An expert system feedback tool improves the reliability of clinical gait kinematics for older adults with lower limb osteoarthritis

Osis, Sean T.; Kobsar, Dylan; Leigh, Ryan J.; Macaulay, Charles A.J.; Ferber, Reed

doi:10.1016/j.gaitpost.2017.08.011

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…Moreover, all data were collected by a single examiner with over 20 years’ experience. This is an especially important point when using unsupervised machine learning methods to identify subgroups, as these methods might pick up patterns originating from subtly different marker placements resulting from different examiners . Specifically, Osis et al reported that a novice examiner, with 6‐years of experience and trained by the same expert examiner used in the current study, made improvements in their consistency over the course of one‐year of training.…”

Section: Discussionmentioning

confidence: 76%

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A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

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Previous studies have suggested that runners can be subgrouped based on homogeneous gait patterns; however, no previous study has assessed the presence of such subgroups in a population of individuals across a wide variety of injuries. Therefore, the purpose of this study was to assess whether distinct subgroups with homogeneous running patterns can be identified among a large group of injured and healthy runners and whether identified subgroups are associated with specific injury location. Three‐dimensional kinematic data from 291 injured and healthy runners, representing both sexes and a wide range of ages (10‐66 years), were clustered using hierarchical cluster analysis. Cluster analysis revealed five distinct subgroups from the data. Kinematic differences between the subgroups were compared using one‐way analysis of variance (ANOVA). Against our hypothesis, runners with the same injury types did not cluster together, but the distribution of different injuries within subgroups was similar across the entire sample. These results suggest that homogeneous gait patterns exist independent of injury location and that it is important to consider these underlying patterns when planning injury prevention or rehabilitation strategies.

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Section: Discussionmentioning

confidence: 76%

“…However, systematic differences were apparent in data collected during the end of the year. Thus, future research involving a large cohort should take into consideration the number of people collecting the data and/or use appropriate feedback methods to minimize marker placement error.…”

Section: Discussionmentioning

confidence: 99%

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Jauhiainen

Pohl

Äyrämö

et al. 2020

Scandinavian Med Sci Sports

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“…By far the most common use is in gait analysis ( 147 ). Early studies included automated detection of gait events using inductive learning ( 148 ) and an integrated gait analysis framework ( 149 , 150 ). Further advances in the use of ES came with the advent of multi-sensor technology and analytics including video capture of movement with reflective sensors placed on the body, force plate derived features and physiological/neuromuscular features ( 151 ).…”

Section: Gait Analysis and Artificial Intelligencementioning

confidence: 99%

“…Use of more ANN-based classification superseded ES with AI demonstrating better accuracy as compared to statistical expert systems models. ANN consists of a series of interconnected nodes that can be either designed as a single layer or multilayer approximating the relationships, or adaptive weightings determined from a training set, between input and output measures, which can then be applied to unseen data ( 150 ). Lugade and colleagues applied self-organizing maps (SOM) or so-called Kohonen maps to estimate gait balance control in the elderly using clinical evaluations ( 152 ).…”

Section: Gait Analysis and Artificial Intelligencementioning

confidence: 99%

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

Mohan

Khandoker

Wasti³

et al. 2021

Front. Neurol.

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Background: Gait dysfunction or impairment is considered one of the most common and devastating physiological consequences of stroke, and achieving optimal gait is a key goal for stroke victims with gait disability along with their clinical teams. Many researchers have explored post stroke gait, including assessment tools and techniques, key gait parameters and significance on functional recovery, as well as data mining, modeling and analyses methods.Research Question: This study aimed to review and summarize research efforts applicable to quantification and analyses of post-stroke gait with focus on recent technology-driven gait characterization and analysis approaches, including the integration of smart low cost wearables and Artificial Intelligence (AI), as well as feasibility and potential value in clinical settings.Methods: A comprehensive literature search was conducted within Google Scholar, PubMed, and ScienceDirect using a set of keywords, including lower extremity, walking, post-stroke, and kinematics. Original articles that met the selection criteria were included.Results and Significance: This scoping review aimed to shed light on tools and technologies employed in post stroke gait assessment toward bridging the existing gap between the research and clinical communities. Conventional qualitative gait analysis, typically used in clinics is mainly based on observational gait and is hence subjective and largely impacted by the observer's experience. Quantitative gait analysis, however, provides measured parameters, with good accuracy and repeatability for the diagnosis and comparative assessment throughout rehabilitation. Rapidly emerging smart wearable technology and AI, including Machine Learning, Support Vector Machine, and Neural Network approaches, are increasingly commanding greater attention in gait research. Although their use in clinical settings are not yet well leveraged, these tools promise a paradigm shift in stroke gait quantification, as they provide means for acquiring, storing and analyzing multifactorial complex gait data, while capturing its non-linear dynamic variability and offering the invaluable benefits of predictive analytics.

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An expert system feedback tool improves the reliability of clinical gait kinematics for older adults with lower limb osteoarthritis

Cited by 2 publications

References 23 publications

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

A hierarchical cluster analysis to determine whether injured runners exhibit similar kinematic gait patterns

Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis

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