Eine kinematische Analyse der Fingerbewegung beim Lösen der Sehne im Recurve-Bogensport

Ziel dieser Arbeit ist die Erfassung kinematischer Daten der Fingerbewegungen und der Bogensehnenbewegung beim Lösen der Sehne im Recurvebogensport. METHODIK: Zur Erfassung der kinematischen Daten wurde ein Infrarotkamerasystem der Firma Vicon verwendet. Untersucht wurde ein B-Nationalkader Schütze mit internationaler Wettkampferfahrung (WM Teilnahme 2007). 56 ausgewertete Schüsse wurden in zwei Gruppen eingeteilt: gute und schlechte Schüsse. Weiters wurden fünf kinematische Kenngrößen definiert und für beide Gruppen mittels eines Student´s t-Test für unabhängige Stichproben statistisch überprüft. Folgende fünf Kenngrößen wurden bestimmt: Minimumwert der Winkelgeschwindigkeit (MIN) beim Lösen der Bogensehne, Maximumwert der Winkelgeschwindigkeit (MAX) beim Lösen der Bogensehne, Zeitspanne (P-P) zwischen dem Auftreten von MIN und MAX, Vergleich (VL) der direkten Vektorenweglänge der Bogensehne mit der tatsächlichen und die maximale Sehnenseitauslenkung (hg). ERGEBNISSE: Hinsichtlich der fünf kinematischen Kenngrößen wurde ein signifikanter Unterschied in der Kenngröße hg (p=0,003*) gefunden. Als gute quantifizierte Schüsse zeigten eine größere Auslenkung der Bogensehne zur Seite auf (Mittelwert = 24,31 ± 0,7 mm), als Schüsse, welche als schlecht quantifiziert wurden (Mittelwert = 23,42 ± 0,8 mm). Bei allen weiteren Kenngrößen fand sich kein signifikanter Unterschied in den Gruppen.The aim of this paper is a three-dimensional kinematic analysis of finger and bowstring movement during bowstring release. METHOD: A top athlete of the Austrian B-national Team was investigated. A sum of 90 shots was captured. Fifty-six shots could be used for further analysis. The shots were, due to their achieved score, divided into two groups: good and bad shots. Furthermore, there were five different kinematic variables defined (related to the finger middle joint of the third finger): maximum angular Velocity (MAX), minimum angular velocity (MIN), period between MAX and MIN (P-P), maximum bowstring side-movement (hg) and the comparison (VL) of the distance between the direct vector length and the real vector length of the bowstring. A Student´s t-Test was calculated to check if there are any significant differences between the two groups in the kinematic variables. RESULTS: Significant differences were only found in the variable hg (p=0,003*). Shots out of the “good” group showed larger bowstring side-movement (mean hg = 24,31 ± 0,7 mm) than shots out of the “bad” group (mean hg = 23,42 ± 0,8 mm). All other variables showed no significant differences

Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy

Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time series data. For the automated analysis of these data, machine learning approaches yield promising results. However, due to their black-box nature, such approaches are often mistrusted by clinicians. We propose gaitXplorer, a visual analytics approach for the classification of CP-related gait patterns that integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications. Regions of high relevance for classification are highlighted in the interactive visual interface. The approach is evaluated in a case study with two clinical gait experts. They inspected the explanations for a sample of eight patients using the visual interface and expressed which relevance scores they found trustworthy and which they found suspicious. Overall, the clinicians gave positive feedback on the approach as it allowed them a better understanding of which regions in the data were relevant for the classification.Comment: 7 pages, 4 figures; supplemental material 9 pages, 8 figures; to be published in the proceedings of the 2022 IEEE Workshop on TRust and EXpertise in Visual Analytics (TREX

Modeling biological individuality using machine learning: A study on human gait

Human gait is a complex and unique biological process that can offer valuable insights into an individual’s health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test data being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual’s gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3%), followed by Random Forests (98.7%), Convolutional Neural Networks (95.8%), and Decision Trees (82.8%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions

A wireless instrumented insole device for real-time sonification of gait

Presented at the 21st International Conference on Auditory Display (ICAD2015), July 6-10, 2015, Graz, Styria, Austria.The treatment of gait disorders or impairments is one major challenge in physical therapy. The broad and fast development in low-cost, miniaturized and wireless sensing technologies supported the development of embedded and unobtrusive systems for robust gait-related data acquisition and analysis. Next to their application as portable and lowcost diagnosis tools, such systems bear also the capability of using them as feedback devices during gait retraining to foster motor learning processes. The approach described within this project applies movement-based sonification of gait to foster motor learning aspects during gait retraining. In detail the aim of this manuscript is threefold: (1) present a prototype (the SONIGait device) of a pair of wireless, sensor insoles instrumented with force-sensors for real-time data transmission and acquisition on a mobile client, (2) present the development of a set of sonification prototypes for realtime audible feedback and (3) evaluate the sonification prototypes as well as the SONIGait device within a pilot stud