SMASH Lab Uses Wearables To Train New Privacy-Preserving Sensors

Data about home sales likely won't help someone looking for a car, just like information about basketball won't help someone playing baseball. But that's not true inside the Smart Sensing for Humans (SMASH) Lab at Carnegie Mellon University, where researchers use data collected from one type of sensor to train another. Their work, called IMU2Doppler, has shown that data collected from inertial measurement unit (IMU) sensors in smartwatches can quickly train a millimeter wave doppler to recognize human movements and behaviors. Doppler sensors use millimeter waves and the doppler effect to determine the velocity and direction of a moving object. The ambient sensor can be installed in a house, where it can recognize and track daily activities such as eating, drinking, brushing teeth and folding clothes. It is a privacy-preserving alternative to popular smart devices with speakers and cameras. While large, labeled data sets exist for training sensors that depend on IMU or video, they do not exist for doppler sensors.