A growing number of people are living with conditions that could benefit from physical rehabilitation -- but there aren't enough physical therapists (PTs) to go around. The growing need for PTs is racing alongside population growth, and aging, as well as higher rates of severe ailments, are contributing to the problem. An upsurge in sensor-based techniques, such as on-body motion sensors, has provided some autonomy and precision for patients who could benefit from robotic systems to supplement human therapists. Still, the minimalist watches and rings that are currently available largely rely on motion data, which lack more holistic data a physical therapist pieces together, including muscle engagement and tension, in addition to movement. This muscle-motion language barrier recently prompted the creation of an unsupervised physical rehabilitation system, MuscleRehab, by researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and Massachusetts General Hospital.

Magically seeing around corners to spot moving people or objects may not rank first in most people's superhero daydreams. But MIT researchers have shown how they could someday bestow that superpower upon anyone with a smartphone. Their secret to peeking around corners is detecting slight differences in light patterns reflected from moving objects or people. Those reflected light patterns form subtle variations in the shadowy area near the base of each corner. MIT's Computer Science and Artificial Intelligence Lab (CSAIL) created simple software that can detect fuzzy pattern variations in the pixels of a 2-D video--taken by a basic consumer camera or even a smartphone camera--and reconstruct the speed and trajectory of moving objects by stitching together multiple, distinct 1-D images.