Background: The prevalence of neurological disorders is increasing, underscoring the importance of objective gait analysis to help clinicians identify specific deficits. Nevertheless, existing technological solutions for gait analysis often suffer from impracticality in daily clinical use, including excessive cost, time constraints, and limited processing capabilities. Summary: This review aims to evaluate existing techniques for clustering patients with the same neurological disorder to assist clinicians in optimizing treatment options. A narrative review of thirteen relevant studies was conducted, characterizing their methods, and evaluating them against seven criteria. Additionally, the results are summarized in two comprehensive tables. Recent approaches show promise; however, our results indicate that, overall, only three approaches display medium or high process maturity, and only two show high clinical applicability. Key Messages: Our findings highlight the necessity for advancements, specifically regarding the use of markerless optical tracking systems, the optimization of experimental plans, and the external validation of results. This narrative review provides a comprehensive overview of existing clustering techniques, bridging the gap between instrumented gait analysis and its real-world clinical utility. We encourage researchers to use our findings and those from other medical fields to enhance clustering techniques for patients with neurological disorders, facilitating the identification of disparities within groups and their extent, ultimately improving patient outcomes.

Golf is a sport that consists of complex movement skills that need to be executed with utmost precision. Consequently, motor skill learning plays a crucial role in golf, and large numbers of studies address various methods of motor learning. In the present review, we give a systematic overview of randomized controlled trials (RCTs) on motor learning of golf-specific motor skills. Three electronic databases were searched for RCTs looking at the effect of at least one learning method on performance in a golf-specific motor task. We grouped the studies depending on the learning strategies “cognitive training”, “practice scheduling”, “augmented feedback”, “implicit and explicit learning” and “focus of attention”. Fifty-two RCTs met the eligibility criteria and were included in the systematic review. Superior methods within their respective strategies were an external focus of attention and increasing contextual interference, as well as errorless learning. For “cognitive training” and “augmented feedback”, no single method can be considered the most favorable. The overall biggest limitations were the lack of statistical power for more than half of the RCTs, and the fact that most studies of the present review investigated simple putting tasks in novices only. Although we have shown superiority of specific learning methods, transferability of the recommendations that can be derived from simple golf tasks in novices to sport-specific tasks in advanced players still has to be demonstrated and require study designs with the intention to provide practical recommendations for coaches and athletes in the sport of golf.

Clinical gait analysis plays a central role in the rehabilitation of stroke patients. However, practical and technical challenges limit their use in clinical settings. This study aimed to validate SMARTGAIT, a deep learning-based gait analysis system that addresses these limitations. Eight stroke patients took part in the study at the Human Performance Research Centre of the University of Konstanz. Gait measurements were taken using both the marker-based Vicon motion capture system and the single-smartphone-based SMARTGAIT system. We evaluated the agreement for knee, hip, and ankle joint angle kinematics in the frontal and sagittal plane and spatiotemporal gait parameters between the two systems. The results mostly demonstrated high levels of agreement between the two systems, with Pearson correlations of ≥0.79 for all lower body angle kinematics in the sagittal plane and correlations of ≥0.71 in the frontal plane. RMSE values were ≤4.6°. The intraclass correlation coefficients for all derived gait parameters showed good to excellent levels of agreement. SMARTGAIT is a promising tool for gait analysis in stroke, particularly for quantifying gait characteristics in the sagittal plane, which is very relevant for clinical gait analysis. However, further analyses are required to validate the use of SMARTGAIT in larger samples and its transferability to different types of pathological gait. In conclusion, a single smartphone recording (monocular 2D RGB camera) could make gait analysis more accessible in clinical settings, potentially simplifying the process and making it more feasible for therapists and doctors to use in their day-to-day practice.

