In clinical practice, neurologists usually rely on direct visual observation (or through a video) to evaluate motor symptoms following subjective methods of evaluation based on clinical scores. To address these limitations, we present the NeuroKinect, a portable and low-cost 3D video system designed to provide quantitative data on human motion in the context of neurological diseases with movement impairment.
Challenge | Opportunity
Although the analysis of body movement patterns is critical in the assessment of neurological diseases, it is still relying on visual observation. 2D monitoring systems have been considered to replace visual inspections but their limitations are obvious: difficulty of tracking the erratic movements of interest, frequent marker occlusions, and instability of the attached reflectors or sensors.
Other proposed 3D systems and non-camera approaches have limited widespread use due to their high associated costs, heavy maintenance demands (calibrations, reflectors placing), and complex set-up. Neurokinect offers accurate and fast body motion assessment in Parkinson’s disease and epilepsy unlike the generic few systems based on the Microsoft® Kinect®.
- Provides a user-friendly and comfortable system for both patients and clinical professionals;
- Is less expensive than previous approaches based on non-camera or 3D systems;
- Offers a reliable and accurate tool to support human body motion assessment of Parkinson’s disease and Epilepsy patients.
- Clinical continuous monitoring units (i.e. epilepsy units)
- Gait analysis
- Motion Capture for Rehabilitation and Sports Performance
- Posture and balance assessment
Development StageIndustrial prototype (TRL 5-6)
Further Information<p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0145669" target="_blank">NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification</a></p>
Industrial CategoriesArts, entertainment, and recreation, Healthcare, Sports
TagsMedical imaging, e-Health, Computer-aided diagnosis, Software, Parkinson’s Disease, Motion analysis