Indoor Human Activity Recognition Using Bumble Bee Radar

Human detection offers many advantages in applications of search and rescue, smart environments, and security. Infrared, acoustic, vibration/seismic and visual sensors have been often used in human detection and recognition systems. Radar offers unique advantages for sensing humans, such as remote operation during virtually all weather conditions, increased range, and better coverage. However, radar systems are typically very expensive and physically large. The Bumble Bee radar, in contrast to most radars, is a low power pulse Doppler radar that is about the size of a business card. Moreover, it is a radar that can be integrated into indoor wireless sensor networks. In this work, the application of Bumble Bee radar to human activity recognition by computing the human micro-Doppler signature is examined. Humans are complex targets that are capable of many motions. Every part of the human causes different reflection and every motion of the human has its unique micro-doppler signatures. The differences in micro-Doppler data of activities such as walking, running, and crawling that is gathered from low-cost, low-power radar is discussed.