Confidence Filtering in Video-Based Gait Assessment: Measurement Error versus Selection Shift

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1. Proche Base de cet article Cité par cet article Pas pertinent

   EMBC Special Issue: Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture

   Seth Donahue, Irina Djuraskovic, Kunal Shah et al. · 2026 · IEEE Transactions on Biomedical Engineering · cité 5 fois · Pas encore résumé

   « Calibrated Uncertainty » évalue l’étalonnage et la précision des intervalles de confiance d’une capture markerless multivue, préalable à l’analyse du filtrage par confiance.

   Résumé scientifique

   <italic>Objective:</italic> This study evaluates the calibration and accuracy of a probabilistic MMMC method. Video-based human movement analysis holds potential for movement assessment in clinical practice and research. However, the clinical implementation and trust of multi-view markerless motion capture (MMMC) require that, in addition to being accurate, these systems produce reliable confidence intervals to indicate how accurate they are for any individual. Building on our prior work utilizing variational inference to estimate estimate biomechanical variables against clinical gold-standards. <italic>Methods:</italic> We analyzed data from 68 participants across two institutions, validati…

2. Proche Même question Pas pertinent

   Improving reliability of movement assessment in Parkinson's disease using computer vision-based automated severity estimation

   Jinyu Xu, Xin Xu, Xudong Guo et al. · 2025 · Journal of Parkinson's Disease · cité 10 fois · Pas encore résumé

   « Improving reliability » estime automatiquement la sévérité motrice dans la maladie de Parkinson à partir de vidéos, comme l’évaluation clinique de la marche étudiée ici.

   Résumé scientifique

   Background Clinical assessments of motor symptoms rely on observations and subjective judgments against standardized scales, leading to variability due to confounders. Improving inter-rater agreement is essential for effective disease management. Objective We developed an objective rating system for Parkinson's disease (PD) that integrates computer vision (CV) and machine learning to correct potential discrepancies among raters while providing the basis for model performance to gain professional acceptance. Methods A prospective PD cohort (n = 128) were recruited from multi-centers. Motor examination videos were recorded using an android tablet with CV-based software following the MDS-UPDRS …

3. Proche Base de cet article Cité par cet article Pas pertinent

   Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change

   Jan Stenum, Melody Hsu, Alexander Pantelyat et al. · 2023 · medRxiv · cité 111 fois · Pas encore résumé

   « Clinical gait analysis » montre l’analyse quantitative de la marche par pose vidéo dans plusieurs populations cliniques, cadre repris par l’étude.

   Résumé scientifique

   Gait dysfunction is common in many clinical populations and often has a profound and deleterious impact on independence and quality of life. Gait analysis is a foundational component of rehabilitation because it is critical to identify and understand the specific deficits that should be targeted prior to the initiation of treatment. Unfortunately, current state-of-the-art approaches to gait analysis (e.g., marker-based motion capture systems, instrumented gait mats) are largely inaccessible due to prohibitive costs of time, money, and effort required to perform the assessments. Here, we demonstrate the ability to perform quantitative gait analyses in multiple clinical populations using only …

4. Proche Contexte plus large Pas pertinent

   Evaluating Temporal Windows and Subject -Specific Fine -Tuning for Freezing of Gait Detection with Classical Machine Learning Models

   Clebson I. S. Silva, R. de Freitas Zampolo, Antônio Pereira · 2025 · Anais do XVII Congresso Brasileiro de Inteligência Computacional · Pas encore résumé

   « Evaluating Temporal Windows » détecte le freezing de la marche par apprentissage automatique sur fenêtres temporelles, plutôt que d’étudier le biais du filtrage vidéo.

   Résumé scientifique

   Freezing of Gait (FoG) is a critical motor symptom in Parkinson’s disease and a prime target for wearable-based detection. While global models trained across subjects offer scalability, they may fail to generalize to individuals with distinct gait signatures. This study investigates the effectiveness of classical machine learning models, explores the impact of temporal window size, and evaluates whether subject-specific fine-tuning improves detection performance. Using data from a dataset with 35 subjects performing a rotation-eliciting task, we show that ensemble models with 4–5 s windows achieve F1-scores up to 0.92. The results of the importance analysis of features highlight the role of …

5. Proche Contexte plus large Pas pertinent

   A Dual-Use Framework for Clinical Gait Analysis: Attention-Based Sensor Optimization and Automated Dataset Auditing

   Hamidreza Sadeghsalehi · 2025 · arXiv.org · preprint, non relu par les pairs · cité 2 fois · Pas encore résumé

   « A Dual-Use Framework » examine les biais de données dans l’analyse clinique de la marche et optimise les capteurs, tandis que l’étude analyse le déplacement dû au filtrage.

