Motion sensors for activity recognition in an ambient-intelligence scenario

In recent years, Ambient Intelligence (AmI) has attracted a number of researchers due to the widespread diffusion of unobtrusive sensing devices. The availability of such a great amount of acquired data has driven the interest of the scientific community in producing novel methods for combining raw measurements in order to understand what is happening in the monitored scenario. Moreover, due the primary role of the end user, an additional requirement of any AmI system is to maintain a high level of pervasiveness. In this paper we propose a method for recognizing human activities by means of a time of flight (ToF) depth and RGB camera device, namely Microsoft Kinect. The proposed approach is based on the estimation of some relevant joints of the human body by using Kinect depth information. The most significative configurations of joints positions are combined by a clustering approach and classified by means of a multi-class Support Vector Machine. Then, Hidden Markov Models (HMMs) are applied to model each activity as a sequence of known postures. The proposed solution has been tested on a public dataset while considering four different configurations corresponding to some state-of-the-art approaches and results are very promising. Moreover, in order to maintain a high level of pervasiveness, we implemented a real prototype by connecting Kinect sensor to a miniature computer capable of real-time processing.