0000000000992062
AUTHOR
Gabriele Maida
showing 3 related works from this author
User Activity Recognition via Kinect in an Ambient Intelligence Scenario
2014
The availability of an ever-increasing kind of cheap, unobtrusive, sensing devices has stressed the need for new approaches to merge raw measurements in order to realize what is happening in the monitored environment. Ambient Intelligence (AmI) techniques exploit information about the environment state to adapt the environment itself to the users’ preferences. Even if traditional sensors allow a rough understanding of the users’ preferences, ad-hoc sensors are required to obtain a deeper comprehension of users’ habits and activities. In this paper we propose a framework to recognize users’ activities via a depth and RGB camera device, namely the Microsoft Kinect. The proposed approach takes…
Motion sensors for activity recognition in an ambient-intelligence scenario
2013
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…
Gait Analysis Using Multiple Kinect Sensors
2014
A gait analysis technique to model user presences in an office scenario is presented in this chapter. In contrast with other approaches, we use unobtrusive sensors, i.e., an array of Kinect devices, to detect some features of interest. In particular, the position and the spatio-temporal evolution of some skeletal joints are used to define a set of gait features, which can be either static (e.g., person height) or dynamic (e.g., gait cycle duration). Data captured by multiple Kinects is merged to detect dynamic features in a longer walk sequence. The approach proposed here was been evaluated by using three classifiers (SVM, KNN, Naive Bayes) on different feature subsets.