Robustness and static-positional accuracy of the SteamVR 1.0 virtual reality tracking system

The use of low-cost immersive virtual reality systems is rapidly expanding. Several studies started to analyse the accuracy of virtual reality tracking systems, but they did not consider in depth the effects of external interferences in the working area. In line with that, this study aimed at exploring the static-positional accuracy and the robustness to occlusions inside the capture volume of the SteamVR (1.0) tracking system. To do so, we ran 3 different tests in which we acquired the position of HTC Vive PRO Trackers (2018 version) on specific points of a grid drawn on the floor, in regular tracking conditions and with partial and total occlusions. The tracking system showed a high inter- and intra-rater reliability and detected a tilted surface with respect to the floor plane. Every acquisition was characterised by an initial random offset. We estimated an average accuracy of 0.5 +/- 0.2 cm across the entire grid (XY-plane), noticing that the central points were more accurate (0.4 +/- 0.1 cm) than the outer ones (0.6 +/- 0.1 cm). For the Z-axis, the measurements showed greater variability and the accuracy was equal to 1.7 +/- 1.2 cm. Occlusion response was tested using nonparametric Bland-Altman statistics, which highlighted the robustness of the tracking system. In conclusion, our results promote the SteamVR system for static measures in the clinical field. The computed error can be considered clinically irrelevant for exercises aimed at the rehabilitation of functional movements, whose several motor outcomes are generally measured on the scale of metres

Decomposition of 3D joint kinematics of walking in Drosophila melanogaster

Animals exhibit a rich repertoire of locomotive behaviors. In the context of legged locomotion, i.e. walking, animals can change their heading direction, traverse diverse substrates with different speeds, or can even compensate for the loss of a leg. This versatility emerges from the fact that biological limbs have more joints and/or more degrees of freedom (DOF), i.e. independent directions of motions, than required for any single movement task. However, this further entails that multiple, or even infinitely many, joint configuration can result in the same leg stepping pattern during walking. How the nervous system deals with such kinematic redundancy remains still unknown. One proposed hypothesis is that the nervous system does not control individual DOFs, but uses flexible combinations of groups of anatomical or functional DOFs, referred to as motor synergies. Drosophila melanogaster represents an excellent model organism for studying the motor control of walking, not least because of the extensive genetic toolbox available, which, among others, allows the identification and targeted manipulation of individual neurons or muscles. However, their tiny size and ability for relatively rapid leg movements hampered research on the kinematics at the level of leg joints due to technical limitations until recently. Hence, the main objective of this dissertation was to investigate the three-dimensional (3D) leg joint kinematics of Drosophila during straight walking. For this, I first established a motion capture setup for Drosophila which allowed the accurate reconstruction of the leg joint positions in 3D with high temporal resolution (400 Hz). Afterwards, I created a kinematic leg model based on anatomical landmarks, i.e. joint condyles, extracted from micro computed-tomography scan data. This step was essential insofar that the actual DOFs of the leg joints in Drosophila were currently unknown. By using this kinematic model, I have found that a mobile trochanter-femur joint can best explain the leg movements of the front legs, but is not mandatory in the other leg pairs. Additionally, I demonstrate that rotations of the femur-tibia plane in the middle legs arise from interactions between two joints suggesting that the natural orientation of joint rotational axes can extent the leg movement repertoire without increasing the number of elements to be controlled. Furthermore, each leg pair exhibited distinct joint kinematics in terms of the joint DOFs employed and their angle time courses during swing and stance phases. Since it is proposed that the nervous system could use motor synergies to solve the redundancy problem, I finally aimed to identify kinematic synergies based on the obtained joint angles from the kinematic model. By applying principal component analysis on the mean joint angle sets of leg steps, I found that three kinematic synergies are sufficient to reconstruct the movements of the tarsus tip during stepping for all leg pairs. This suggests that the problem of controlling seven to eight joint DOFs can be in principle reduced to three control parameters. In conclusion, this dissertation provides detailed insights into the leg joint kinematics of Drosophila during forward walking which are relevant for deciphering motor control of walking in insects. When combined with the extensive genetic toolbox offered by Drosophila as model organism, the experimental platform presented here, i.e. the 3D motion capture setup and the kinematic leg model, can facilitate investigations of Drosophila walking behavior in the future

Robust labeling of human motion markers in the presence of occlusions

Human motion capture by optical sensors produces snapshots of the motion of a cloud of points that need to be labeled in order to carry out ensuing motion analysis for medical or other purposes. We generate the labeling of instantaneous captures of the cloud of points, discarding temporal correlations, in the presence of occlusions. Our approach proposes an ensemble of weak classifiers defined over geometrical features extracted from small subsets of the cloud of points. We apply an Adaboost strategy to select a minimal ensemble of weak classifiers achieving a target correct labeling detection accuracy. Furthermore, we use these features to generate the labeling of the points in the cloud even in the presence of occlusions.To deal with the occlusions of markers we search for ensembles of partial labeling solvers which can provide partial consistent labelings which cover the unoccluded markers. We test two greedy search approaches and a genetic algorithm in the search for the optimal ensemble of partial solvers We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, with available ground truth labeling. Results are encouraging, achieving high accuracy label generation at a reduced computational cost. (C) 2019 The Author(s). Published by Elsevier B.V.The work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and projects KK-2018/00071 and KK-2018/00082 of the Elkartek 2018 funding program of the Basque Government. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 777720