This topic is focused to the MicroSoft Kinect sensor in its version for Windows and the development of applications using the Kinect SDK, especially the treatment of body movement and postures and gestures recognition.

The Microsoft Kinect sensor is a very powerful device that provides image capturing, distance measurement and body postures and facial expressions recognition services, which makes it suitable for infinity of applications. In this introductory article I will show how to use it to capture different types of images.

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In previous articles I presented a home video surveillance system, the ThiefWatcher application. It is an extensible application that works combining several protocols, such as cameras, triggers, communication channels and storage systems. In this article I will show how to implement some of these protocols using the Microsoft Kinect sensor.

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One of the most interesting features offered by the Microsoft Kinect sensor is human bodies detection, which allows us to develop applications based on the different positions of the user's body and that can be handled remotely using those positions. To do this, it provides a series of points that represent the different joints of the body.

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In this second article in the series about recognition of postures using the Microsoft Kinect sensor I will show generic classes and structs used to isolate the application from the different versions of the SDK. The version of the SDK that we must use depends on the sensor model that we have. In the example code I have used version 2.0, for the Xbox One sensor.

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In the previous article of this series I showed the structures, enumerations and classes that the application uses to be independent of the sensor version and the Kinect SDK. In this third article I will show a possible implementation of a class that deals with reading and converting the skeletons using version 2.0 of the SDK, for the Xbox One sensor.

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We continue with the series about posture recognition with the Microsoft Kinect sensor. This time I will comment the classes responsible of posture normalization of the body in the way commented in the first article, so that we obtain a series of data that constitute a simplified version of the skeleton most appropriate for analysis using pattern recognition techniques.

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This article closes the series on posture recognition using the Microsoft Kinect sensor. To finish, I will simply show you the implementation of a basic form that uses the class libraries explained in the previous articles.

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