The process of cleaning motion capture data of aberrant points has been described as "the bane of motion capture operators". Yet, managing the high volume kinematic data generated through in-home neurogames requires data quality control that, executed insufficiently, jeopardizes accuracy of outcomes. To begin to address this issue at the intersection of biomechanics and "big data", we performed a secondary analysis of a neurogame, evaluating gesture count as well as shoulder and elbow joint angle outcomes calculated from kinematic data in which valid gestures were identified through 3 methods: visual review of regions of interest by an expert (BP); manufacturer-recommended data smoothing (MS); and automated methods (AI). We hypothesized that upper extremity kinematic outcomes from BP would be matched by AI but not MS methods. From one person with post-stroke hemiparesis, upper-extremity kinematic data were collected for 6 days over 2 weeks using a Microsoft Kinect™-based neurogame. We calculated gesture count, shoulder angle, and elbow angle outcomes from data managed using BP, MS, and AI methods. BP identified 1929 valid gestures total over 6 days which was different than the other two methods (p = 0.0015). In contrast, the AI algorithm with best precision identified 4372 and MS identified 4459 valid gestures. Furthermore, angle outcomes calculated from AI and MS methods resulted in different values than BP (p < 0.001 for 5 of 6 variables). More research is needed to automate treatment of high volume, low quality motion data to support investigation of motion associated with in-home rehabilitation neurogames.
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