Walking ability is frequently assessed with the 10-meter walking test (10MWT), which may be instrumented with multiple Kinect v2 sensors to complement the typical stopwatch-based time to walk 10 meters with quantitative gait information derived from Kinect’s 3D body point’s time series. The current study aimed to evaluate a multi-Kinect v2 set-up for quantitative gait assessments during the 10MWT against a gold-standard motion-registration system by determining between-systems agreement for body point’s time series, spatiotemporal gait parameters and the time to walk 10 meters. To this end, the 10MWT was conducted at comfortable and maximum walking speed, while 3D full-body kinematics was concurrently recorded with the multi-Kinect v2 set-up and the Optotrak motion-registration system (i.e., the gold standard). Between-systems agreement for body point’s time series was assessed with the intraclass correlation coefficient (ICC). Between-systems agreement was similarly determined for the gait parameters’ walking speed, cadence, step length, stride length, step width, step time, stride time (all obtained for the intermediate 6 meters) and the time to walk 10 meters, complemented by Bland-Altman’s bias and limits of agreement. Body point’s time series agreed well between the motion-registration systems, particularly so for body points in motion. For both comfortable and maximum walking speeds, the between-systems agreement for the time to walk 10 meters and all gait parameters except step width was high (ICC ≥ 0.888), with negligible biases and narrow limits of agreement. Hence, body point’s time series and gait parameters obtained with a multi-Kinect v2 set-up match well with those derived with a gold standard in 3D measurement accuracy. Future studies are recommended to test the clinical utility of the multi-Kinect v2 set-up to automate 10MWT assessments, thereby complementing the time to walk 10 meters with reliable spatiotemporal gait parameters obtained objectively in a quick, unobtrusive and patient-friendly manner.

Background: Most falls occur during walking and are due to trips, slips or misplaced steps, which suggests a reduced walking adaptability. The objective of this study was to evaluate the potential merit of a walkingadaptability assessment for identifying prospective fallers and risk factors for future falls in a cohort of stroke patients, Parkinson's disease patients, and controls (n = 30 for each group).Research question: Does an assessment of walking-adaptability improve the identification of fallers compared to generic fall-risk factors alone?Methods: This study comprised an evaluation of subject characteristics, clinical gait and balance tests, a quantitative gait assessment and a walking-adaptability assessment with the Interactive Walkway. Subjects' falls were registered prospectively with falls calendars during a 6-month follow-up period. Generic and walkingrelated fall-risk factors were compared between prospective fallers and non-fallers. Binary logistic regression and Chi-square Automatic Interaction Detector analyses were performed to identify fallers and predictor variables for future falls.Results: In addition to fall history, obstacle-avoidance success rate and normalized walking speed during goaldirected stepping correctly classified prospective fallers and were predictors of future falls. Compared to the use of generic fall-risk factors only, the inclusion of walking-related fall-risk factors improved the identification of prospective fallers.Significance: If cross-validated in future studies with larger samples, these fall-risk factors may serve as quick entry tests for falls prevention programs. In addition, the identification of these walking-related fall-risk factors may help in developing falls prevention strategies. Methods Subjects30 stroke patients, 30 PD patients and 30 controls participated in this study (Table 1). Groups were age-and sex-matched. Patients were https://doi.

Introduction: In people with Parkinson's disease (PD) many aspects of walking ability deteriorate with advancing disease. Clinical tests typically evaluate single aspects of walking and to a lesser extent assess more complex walking tasks involving a combination of the three key aspects of walking ability (i.e., generating stepping, maintaining postural equilibrium, adapting walking). The Interactive Walkway allows for assessing more complex walking tasks to address features that are relevant for daily life walking of patients, including adaptive walking and dual-task walking.Methods: To evaluate the expected added value of Interactive Walkway assessments in people with PD, we first evaluated its known-groups validity for outcome measures of unconstrained walking, adaptive walking and dual-task walking. Subsequently, these outcome measures were related to commonly used clinical test scores. Finally, we evaluated the expected added value of these outcomes over clinical tests scores in discriminating people with PD with and without freezing of gait.Results: Interactive Walkway outcome measures showed significant differences between freezers, non-freezers and healthy controls, in expected directions. Most Interactive Walkway outcome measures were not or at best moderately correlated with clinical test scores. Finally, Interactive Walkway outcome measures of adaptive walking slightly better discriminated freezers from non-freezers than clinical tests scores.Conclusion: We confirmed the added value of Interactive Walkway assessments, which provides a comprehensive evaluation of walking ability incorporating features of its three key aspects. Future studies are warranted to examine the potential of the Interactive Walkway for the assessment of fall risk and informing on tailored falls prevention programs in people with PD and in other populations with impaired walking ability.