This paper suggests using a wideband sonar system to detect and classify human activity in indoor environment. The high bandwidth enables precise tracking of body parts, and its enhanced correlation properties can be used to distinguish between human and nonhuman objects. Maximal Likelihood (ML) criterions to derive kinematic features and processing methods to estimate the subject activity level and activity type were derived and tailored to the wideband sonar. For tracking and association of the echoes reflected from the different body part, an efficient approximation of the sequential ML estimator was derived in the natural time-space domain, which eases the exploitation of the a priori knowledge about the human subject target. For classification of the activity, a weighted two level nested k-nearest neighbor classifier was applied on only four kinematic features. A set of experiments with five subjects, performing three different activity types of standing, walking, and swinging upper limbs, was carried out in a typical indoor environment. The proposed technology has managed to classify well the different activity types and demonstrated the potential of this technology for continuous assessment of various kinematic features of humans in indoor environment with reduced costs, under any light, smoke, or humidity conditions. This can be useful for instance for monitoring patients at home, and for detecting intruders.

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