Human micro-doppler classification using a low cost, wireless radar mote for health monitoring

In recent years, radar applications aiming health monitoring have been increasingly investigated. Infrared, acoustic, vibration/sismic, and visual sensors have been used for the recognition of human activities. Unlike these sensors, radars offer unique advantages over ether sensors for recognition and classification of human activities, such as operating virtually all weather conditions, increased range, and better coverage. Human activity monitoring using radars is gaining impartance for detection of emergency situations in home and clinical environments.In this thesis, normal human activities such as walking, running , crawling and elderly and disabled human activities such as walking with a cane, walking with a limp, walking with a walker, and wheelchair will be observed with a radar and differences will be discussed.Effects of aspect angle and dwell time on classification success rate will be presented for these activities.