality of the signals.

Design of a miniaturized wearable EIT system for imaging and hand gesture recognition