3D camera aids in multiple sclerosis treatment

Researchers from McGill University are using a Microsoft Kinect3D camera alongside computer vision algorithms to evaluate walking difficulties of multiple sclerosis (MS) patients.

Though the Microsoft Kinect 3D depth sensing camera was developed for gaming in the Xbox console or a Windows computer, a team led by postdoctoral fellow Farnood Gholami (pictured) decided to use it in their research. A standard Kinect device incorporates both a 640 x 480 RGB camera and an infrared emitter that projects an infrared speckle pattern onto a scene. The projected pattern is then captured by an infrared camera in the sensor, and by comparing the returned pattern to reference patterns at known distances from the camera, a depth map of the scene is created.

Gholami, along with Jozsef Kövecses from the Department of Mechanical Engineering and Centre for Intelligent Machines, and Daria Trojan, a physiatrist in the Department of Neurology and Neurosurgery working at the Montreal Neurological Institute and Hospital, conducted tests on whether the Kinect could detect the differences in gait of MS patients compared to healthy individuals.

Current clinical practices, according to McGill University, involving the assessment of MS patients walking movement by doctors, but subjective evaluations may distort results in that two different clinicians may give the same patient different evaluations. By using the Kinect and algorithms developed to quantify the patients’ walking patterns, this reduces the potential for human error.

Using the device, Gholami captured the movement of 10 patients and 10 members of an age-and-sex-matched control group, with the MS patients having previously been assessed for gait abnormalities using the traditional clinician method. Using the data, the team then developed algorithms that quantified gait characteristics of MS patients and healthy people. The team found that gait characteristics measured with the Kinect camera and analyzed with the developed algorithms were reproducible when assessed at one visit and were different between MS patients and the healthy individuals.

Additionally, the gait characteristics of MS patients obtained by the algorithm were correlated with clinical measures of gait, and the algorithms could mathematically define the characteristics of gait in MS patients at different severity levels, accurately determining his/her level of gait abnormality.

Gholami noted in the press release that he became interested in using motion capture technology for clinical purposes as a PhD student, but at the time, the equipment was too expensive and difficult to use and transport. Enter the Kinect, which is an easy-to-use and inexpensive device.

"This tool may help the clinician provide a better diagnosis of gait pathology, and may be used to observe if a prescribed medication has been effective on the gait of the patient or not," he says. "Our developed framework can likely be used for other diseases causing gait abnormalities as well, for instance Parkinson’s disease."

The next step in the process is to conduct a study with a larger group of MS patients, including evaluation in a gait laboratory, using a newer version of the Kinect that features improved accuracy.

View thepress release.

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