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Updated: Jun 30, 2025
A Method for Quantifying Upper Limb Performance in Daily Life Using Accelerometers
Published on: April 21, 2017
Referent data for investigations of upper limb accelerometry: harmonized data from three cohorts of
Catherine E Lang1,2,3, Catherine R Hoyt2, Jeffrey D Konrad1
1Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO, United States.
Insights
Wearable sensors offer insights into pediatric motor behavior. This study provides referent accelerometer data for 25 upper limb movement variables in typically developing children aged 3-17, aiding cross-study comparisons.
Area of Science:
- Pediatric motor behavior analysis
- Wearable sensing technology
- Biomedical data science
Background:
- Pediatric wearable sensing research lacks standardization in sensor use, placement, and data analysis.
- Inconsistent methodologies hinder cross-study comparisons and clinical application.
- There is a need for referent data in pediatric motor behavior research.
Purpose of the Study:
- To provide referent upper limb wearable sensor data from accelerometers.
- To quantify 25 movement variables in typically developing children aged 3-17 years.
- To establish a baseline for analyzing pediatric motor behavior using wearable sensors.
Main Methods:
- Secondary analysis of data from 222 children (ages 3-17) across three pediatric cohorts.
- Bilateral wrist accelerometers worn for 2-4 days, totaling 622 recording days.
- Reprocessing accelerometer data to compute 25 variables of upper limb movement (duration, intensity, symmetry, complexity).
Main Results:
- Most movement variables showed similarity between dominant and non-dominant sides.
- Variables slightly declined with age and showed no significant gender differences.
- Intraclass Correlation Coefficient (ICC) values were moderate to excellent, with within-individual variation ranging from 3% to 32%.
Conclusions:
- The provided referent data are valuable for researchers designing studies in pediatric populations, especially neurodevelopmental and rare disease groups.
- Standardized data interpretation is facilitated by this comprehensive dataset.
- A web-based tool is available for interactive data exploration.
Aim:
The rise of wearable sensing technology shows promise for addressing the challenges of measuring motor behavior in pediatric populations. The current pediatric wearable sensing literature is highly variable with respect to the number of sensors used, sensor placement, wearing time, and how data extracted from the sensors are analyzed. Many studies derive conceptually similar variables via different calculation methods, making it hard to compare across studies and clinical populations. In hopes of moving the field forward, this report provides referent upper limb wearable sensor data from accelerometers on 25 variables in typically-developing children, ages 3-17 years.
Methods:
This is a secondary analysis of data from three pediatric cohorts of children 3-17 years of age. Participants (n = 222) in the cohorts wore bilateral wrist accelerometers for 2-4 days for a total of 622 recording days. Accelerometer data were reprocessed to compute 25 variables that quantified upper limb movement duration, intensity, symmetry, and complexity. Analyses examined the influence of hand dominance, age, gender, reliability, day-to-day stability, and the relationships between variables.
Results:
The majority of variables were similar on the dominant and non-dominant sides, declined slightly with age, and were not different between boys and girls. ICC values were moderate to excellent. Variation within individuals across days generally ranged from 3% to 32%. A web-based R shiny object is available for data viewing.
Interpretation:
With the use of wearable movement sensors increasing rapidly, these data provide key, referent information for researchers as they design studies, and analyze and interpret data from neurodevelopmental and other pediatric clinical populations. These data may be of particularly high value for pediatric rare diseases.
