A Survey of Using Microsoft Kinect in Healthcare

A Survey of Using Microsoft Kinect in Healthcare

Roanna Lun (Cleveland State University, USA) and Wenbing Zhao (Department of Electrical and Computer Engineering, Cleveland State University, USA)
DOI: 10.4018/978-1-4666-5888-2.ch322
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Launched in 2010, the Microsoft Kinect device is one of the most popular game controllers in recent years. Kinect allows users to naturally interact with a computer or game console by human body movement or voice command. At a very affordable price ranging from $99 to $200, Kinect is built with a color camera, infrared depth sensor, and a multi-array microphone, as shown in Figure 1. In late 2011, Microsoft released the Software Development Kit (SDK) for Kinect, which enables users to develop sophisticated computer-based human body tracking applications on both C# and C++ programming platforms (Jana 2012). Through the SDK, Kinect provides skeleton tracking of the positions of 20 articulated joints of the human body (Shotton et al., 2013), shown in Figure 2.

Figure 1.

The Kinect sensor

Figure 2.

Kinect color, skeleton, and depth images

The low-cost, and the availability of SDK for the Kinect sensor has attracted many researchers to investigate its applications beyond the video gaming industry, particular in the healthcare realm (for example, Brian et al., 2012). As the aging population rapidly grows in the United States, demands of healthcare services, especially physical therapy and rehabilitation services, have grown enormously in recent years. To meet the increasing demands and reduce the cost of services, providers are often looking for computers and other equipment that can assist them in providing services to patients in an affordable, convenient, and user-friendly environment. As a low-cost, portable, accurate, nonintrusive, and easily set up motion detecting sensor, Kinect enables researchers to develop computer-based vision control without using traditional input devices, e.g. mouse, keyboard, or joystick. This revolutionary technology makes it possible for Kinect to meet the challenge of providing high quality evaluations and interventions at an affordable price for healthcare services, as seen from the works surveyed in this article.


Applications Review

In this section, we review the applications of the Kinect technology in a number of healthcare domains. A summary of the applications is provided in Table 1. The detailed description of the applications is given in individual subsections.

Key Terms in this Chapter

OpenNI: An open source SDK used for the development of 3D sensing middleware libraries and applications for depth sensors such as Kinect.

Logistic Regression (LR): A type of probabilistic classification model used for predicting the outcome of categorical dependent variables based on one or more predictor variables (features).

Fuzzy Logic: A form of many-valued logic. Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. Compared to traditional true or false values, fuzzy logic variables may have a truth value that ranges in degree from 0 to 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.

Randomized Decision Forest (RDF): An ensemble learning method for classification and regression that can operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes output by individual trees.

Kalman Filter: An algorithm that uses a series of measurements observed over time, containing random variations and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone.

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