Sensor-Based Categorization of Upper Limb Performance in Daily Life of Persons With and Without Neurological Upper Limb Deficits

Barth, Jessica; Lohse, Keith R.; Konrad, Jeffrey D.; Bland, Marghuertta D.; Lang, Catherine E.

doi:10.3389/fresc.2021.741393

Cited by 14 publications

(21 citation statements)

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“…This study was a secondary analysis of data collected from a prospective, observational, longitudinal cohort, tracking UL change over time [ 32 ]. Sources of data from two time points were participant characteristics, clinical measures from early after stroke (within two weeks of onset), and subsequent categories of UL performance (from a previous report) [ 14 ] later after stroke.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The dependent variable (outcome or class in machine learning) in this analysis was a category of UL performance established in previous report [ 14 ]. These were derived from UL performance variables quantified via accelerometer data [ 14 ]. Participants in the prospective, longitudinal, observational, cohort wore Actigraph GT9X-Link accelerometers on both wrists at each time point with methods previously described [ 1 ].…”

Section: Methodsmentioning

confidence: 99%

“…Each UL performance variable conveys slightly different information about the collective nature of UL use; multiple variables may provide a fuller understanding of the scope of UL performance in daily life [ 13 ]. As a solution to the multi-variable problem, we recently categorized UL performance in adult cohorts with and without stroke [ 14 ]. The most parsimonious solution was five categories of UL performance formed from five UL performance variables named: (A) Minimal activity/rare integration; (B) Minimal activity/limited integration; (C) Moderate activity/moderate integration; (D) Moderate activity/full integration; (E) High activity/full integration.…”

Section: Introductionmentioning

confidence: 99%

“…The UL performance categories are multi-dimensional, with each category providing information about UL activity with respect to the different movement characteristics in adults with and without neurological UL deficits. Thus, the five categories of UL performance may provide a more complete measure of UL use in daily life [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis

Barth

Lohse

Bland

et al. 2023

J NeuroEngineering Rehabil

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Background Accelerometers allow for direct measurement of upper limb (UL) activity. Recently, multi-dimensional categories of UL performance have been formed to provide a more complete measure of UL use in daily life. Prediction of motor outcomes after stroke have tremendous clinical utility and a next step is to explore what factors might predict someone’s subsequent UL performance category. Purpose To explore how different machine learning techniques can be used to understand how clinical measures and participant demographics captured early after stroke are associated with the subsequent UL performance categories. Methods This study analyzed data from two time points from a previous cohort (n = 54). Data used was participant characteristics and clinical measures from early after stroke and a previously established category of UL performance at a later post stroke time point. Different machine learning techniques (a single decision tree, bagged trees, and random forests) were used to build predictive models with different input variables. Model performance was quantified with the explanatory power (in-sample accuracy), predictive power (out-of-bag estimate of error), and variable importance. Results A total of seven models were built, including one single decision tree, three bagged trees, and three random forests. Measures of UL impairment and capacity were the most important predictors of the subsequent UL performance category, regardless of the machine learning algorithm used. Other non-motor clinical measures emerged as key predictors, while participant demographics predictors (with the exception of age) were generally less important across the models. Models built with the bagging algorithms outperformed the single decision tree for in-sample accuracy (26–30% better classification) but had only modest cross-validation accuracy (48–55% out of bag classification). Conclusions UL clinical measures were the most important predictors of the subsequent UL performance category in this exploratory analysis regardless of the machine learning algorithm used. Interestingly, cognitive and affective measures emerged as important predictors when the number of input variables was expanded. These results reinforce that UL performance, in vivo, is not a simple product of body functions nor the capacity for movement, instead being a complex phenomenon dependent on many physiological and psychological factors. Utilizing machine learning, this exploratory analysis is a productive step toward the prediction of UL performance. Trial registration NA

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%