MIT's toolkit lets anyone design their own muscle-sensing wearables

It uses your body's electrical conductivity to sense movement and activities.


MIT has unveiled a new toolkit that lets users design health-sensing devices that can detect how muscles move. The university's Science and and Artificial Intelligence Laboratory (CSAIL) created the kit using something called "electrical impedance tomography" (EIT), that measures internal conductivity to gauge whether muscles are activated or relaxed. The research could allow for wearables that monitor distracted driving, hand gestures or muscle movements for physical rehabilitation.

In a paper, the researchers wrote that EIT sensing usually requires expensive hardware setups and complex algorithms to decipher the data. The advent of 3D printing, inexpensive electronics and open-source EIT image libraries has made it feasible for more users, but designing a wearable setup is still a challenge.

To that end, the "EIT-kit" 3D editor allows users to enter the device parameters and place the sensors on a device that may go on a user's wrist or leg, for instance. It can then be exported to a 3D printer and assembled, and the final step is to calibrate the device using a subject. For that, it's connected to the EIT-kit's sensing mother board, and "an on-board microcontroller library automates the electrical impedance measurement and lets you see the visual measured data, even on a mobile phone," according to CSAIL.

Where most wearables can only sense motion, and EIT device can sense actual muscle activity. The team built one prototype that could sense muscle strain and tension in a subject's thigh, allowing them to monitor muscle recovery after an injury. It also showed other possible uses, like gesture recognition, a distracted driving detector and more.

The team is working with Massachusetts General Hospital on rehabilitation tech using the devices while refining the tech. The eventual aim is to develop "rapid function prototyping techniques and novel sensing technologies," said lead author Junyi Zhu.