Influence of upper limb movement patterns on accelerometer measurements: a pediatric case series

Previous studies showed success using wrist-worn accelerometers to monitor upper-limb activity in adults and children with hemiparesis. However, a knowledge gap exists regarding which specific joint movements are reflected in accelerometry readings. We conducted a case series intended to enrich data interpretation by characterizing the influence of different pediatric upper-limb movements on accelerometry data.

The study recruited six typically developing children and five children with hemiparetic cerebral palsy. The participants performed unilateral and bilateral activities, and their upper limb movements were measured with wrist-worn accelerometers and the Microsoft Kinect, a markerless motion-capture system that tracks skeletal data. The Kinect data were used to quantify specific upper limb movements through joint angle calculations (trunk, shoulder, elbow and wrist). Correlation coefficients (r) were calculated to quantify the influence of individual joint movements on accelerometry data. Regression analyses were performed to examine multi-joint patterns and explain variability across different activities and participants.

Single-joint correlation results suggest that pediatric wrist-worn accelerometry data are not biased to particular individual joint movements. Rather, the accelerometry data could best be explained by the movements of the joints with the most functional relevance to the performed activity.

This case series provides deeper insight into the interpretation of wrist-worn accelerometry data, and supports the use of this tool in quantifying functional upper-limb movements in pediatric populations.