MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has created a toolkit for designing health- and motion-sensing devices.
Using an imaging technique called electrical impedance tomography (EIT) that measures and visualizes a person’s internal conductivity, MIT built a range of devices that support different sensing applications such as a personal muscle monitor for physical rehabilitation, a wearable hand gesture recognizer and a bracelet that can detect distracted driving.
EIT sensing requires hardware setups and complicated image reconstruction algorithms, the use of printed electronics and open-source EIT image libraries. But designing EIT items is still difficult and requires a proper fusion of design knowledge, adequate contact between the device and the human as well as optimization.
The EIT kit allows designers to create devices in an editor that can be exported to a 3D printer. The item can then be assembled, placed onto the target measuring area and connected to EIT Kit’s sensing motherboard. Finally, an on-board microcontroller library automates the electrical impedance measurement and lets the users see visual measured data, even on a smartphone.
One device prototyped by the team appears as two simple bands that sense muscle strain and tension in the thigh to monitor muscle recovery post-injury and may even prevent reinjury.
The monitor uses two electrode arrays to create a 3D image of the thigh as well as augmented reality to view the muscle activity in real-time. Just sensing motion would be useless but a person doing a rehabilitation exercise correctly requires using the correct muscle.
"The EIT-kit project fits my long-term vision of creating personal health-sensing devices with rapid function prototyping techniques and novel sensing technologies,” said Junyi Zhu, an MIT CSAIL student and lead in the project. "During our study alongside medical professionals, we discovered that EIT sensing is largely patient- and sensing-location dependent, because of measuring configurations, signal calibration, electrode placements and other bioelectrical-related factors. These challenges can be resolved with customizable hardware and automation algorithms. Beyond EIT, other health sensing technologies face similar complexities and personalized needs.”
Researchers are creating remote rehabilitation devices to monitor different parts of a patient’s body while healing. And since all the EIT kit devices are mobile and customizable, they can be easily used at home to give doctors a more holistic picture of the healing process.