Abstract
Body tracking sensors have become an integral part of the physical therapy not only as a motivational tool used by games but also as a diagnostic instrument. Skeletal tracking helps to analyze and quantify human motion and thus can provide tangible results from therapy sessions. Markerless skeletal tracking with depth sensing cameras represents currently the most popular approach mainly due to low cost cameras that use a model based approach to recognize a human skeleton. The model based approach works in many scenarios but faces limitations as well. This paper presents a method for identifying the patient and detecting interactions between the patient and the therapist. Identifying interactions helps to discriminate between active and passive motion of the patient as well as to estimate the accuracy of the skeletal data. Our experiments show the state-of-the-art performance of real-time face recognition from a low resolution images that is sufficient to use in adaptive systems. We also compare the performance of our interaction detection method with two other approaches (markerless and marker based approach) and shows its superior performance.
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Notes
- 1.
Kinect Quick Setup Guide & Kinect Sensor Manual.
- 2.
An example of the underlying framework is the Kinect for Windows SDK,
More information: http://www.microsoft.com/en-us/kinectforwindows.
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Acknowledgments
Research described in the paper was done within the RehabGoesHome and ICT4REHAB projects (www.ict4rehab.org) funded by Innoviris and within the grant No. 1/0529/13 of the Slovak Grant Agency VEGA.
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Omelina, L., Jansen, B., Bonnechère, B., Oravec, M., Van Sint Jan, S. (2015). Detection of Interaction with Depth Sensing and Body Tracking Cameras in Physical Rehabilitation. In: Fardoun, H., R. Penichet, V., Alghazzawi, D. (eds) ICTs for Improving Patients Rehabilitation Research Techniques. REHAB 2014. Communications in Computer and Information Science, vol 515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48645-0_26
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