MIT has released a new toolkit that enables users to design motion-sensing devices with the help of electrical impedance tomography. For the toolkit, the researchers worked closely with scientists from Massachusetts General Hospital Center for Artificial Intelligence.

The toolkit uses electrical impedance tomography (EIT), which is an imaging technique that can measure and visualize the user’s internal conductivity. The toolkit is named EIT-kit, and with the help of the kit, the team developed devices that support multiple sensing applications.

Building wearable devices at cheaper prices

Devices designed during testing included muscle monitors for physical rehabilitation, a device that can recognize hand gestures, and a wearable device to detect distracted driving. Usually, EIT devices make use of hefty hardware and complex algorithms to reconstructing images.

In the system developed by MIT, printed electronics and open-source EIT algorithms enabling users to build low-cost and portable devices. One of the complications of designing this type is knowing how to integrate contact between the device and the wearer.

However, the EIT-kit 3D editor allows the user to design the direction letting them place sensor electrodes in the editor and export the design to a 3D printer. Once the command is sent to the printer, the product is assembled in the target measuring area and connected to the kit’s motherboard.

Showing muscle activity in real-time

The 3D editor packs an integrated microcontroller library to automate electrical impedance measurement. The automation enables users to see visualized measured data on a smartphone. The biggest takeaway of the EIT-kit is that it is capable of sensing muscle activity while existing devices can only sense motion.

The researchers devised a prototype that packs two bands able to sense muscle strain and tension in the thigh. That monitoring device features a pair of electrode arrays and created a 3D image of the thigh and an AR view of muscle activity in real-time.