Recently, AI-driven skeleton reconstruction tools that use multistage computer vision pipelines were designed to estimate 3D kinematics from 2D video sequences. In the present study, we validated a novel markerless, smartphone video-based artificial intelligence (AI) motion capture system for hip, knee, and ankle angles during countermovement jumps (CMJs). Eleven participants performed six CMJs. We used 2D videos created by a smartphone (Apple iPhone X, 4K, 60 fps) to create 24 different keypoints, which together built a full skeleton including joints and their connections. Body parts and skeletal keypoints were localized by calculating confidence maps using a multilevel convolutional neural network that integrated both spatial and temporal features. We calculated hip, knee, and ankle angles in the sagittal plane and compared it with the angles measured by a VICON system. We calculated the correlation between both method's angular progressions, mean squared error (MSE), mean average error (MAE), and the maximum and minimum angular error and run statistical parametric mapping (SPM) analysis. Pearson correlation coefficients (r) for hip, knee, and ankle angular progressions in the sagittal plane during the entire movement were 0.96, 0.99, and 0.87, respectively. SPM group-analysis revealed some significant differences only for ankle angular progression. MSE was below 5.7°, MAE was below 4.5°, and error for maximum amplitudes was below 3.2°. The smartphone AI motion capture system with the trained multistage computer vision pipeline was able to detect, especially hip and knee angles in the sagittal plane during CMJs with high precision from a frontal view only.

The purpose of the present study was to assess the validity and reliability of the jump height measured by the Polar Vantage V2 sports watch in comparison to a gold-standard force plate measurement. Fifteen healthy adults, seven female, age 20–42 years participated in the study and performed six sets of three CMJs, on two consecutive days. The participants wore the Polar Vantage V2 sports watch (Polar Electro Oy, Kempele, Finland) whilst performing the jumps on two force plates (AMTI, Watertown, Massachusetts, United States). Jump height was on the one hand extracted directly from the watch (“leg recovery test”) and on the other hand calculated by the flight time method with the force plate data. To assess validity, we calculated the mean absolute error, constructed Bland-Altman plots and applied an ordinary least squares regression analysis. To test for left-to-right and day-to-day reliability, we calculated Pearson and intraclass correlations. We found a mean error of ≈5% and a high correlation (r = 0.96; p < 0.001) for the jump height measured by the Polar Vantage V2 sports watch compared to the force plate measurement. The Bland-Altmann plot together with the ordinary least squares regression analysis showed no systematic bias between the methods with a minimal difference at a jump height of 30 cm. For reliability of left-to-right and day-to-day measurements, we found high Pearson and ICC correlations and no indications for systematic bias by Bland-Altmann analysis. The present study has demonstrated that the “leg recovery test” of the Polar Vantage V2 sports watch provide a valid and reliable measurement of the mean vertical jump height of three consecutive CMJs. For the first time the jump height of a CMJ can be measured solely by a sports watch without the need to attach additional sensors or measurement devices. Thus, the “leg recovery test” is an easy to administer, valid and reliable test, that can be used in future studies to measure CMJ-height in the field when lab-based assessments are unavailable or inconvenient. This opens new avenues for cross-sectional and longitudinal assessments of neuromuscular power of the lower extremities in a large number of participants.

Die steigende Digitalisierung aller Lebensbereiche und der ungebrochene Aufstieg spezialisierter (z.B. ResearchGate) und domänenübergreifender (z.B. Twitter) sozialer Netzwerke macht auch vor der Sportwissenschaft nicht Halt. Dieser Aufstieg und die hiermit verbundenen Chancen und Risiken für die Sportwissenschaft werden bereits im Ze-Phir („Digitale Sportwissenschaft – Schöne Neue Welt“, 2020) umfassend beleuchtet und diskutiert. Allein seit Erscheinen des Ze-Phirs konnte Twitter 20 Millionen neue täglich aktive Nutzer*innen gewinnen (Stand: Q2 2021) und ResearchGate wuchs von 17 Millionen auf 20 Millionen Nutzer*innen (ResearchGate 2021; Pöppel 2020). Unmittelbar betroffen von dieser Entwicklung ist die Art und Weise wie Wissenschaft kommuniziert werden kann und wird. Insbesondere der Kurznachrichtendienst Twitter erscheint hierbei besonders relevant. Daher thematisieren wir in diesem Beitrag unsere subjektiven Erfahrungen und Eindrücke zur Nutzung von Twitter als Wissenschaftskommunikationswerkzeug.