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      Avoiding 3D Obstacles in Mixed Reality: Does It Differ from Negotiating Real Obstacles?

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          Abstract

          Mixed-reality technologies are evolving rapidly, allowing for gradually more realistic interaction with digital content while moving freely in real-world environments. In this study, we examined the suitability of the Microsoft HoloLens mixed-reality headset for creating locomotor interactions in real-world environments enriched with 3D holographic obstacles. In Experiment 1, we compared the obstacle-avoidance maneuvers of 12 participants stepping over either real or holographic obstacles of different heights and depths. Participants’ avoidance maneuvers were recorded with three spatially and temporally integrated Kinect v2 sensors. Similar to real obstacles, holographic obstacles elicited obstacle-avoidance maneuvers that scaled with obstacle dimensions. However, with holographic obstacles, some participants showed dissimilar trail or lead foot obstacle-avoidance maneuvers compared to real obstacles: they either consistently failed to raise their trail foot or crossed the obstacle with extreme lead-foot margins. In Experiment 2, we examined the efficacy of mixed-reality video feedback in altering such dissimilar avoidance maneuvers. Participants quickly adjusted their trail-foot crossing height and gradually lowered extreme lead-foot crossing heights in the course of mixed-reality video feedback trials, and these improvements were largely retained in subsequent trials without feedback. Participant-specific differences in real and holographic obstacle avoidance notwithstanding, the present results suggest that 3D holographic obstacles supplemented with mixed-reality video feedback may be used for studying and perhaps also training 3D obstacle avoidance.

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          Real-time human pose recognition in parts from single depth images

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            Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

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              Kinematic Validation of a Multi-Kinect v2 Instrumented 10-Meter Walkway for Quantitative Gait Assessments

              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.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                17 February 2020
                February 2020
                : 20
                : 4
                : 1095
                Affiliations
                Department of Human Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam Movement Sciences, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands; p.j.beek@ 123456vu.nl (P.J.B.); d.j.geerse@ 123456vu.nl (D.J.G.); m.roerdink@ 123456vu.nl (M.R.)
                Author notes
                [* ]Correspondence: h.coolen@ 123456vu.nl
                Author information
                https://orcid.org/0000-0001-5642-1531
                https://orcid.org/0000-0002-0917-8548
                https://orcid.org/0000-0002-8534-1124
                Article
                sensors-20-01095
                10.3390/s20041095
                7071133
                32079351
                69e6feb5-3e22-47e8-875f-e89c993509eb
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 December 2019
                : 14 February 2020
                Categories
                Article

                Biomedical engineering
                hololens,mixed-reality headset,mixed-reality video feedback,walking adaptability,obstacle avoidance

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