Clustering Approaches for Gait Analysis within Neurological Disorders: A Narrative Review.

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.

Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets.

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.

Where to go: Computational and visual what-if analyses in soccer.

To prepare their teams for upcoming matches, analysts in professional soccer watch and manually annotate up to three matches a day. When annotating matches, domain experts try to identify and improve suboptimal movements based on intuition and professional experience. The high amount of matches needing to be analysed manually result in a tedious and time-consuming process, and results may be subjective. We propose an automatic approach for the realisation of effective region-based what-if analyses in soccer. Our system covers the automatic detection of region-based faulty movement behaviour, as well as the automatic suggestion of possible improved alternative movements. As we show, our approach effectively supports analysts and coaches investigating matches by speeding up previously time-consuming work. We enable domain experts to include their domain knowledge in the analysis process by allowing to interactively adjust suggested improved movement, as well as its implications on region control. We demonstrate the usefulness of our proposed approach via an expert study with three invited domain experts, one being head coach from the first Austrian soccer league. As our results show that experts most often agree with the suggested player movement (83%), our proposed approach enhances the analytical capabilities in soccer and supports a more efficient analysis.

Tackling Similarity Search for Soccer Match Analysis: Multimodal Distance Measure and Interactive Query Definition.

Analysts and coaches in soccer sports need to investigate large sets of past matches of opposing teams in a short time to prepare their teams for upcoming matches. Thus, they need appropriate methods and systems supporting them in searching for soccer moves for comparison and explanation. For the search of similar soccer moves, established distance and similarity measures typically only take spatiotemporal features like shape and speed of movement into account. However, movement in invasive team sports such as soccer includes much more than just a sequence of spatial locations. We propose an enhanced similarity measure integrating spatial, player, event as well as high level context such as pressure into the process of similarity search. We present a visual search system supporting analysts in interactively identifying similar contextual enhanced soccer moves in a dataset containing more than 60 soccer matches. Our approach is evaluated by several expert studies. The results of the evaluation reveal the large potential of enhanced similarity measures in the future.

Bring It to the Pitch: Combining Video and Movement Data to Enhance Team Sport Analysis.

Analysts in professional team sport regularly perform analysis to gain strategic and tactical insights into player and team behavior. Goals of team sport analysis regularly include identification of weaknesses of opposing teams, or assessing performance and improvement potential of a coached team. Current analysis workflows are typically based on the analysis of team videos. Also, analysts can rely on techniques from Information Visualization, to depict e.g., player or ball trajectories. However, video analysis is typically a time-consuming process, where the analyst needs to memorize and annotate scenes. In contrast, visualization typically relies on an abstract data model, often using abstract visual mappings, and is not directly linked to the observed movement context anymore. We propose a visual analytics system that tightly integrates team sport video recordings with abstract visualization of underlying trajectory data. We apply appropriate computer vision techniques to extract trajectory data from video input. Furthermore, we apply advanced trajectory and movement analysis techniques to derive relevant team sport analytic measures for region, event and player analysis in the case of soccer analysis. Our system seamlessly integrates video and visualization modalities, enabling analysts to draw on the advantages of both analysis forms. Several expert studies conducted with team sport analysts indicate the effectiveness of our integrated approach.

Director's Cut: Analysis and Annotation of Soccer Matches.

For development and alignment of tactics and strategies, professional soccer analysts spend up to three working days manually analyzing and annotating professional soccer matches. In an effort to improve soccer player and match analysis, a visual-interactive and data-analysis support system focuses on key situations by using rule-based filtering and automatically annotating key types of soccer match elements. The authors evaluate the proposed approach by analyzing real-world soccer matches and several expert studies. Quantitative measures show the proposed methods can significantly outperform naive solutions.