The paper presents an active vision system for human posture recognition, which is an important function of any assisted living system, suitable to be employed in indoor environments. Both hardware and software architectures are defined in order to meet constraints typically imposed by AAL (Ambient Assisted Living) contexts such as compactness, low-power consumption, installation simplicity, privacy preserving and non-intrusiveness. Two different approaches for feature extraction (topological and volumetric) are discussed and the related discrimination capabilities evaluated by using a statistical learning methodology. Experimental results show the soundness of the presented active vision-based solution in order to classify four main human postures (standing, sitting, bent, lying) with an adequate detail level for the specific AAL application.
28 Jun 2011
2011 4th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)