MIT has unveiled a new toolkit that allows users to design health-sensing devices that can detect muscle movement. The university’s Science and Artificial Intelligence Laboratory (CSAIL) created the kit using something called “electrical impedance tomography” (EIT), which measures internal conductivity to determine if muscles are activated or relaxed. The research could enable wearable devices that monitor distracted driving, hand gestures or muscle movements for physical rehabilitation.
In an article, the researchers wrote that EIT detection typically requires expensive hardware setups and complex algorithms to decipher the data. The advent of 3D printing, cheap electronics, and open source EIT image libraries have made it possible for more users, but designing a portable setup remains a challenge.
To this end, the “EIT-kit” 3D editor allows users to enter device parameters and place the sensors on a device that can go on a user’s wrist or leg, for example. It can then be exported to a 3D printer and assembled, and the last step is to calibrate the device using a subject. For this, it is connected to the detection motherboard of the EIT kit, and “a library of on-board microcontrollers automates the measurement of electrical impedance and allows you to see the measured data visually, even on a mobile phone,” according to CSAIL. .
Where most wearable devices can only detect movement, and the EIT device can detect actual muscle activity. The team built a prototype capable of detecting muscle fatigue and tension in a subject’s thigh, allowing them to monitor muscle recovery after injury. It also showed off other possible uses, like gesture recognition, a distracted driving detector and more.
The team is working with Massachusetts General Hospital on rehab technology using the devices while refining the technology. The end goal is to develop ârapid function prototyping techniques and new sensing technologies,â said lead author Junyi Zhu.
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