Hand Gestures Classification using Electrical Impedance Tomography Images

Human-computer communication using hand gestures has always been difficult. More than half a century ago people used different ways of interaction with computers from the early mediums such as perforated game cards. Nowadays, if a richer lexicon of gestures is given, people can communicate more effectively with computers. Machine learning is now used to recognize and classify the hand gestures more precise way. In order to increase the communication between computer and human, we proposed a technique which uses a wearable low-cost device to generate the Electrical Impedance Tomography (EIT) images to recover the inner impedance structure of a user’s wrist. This is done by measuring the transverse impedance between all the sixteen pairs of electrodes of wrist band that lie on the skin of the user hand. The proposed technique is enough to integrate the technology into the prototype wrist band to monitor and classify gestures in real time. We have conducted a study of sixteen gestures with a focus on gross hand and pinches finger gestures. The results evaluation shows that the gross hand gestures achieved 90% accuracy in wrist position, while pinches gestures achieved 93% accuracy.