Open Access

Rationale: Three-dimensional (3D) motion analysis has proved helpful in the diagnosis of different musculoskeletal syndromes and identifying injurious movement patterns in high string players. Furthermore, an optoelectronic 3D motion capture system allows an accurate and objective assessment of upper body posture and motion during violin and viola performance. However, no reference upper body model of high string players has been proposed as yet. Moreover, a more physiological shoulder model that separates the joints of the shoulder complex has not been reported. Especially in view of given the role of the scapula in the normal movement of the humerus, it cannot be disregarded when evaluating musculoskeletal strain in the shoulder. The International Society of Biomechanics recommends definitions of joint coordinate systems for the report of upper body joint motion using anatomical landmarks as reference for the placement of surface markers. Using markers on the skin for some of the proposed locations is, however, inappropriate when an instrument is being played. There are skin movement artifacts, e. g. caused by the movement of the scapula underneath the skin, whereas some markers interfere with the instrument on the shoulder or might be occluded by the bowing arm in motion. Purpose: The aim of this study was to develop a marker-based method for quantifying 3D upper body kinematics of high string players and to demonstrate its clinical feasibility in violin and viola performance. The method is intended to provide an objective evaluation of high string players’ motor strategies, especially in the shoulder complex, while minimizing skin movement artifacts, marker occlusions and limitations in instrument placement. Methods: A custom marker set was developed consisting of thirty-one single markers to define the anatomical coordinate systems of sixteen upper body segments including the pelvis, thorax, spine and head, as well as both scapulae, upper arms, forearms and hands. Twenty-one of these markers as well as two pre-built and four custom-made rigid marker clusters were used for tracking the segment motions. Twelve professional violinists without history of musculoskeletal or neurological problems were recruited for assessing the clinical feasibility of the method. They were asked to perform a single sequence of two consecutive musical notes on each of two adjacent strings (G- and D-string) in real time, played at 50 bpm with tempo audibly regulated by a metronome, and using a standardized violin and bow. The participants played up- and down-bow alternately using the whole length of the bow. A custom biomechanical model was applied to the motion capture data and the rotation angles of fifteen joints were calculated. The location of each glenohumeral joint rotation center was computed by upper arm movements with respect to the scapula based on a functional method. For a description of the motion patterns, minimum, maximum and range of angular motion were averaged across participants for each string and rotation. Inter-subject variability was assessed by calculating the standard deviation (SD) at each sample of the angle-time series between participants for each rotation and for both strings. Then SD was averaged over sequences for each rotation and string. For comparing mean rotation angles between strings over time, random effect models were used. Results: The highest range of motion was observed in the right elbow flexion and right wrist flexion/extension. Also, high ranges of motion (> 10°) were found in all right glenohumeral rotations and right wrist deviation and pronation/supination. In conclusion, lumbar and thoracic spine, thorax, neck, and left upper limb were quite static, while large motion occurred in the right upper limb during up and down bowing. Most rotation angles showed a reasonable inter-subject variability except for left and right glenohumeral plane of elevation as well as left glenohumeral internal/external rotation, and left and right wrist pronation/supination (> 10°). Significant differences in the rotation angles between G- and D-string bowing were detected especially in the left wrist and right shoulder joints. Conclusions: This is the first study that used quantitative 3D analysis to explore the upper body kinematics of high string players during performance, providing a detailed view of the motor control in the shoulder as well as in the lumbar and thoracic spine. The biggest advantage over previously published methods is the more physiological shoulder and spine models while providing a simple application. The method was found to give consistent motion patterns across participants and to be sensitive to differences between adjacent strings. Although the method appears to be valid, more rigorous validation is necessary. Since there is no gold standard with which we could compare results, we were only able to assess the clinical feasibility. We believe that our method represents a good compromise between accuracy and practicability for clinical application. Due to the inclusion of multi-segmented shoulder and spine models, it will improve understanding of the motor strategies adopted by high string players and may contribute to injury prevention, diagnosis and treatment.