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Volume 25
Issue 15
10.3390/s25154618
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations

by
Chelsea E. Macpherson
1,
Marghuretta D. Bland
1,2,3,
Christine Gordon
1,
Allison E. Miller
1,
Caitlin Newman
4,
Carey L. Holleran
1,2,
Christopher J. Dy
5,
Lindsay Peterson
6,
Keith R. Lohse
1,2 and
Catherine E. Lang
1,2,3,*
1
Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
2
Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA
3
Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA
4
Shirley Ryan AbilityLab, Chicago, IL 60611, USA
5
Department of Orthopedic Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
6
Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(15), 4618; https://doi.org/10.3390/s25154618
Submission received: 14 June 2025 / Revised: 18 July 2025 / Accepted: 23 July 2025 / Published: 25 July 2025
(This article belongs to the Special Issue Sensor-Based Human Activity Recognition)
Review Reports Versions Notes

Abstract

Background: Wearable movement sensors can measure upper limb (UL) activity, but single variables may not capture the full picture. This study aimed to replicate prior work identifying five multivariate categories of UL activity performance in people with stroke and controls and expand those findings to other UL conditions. Methods: Demographic, self-report, and wearable sensor-based UL activity performance variables were collected from 324 participants (stroke n = 49, multiple sclerosis n = 19, distal UL fracture n = 40, proximal UL pain n = 55, post-breast cancer n = 23, control n = 138). Principal component (PC) analyses (12, 9, 7, or 5 accelerometry input variables) were followed by cluster analyses and numerous assessments of model fit across multiple subsets of the total sample. Results: Two PCs explained 70–90% variance: PC1 (overall UL activity performance) and PC2 (preferred-limb use). A five-variable, five-cluster model was optimal across samples. In comparison to clusters, two PCs and individual accelerometry variables showed higher convergent validity with self-report outcomes of UL activity performance and disability. Conclusions: A five-variable, five-cluster model was replicable and generalizable. Convergent validity data suggest that UL activity performance in daily life may be better conceptualized on a continuum, rather than categorically. These findings highlight a unified, data-driven approach to tracking functional changes across UL conditions and severity of functional deficits.
Keywords: activities of daily living; measurement; musculoskeletal; neurology; rehabilitation; upper limb; wearable sensors activities of daily living; measurement; musculoskeletal; neurology; rehabilitation; upper limb; wearable sensors

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MDPI and ACS Style

Macpherson, C.E.; Bland, M.D.; Gordon, C.; Miller, A.E.; Newman, C.; Holleran, C.L.; Dy, C.J.; Peterson, L.; Lohse, K.R.; Lang, C.E. Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations. Sensors 2025, 25, 4618. https://doi.org/10.3390/s25154618

AMA Style

Macpherson CE, Bland MD, Gordon C, Miller AE, Newman C, Holleran CL, Dy CJ, Peterson L, Lohse KR, Lang CE. Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations. Sensors. 2025; 25(15):4618. https://doi.org/10.3390/s25154618

Chicago/Turabian Style

Macpherson, Chelsea E., Marghuretta D. Bland, Christine Gordon, Allison E. Miller, Caitlin Newman, Carey L. Holleran, Christopher J. Dy, Lindsay Peterson, Keith R. Lohse, and Catherine E. Lang. 2025. "Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations" Sensors 25, no. 15: 4618. https://doi.org/10.3390/s25154618

APA Style

Macpherson, C. E., Bland, M. D., Gordon, C., Miller, A. E., Newman, C., Holleran, C. L., Dy, C. J., Peterson, L., Lohse, K. R., & Lang, C. E. (2025). Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations. Sensors, 25(15), 4618. https://doi.org/10.3390/s25154618

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