   Résumé scientifique

   Objective gait analysis using wearable sensors and AI is critical for managing neurological and orthopedic conditions. However, models are vulnerable to hidden dataset biases, and task-specific sensor optimization remains a challenge. We propose a multi-stream attention-based deep learning framework that functions as both a sensor optimizer and an automated data auditor. Applied to the Voisard et al. (2025) multi-cohort gait dataset on four clinical tasks (PD, OA, CVA screening; PD vs CVA differential), the model's attention mechanism quantitatively discovered a severe dataset confound. For OA and CVA screening, tasks where bilateral assessment is clinically essential, the model assigned mor…

6. Proche Même question Pas pertinent

   A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson’s Disease

   S. Rupprechter, Gareth Morinan, Yuwei Peng et al. · 2021 · Italian National Conference on Sensors · cité 77 fois · Pas encore résumé

   « A Clinically Interpretable Method » estime la sévérité de l’atteinte de la marche dans Parkinson à partir de vidéos et de vision par ordinateur.

   Résumé scientifique

   Gait is a core motor function and is impaired in numerous neurological diseases, including Parkinson’s disease (PD). Treatment changes in PD are frequently driven by gait assessments in the clinic, commonly rated as part of the Movement Disorder Society (MDS) Unified PD Rating Scale (UPDRS) assessment (item 3.10). We proposed and evaluated a novel approach for estimating severity of gait impairment in Parkinson’s disease using a computer vision-based methodology. The system we developed can be used to obtain an estimate for a rating to catch potential errors, or to gain an initial rating in the absence of a trained clinician—for example, during remote home assessments. Videos (n=729) were co…

7. Proche Article de revue Pas pertinent

   Reliability and validity of current computer vision based motion capture systems in gait analysis: A systematic review.

   Xing-Ye Cheng, Yiran Jiao, R. Meiring et al. · 2025 · Gait & Posture · cité 41 fois · Pas encore résumé

   « Reliability and validity » synthétise la précision des systèmes d’analyse de la marche fondés sur l’estimation de pose et les facteurs algorithmiques associés.

   Résumé scientifique

   BACKGROUND Traditional instrumented gait analysis (IGA) objectively quantifies gait deviations, but its clinical use is hindered by high cost, lab environment, and complex protocols. Pose estimation algorithm (PEA)-based gait analysis, which infers joint positions from videos, offers an accessible method to detect gait abnormalities and tailor rehabilitation strategies. However, its reliability and validity in gait analysis and algorithmic factors affecting accuracy have not been reviewed. RESEARCH QUESTION This systematic review aims to evaluate the accuracy of PEA-based gait analysis systems and to identify the algorithmic factors impacting their accuracy. METHOD A total of 644 articles we…

8. Proche Même méthode, autre problème Cité par cet article Pas pertinent

   Computer Vision for Gait Assessment in Cerebral Palsy: Metric Learning and Confidence Estimation

   Peijun Zhao, M. Alencastre-Miranda, Zhan Shen et al. · 2024 · IEEE transactions on neural systems and rehabilitation engineering · cité 12 fois · Pas encore résumé

   « Computer Vision for Gait Assessment » utilise pose vidéo et estimation de confiance pour évaluer les troubles moteurs, mais chez des personnes atteintes de paralysie cérébrale.

   Résumé scientifique

   Assessing the motor impairments of individuals with neurological disorders holds significant importance in clinical practice. Currently, these clinical assessments are time-intensive and depend on qualitative scales administered by trained healthcare professionals at the clinic. These evaluations provide only coarse snapshots of a person’s abilities, failing to track quantitatively the detail and minutiae of recovery over time. To overcome these limitations, we introduce a novel machine learning approach that can be administered anywhere including home. It leverages a spatial-temporal graph convolutional network (STGCN) to extract motion characteristics from pose data obtained from monocular…

Les travaux antérieurs sur lesquels il s’appuie

1. Apparenté Même méthode, autre problème Utilisé en profondeur par cet article Pas pertinent

   Bridge-Disease Curriculum Transfer Enables Smartphone-Based Balance Assessment in Rare Neurologic Disorders

   E. Bamani, J. Buzzatto, Fang-Qiang Ding et al. · 2026 · IEEE transactions on neural systems and rehabilitation engineering · cité 1 fois · Pas encore résumé

   « Bridge-Disease Curriculum Transfer » utilise des vidéos et des modèles de pose pour évaluer l’équilibre dans NF2, avec transfert depuis Parkinson.

