Complex hand dexterity is fundamental to our interactions with the physical, social and cultural environment. Dexterity can be an expression of creativity and precision in a range of activities, including musical performance. Little is understood about complex hand dexterity or how virtuoso expertise is acquired, due to the versatility of movement combinations available to complete any given task. This has historically limited progress of the field because of difficulties in measuring movements of the hand. Recent developments in methods of motion capture and analysis mean it is now possible to explore the intricate movements of the hand and fingers. These methods allow us insights into the neurophysiological mechanisms underpinning complex hand dexterity and motor learning. They also allow investigation into the key factors that contribute to injury, recovery and functional compensation.The application of such analytical techniques within musical performance provides a multidisciplinary framework for purposeful investigation into the process of learning and skill acquisition in instrumental performance. These highly skilled manual and cognitive tasks present the ultimate achievement in complex hand dexterity. This paper will review methods of assessing instrumental performance in music, focusing specifically on biomechanical measurement and the associated technical challenges faced when measuring highly dexterous activities.

Dynamic finger joint motion is difficult to measure using optical motion analysis techniques due to the limited surface area allowed for adequate marker placement. This paper describes an extension of a previously validated kinematic measurement technique using a reduced surface marker set and outlines the required calculations based on a specific surface marker placement to calculate flexion/extension and hyperextension of the metacarpophalangeal, proximal interphalangeal, and distal interphalangeal joints. The modified technique has been assessed for accuracy using a series of static reference frames (absolute residual error = ±3.7°, cross correlation between new method and reference frames; r = 0.99). The method was then applied to a small group of participants with rheumatoid arthritis (seven females, one male; mean age = 62.8 years ± 12.04) and illustrated congruent strategies of movement for a participant and a large range of finger joint movement over the sample (5.8-71.1°, smallest to largest active range of motion). This method used alongside the previous paper provides a comprehensive, validated method for calculating 3-D wrist, hand, fingers, and thumb kinematics to date and provides a valuable measurement tool for clinical research.

Fifteen percent of the world’s population has a disability, over a billion people, and 80% live in lower and middle income countries (LMICs) [1]. The Global Burden of Disease study 2019 indicates the international prevalence of amputation at 176 million, which has increased by over 50% since 1990 [2]. The prevalence of major upper- and […]