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

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

JOURNAL=Frontiers in Rehabilitation Sciences

VOLUME=2

YEAR=2021

URL=https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2021.741393

DOI=10.3389/fresc.2021.741393

ISSN=2673-6861

ABSTRACT=<p><bold>Background:</bold> The use of wearable sensor technology (e. g., accelerometers) for tracking human physical activity have allowed for measurement of actual activity performance of the upper limb (UL) in daily life. Data extracted from accelerometers can be used to quantify multiple variables measuring different aspects of UL performance in one or both limbs. A limitation is that several variables are needed to understand the complexity of UL performance in daily life.</p><p><bold>Purpose:</bold> To identify categories of UL performance in daily life in adults with and without neurological UL deficits.</p><p><bold>Methods:</bold> This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (<italic>N</italic> = 211), two samples of persons with stroke and one sample from neurologically intact adult controls. Data used in these analyses were UL performance variables calculated from accelerometer data, associated clinical measures, and participant characteristics. A total of twelve cluster solutions (3-, 4-, or 5-clusters based with 12, 9, 7, or 5 input variables) were calculated to systematically evaluate the most parsimonious solution. Quality metrics and principal component analysis of each solution were calculated to arrive at a locally-optimal solution with respect to number of input variables and number of clusters.</p><p><bold>Results:</bold> Across different numbers of input variables, two principal components consistently explained the most variance. Across the models with differing numbers of UL input performance variables, a 5-cluster solution explained the most overall total variance (79%) and had the best model-fit.</p><p><bold>Conclusion:</bold> The present study identified 5 categories of UL performance formed from 5 UL performance variables in cohorts with and without neurological UL deficits. Further validation of both the number of UL performance variables and categories will be required on a larger, more heterogeneous sample. Following validation, these categories may be used as outcomes in UL stroke research and implemented into rehabilitation clinical practice.</p>