   Résumé scientifique

   Video-based motor assessment could enable scalable assessment of neurological function, but rare-disease applications are constrained by extreme data scarcity and poor generalization from small cohorts. We develop and evaluate a smartphone-based balance-assessment framework in NF2-related schwannomatosis (NF2-SWN), an ultra-rare disorder for which objective balance assessment is clinically valuable yet logistically challenging. At its core, a staged bridge-disease curriculum adapts a skeleton-based spatiotemporal graph model from large-scale action recognition through an intermediate Parkinson’s disease domain to the target NF2-SWN cohort ( ${N}={19}$ ), decomposing one large domain shift in…

2. Apparenté Base de cet article Pas pertinent

   Using psychometric techniques to improve the Balance Evaluation System’s Test: the mini-BESTest

   F. Franchignoni, F. Horak, M. Godi et al. · 2010 · Journal of Rehabilitation Medicine · cité 1 015 fois · Pas encore résumé

   « Using psychometric techniques » développe le Mini-BESTest utilisé ici comme mesure clinique de l’équilibre.

   Résumé scientifique

   Objective To improve, with the aid of psychometric analysis, the Balance Evaluation System’s Test (BESTest), a tool designed to analyse several postural control systems that may contribute to poor functional balance in adults. Methods We examined performance of the BESTest in a convenience sample of 115 consecutive adult patients with diverse neurological diagnoses and disease severity, referred to rehabilitation for balance disorders. Factor (both explorative and confirmatory) and Rasch analysis were used to process the data in order to produce a new, reduced and coherent balance measurement tool. Results Factor analysis selected 24 out of the 36 original BESTest items likely to represent t…

3. Apparenté Même méthode, autre problème Pas pertinent

   Selective Regression Under Fairness Criteria

   Abhin Shah, Yuheng Bu, J. Lee et al. · 2021 · International Conference on Machine Learning · cité 45 fois · Pas encore résumé

   « Selective Regression » étudie la sélection de prédictions selon la confiance et ses effets sur les sous-groupes, plutôt que sur les mesures de mouvement.

   Résumé scientifique

   Selective regression allows abstention from prediction if the confidence to make an accurate prediction is not sufficient. In general, by allowing a reject option, one expects the performance of a regression model to increase at the cost of reducing coverage (i.e., by predicting on fewer samples). However, as we show, in some cases, the performance of a minority subgroup can decrease while we reduce the coverage, and thus selective regression can magnify disparities between different sensitive subgroups. Motivated by these disparities, we propose new fairness criteria for selective regression requiring the performance of every subgroup to improve with a decrease in coverage. We prove that if a…

4. Apparenté Même méthode, autre problème Pas pertinent

   Interpretable Biomechanical Embeddings for CP Gait: From Clinical Metrics to Learned Representations

   E. B. Beeri, J. Buzzatto, H. Krebs · 2026 · International Conference on Biomedical Robotics and Biomechatronics · cité 1 fois · Pas encore résumé

   « Interpretable Biomechanical Embeddings » applique des représentations apprises à la marche en paralysie cérébrale, tandis que l’étude porte sur le filtrage des poses.

   Résumé scientifique

   Recent advances in video-based gait analysis have enabled accurate clinical assessment of movement disorders using deep learning. Yet, the interpretability of these models and their correspondence to established biomechanical reasoning remain poorly understood, hindering their integration into clinical practice. This study complements our previous work on diffusion-augmented spatio-temporal graph convolutional networks (BA-STGCN) for gait assessment in children with cerebral palsy (CP). While our earlier research emphasized robustness and predictive accuracy, the present work shifts focus toward interpretability and clinical transparency. We investigate how data-driven spatio-temporal repres…

5. Apparenté Même méthode, autre problème Pas pertinent

   Diffusion-Augmented Spatiotemporal Graph Convolution for Clinical Gait and Motor Function Assessment

   E. Bamani, J. Buzzatto, H. Krebs · 2026 · IEEE transactions on neural systems and rehabilitation engineering · cité 4 fois · Pas encore résumé

   Cette méthode combine diffusion de squelettes et réseau spatio-temporel pour évaluer la motricité en paralysie cérébrale, tandis que l’article étudie le filtrage de poses.

   Résumé scientifique

   Accurate assessment of gross motor function in children with cerebral palsy (CP) is essential for clinical decision-making, yet current practice is limited by data scarcity, severe class imbalance, and patient heterogeneity. Recent skeleton-based deep learning approaches, such as spatio-temporal graph convolutional networks (STGCN), enable automatic GMFCS prediction from monocular video but are limited in generalizability and fairness. In this work, we propose a unified generative–diagnostic pipeline that integrates a Conditional Skeleton Diffusion Model (CSDM) with a Biomechanically-Aware Spatio-Temporal Graph Convolutional Network (BA-STGCN). The CSDM generates clinically plausible 2D skel…