An electrical impedance tomography toolkit lets users design and fabricate health and motion sensing devices.
Previous definitions of “well-being,” limited to taking a brisk walk and eating a few more vegetables, feel in many ways like a distant past. Shiny watches and sleek rings now measure how we eat, sleep, and breathe, calling on a combination of motion sensors and microprocessors to crunch bytes and bits.
Even with today's variety of smart jewelry, clothing, and temporary tattoos that feel equal parts complex and manageable, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital’s (MGH) Center for Artificial Intelligence (CPAI) wanted to make things a little more personal. They created a toolkit for designing health- and motion-sensing devices using something called “electrical impedance tomography (EIT),” a fancy word for an imaging technique that measures and visualizes a person’s internal conductivity. (EIT is typically used for things like observing lung function or detecting cancer.)