Using Data From the Microsoft Kinect 2 to Quantify Upper Limb Behavior: A Feasibility Study
| Autor: | Alexandre Barachant, Victor He, David Harary, Silverio Joseph Bumanlag, John D. Long, David Putrino, K. Zoe Tsagaris, Behdad Dehbandi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male medicine.medical_specialty Support Vector Machine Future studies Computer science Video Recording 02 engineering and technology Motor behavior Motor Activity computer.software_genre Models Biological Motor function Upper Extremity Young Adult 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Health Information Management Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering medicine Humans Electrical and Electronic Engineering Stroke survivor Data collection Healthy subjects Reproducibility of Results Biomechanical Phenomena Computer Science Applications Support vector machine medicine.anatomical_structure Feasibility Studies Upper limb Female 020201 artificial intelligence & image processing Data mining computer Algorithms 030217 neurology & neurosurgery Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 21:1386-1392 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2016.2606240 |
| Popis: | The objective of this study was to assess whether the novel application of a machine learning approach to data collected from the Microsoft Kinect 2 (MK2) could be used to classify differing levels of upper limb impairment. Twenty-four healthy subjects completed items of the Wolf Motor Function Test (WMFT), which is a clinically validated metric of upper limb function for stroke survivors. Subjects completed the WMFT three times: 1) as a healthy individual; 2) emulating mild impairment; and 3) emulating moderate impairment. A MK2 was positioned in front of participants, and collected kinematic data as they completed the WMFT. A classification framework, based on Riemannian geometry and the use of covariance matrices as feature representation of the MK2 data, was developed for these data, and its ability to successfully classify subjects as either "healthy," "mildly impaired," or "moderately impaired" was assessed. Mean accuracy for our classifier was 91.7%, with a specific accuracy breakdown of 100%, 83.3%, and 91.7% for the "healthy," "mildly impaired," and "moderately impaired" conditions, respectively. We conclude that data from the MK2 is of sufficient quality to perform objective motor behavior classification in individuals with upper limb impairment. The data collection and analysis framework that we have developed has the potential to disrupt the field of clinical assessment. Future studies will focus on validating this protocol on large populations of individuals with actual upper limb impairments in order to create a toolkit that is clinically validated and available to the clinical community. |
| Databáze: | OpenAIRE |
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