ArticleFrontiers in rehabilitation sciences2021
Sensor-based categorization of upper limb performance in daily life of persons with and without neurological upper limb deficits.
Article in Frontiers in rehabilitation sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- From Metrics to Meaning in Neurological Rehabilitation: Clinicians' Perspectives on Digital Metrics of Upper Limb Functioning-A Focus Group Study.JMIR rehabilitation and assistive technologies · 2026Article
- Quantifying Upper Limb Movement During Naturalistic Driving: A Clinically Informed Ecological Approach.Sensors (Basel, Switzerland) · 2026Article
- Advancing Wrist-Worn Accelerometry for Measurement of Infant Motor Behavior.Developmental psychobiology · 2025Article
- Replication of Sensor-Based Categorization of Upper-Limb Performance in Daily Life in People Post Stroke and Generalizability to Other Populations.Sensors (Basel, Switzerland) · 2025Article
- Article
- The Feasibility and User Experience of a Program of Progressive Cued Activity to Promote Functional Upper Limb Activity in the Inpatient Rehabilitation Setting with Follow-Up at Home.Applied sciences (Basel, Switzerland) · 2025Article
- Factors associated with upper extremity use after stroke: a scoping review of accelerometry studies.Journal of neuroengineering and rehabilitation · 2025Article
- Trends and Innovations in Wearable Technology for Motor Rehabilitation, Prediction, and Monitoring: A Comprehensive Review.Sensors (Basel, Switzerland) · 2024Review
- Quantifying the effects of sleep on sensor-derived variables from upper limb accelerometry in people with and without upper limb impairment.Journal of neuroengineering and rehabilitation · 2024Article
- Wearable Technology to Capture Arm Use of People With Stroke in Home and Community Settings: Feasibility and Early Insights on Motor Performance.Physical therapy · 2024Article
- Referent data for investigations of upper limb accelerometry: harmonized data from three cohorts of typically-developing children.Frontiers in pediatrics · 2024Article
- Associations Between Coordination and Wearable Sensor Variables Vary by Recording Context but Not Assessment Type.Journal of motor behavior · 2024Article
- Predicting later categories of upper limb activity from earlier clinical assessments following stroke: an exploratory analysis.Journal of neuroengineering and rehabilitation · 2023Article
- Computer-assisted approaches for measuring, segmenting, and analyzing functional upper extremity movement: a narrative review of the current state, limitations, and future directions.Frontiers in rehabilitation sciences · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Background: 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. Purpose: To identify categories of UL performance in daily life in adults with and without neurological UL deficits. Methods: This study analyzed data extracted from bimanual, wrist-worn triaxial accelerometers from adults from three previous cohorts (N=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. Results: 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. Conclusion: 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.
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