Tomography analyzes the inner structure and composition of objects by examining them with excitations such as electricity and radiation in a cross-sectional manner. Electrical Impedance Tomography (EIT), proposed in 1978, uses pair-wise impedance measurements from surface electrodes surrounding an object to recover the impedance distribution of the inner structure. Like other tomographic methods – such as CT scans (x-rays), PET scans (gamma rays) and magnetic resonance imaging – medical EIT devices tend to be large and expensive, precluding integration into consumer electronics.

In this research, we describe our efforts to create a small, low-powered and low-cost EIT sensor, one that could be integrated into consumer worn devices, such as smartwatches. Achieving these design properties comes at the cost of reduced precision and resolution compared to medical EIT systems. However, as our work shows, our system is still able to resolve considerable detail. This ability to non-invasively look inside a user’s body opens many new and interesting application possibilities. For example, muscles change their cross-sectional shape and impedance distribution when flexed. Therefore, as a proof-of-concept application domain, we use our EIT sensor for hand gesture recognition. We call this system Tomo – a sensing armband that can be worn on the wrist or arm.

Research Team: Yang Zhang, Chris Harrison

Awards: Best Talk Nominee