Motor variability (MV) refers to the intrinsic variability naturally present in the motor control system. Occurring even in the simplest movements, it is usually manifested as a difference in joint movements, joint coordination and/or muscle activities between successive repeats of a task which are identical in performance. Contrary to the traditional view that MV is detrimental to performance, it is now widely accepted that MV may actually have an important functional role in skill acquisition, and that skilled performance may, actually, be associated with increased MV. Further, MV is related to pain and fatigue, and may play a decisive role in rehabilitation (reviewed in Srinivasan & Mathiassen 2012). Hypothetically, individuals with a larger MV would be better protected against overuse injuries, and recover faster after disorders affecting motor performance. However, whether the extent of MV is, indeed, a consistent individual trait across different tasks is not known.    

The purpose of this study was to let individuals perform a laboratory-based simulation of repetitive upper-extremity precision work and determine:

(i)             Whether it is possible to systematically classify individuals according to the size of their MV in repetitive work;

(ii)            Whether classification of individuals in one working condition on one day persists even when some work-factors are slightly changed, and between different days when they perform the same work.

Repetitive pipetting with a cycle time of 2.8s was performed in the laboratory by a group of 14 healthy female subjects, aged 20-45 years, right-handed and experienced in pipetting, on 3 different days under identical protocol and experimental conditions. Work factors such as work-pace, precision and cognitive load (on top of the pipetting work) were manipulated within each day. Kinematic data were obtained using electromagnetic motion capture systems (FASTRAK).

MV in shoulder elevation, elbow flexion and shoulder-elbow coordination were operationalized using cycle-to-cycle standard deviations of motor parameters such as peak velocities, time lag of peak velocities, phase angle and inter-segmental phase angle. The resulting traits in individuals and the consistency of those traits across tasks and days will be presented.