Personalizing a computational upper body model improves kinematic tracking in high range-of-motion arm movements

Abstract

Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models can be scaled to represent the size of an individual person, but the kinematics are generic. We introduce a method to personalize the shoulder kinematics of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17-20 mm) and in the dynamic trials (22 mm to 14-17 mm). Only personalization reduces the average scapula marker error (51 mm to 36-38 mm) and scapula axis-angle error (15{degrees} to 10{degrees}) in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37-40 mm, 15{degrees} to 9{degrees}). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.