Modeling investigation of learning a fast elbow flexion in the horizontal plane—prediction of muscle forces and motor units action R. T. RAIKOVA†*, D. A. GABRIEL‡ and H. TS. ALADJOV† †Centre of Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Block 105, 1113 Sofia, Bulgaria ‡Department of Physical Education and Kinesiology, Brock University, St. Catharines, Ont., Canada, L2S 3A1 (Received 20 July 2005; in final form 8 May 2006) Experimental investigation of practicing a dynamic, goal-directed movement reveals significant changes in kinematics. Modeling can provide insight into the alterations in muscle activity, associated with the kinematic adaptations, and reveal the potential motor unit (MU) firing patterns that underlie those changes. In this paper, a previously developed muscle model and software (Raikova and Aladjov, Journal of Biomechanics, 35, 2002) have been used to investigate changes in MU control, while practicing fast elbow flexion to a target in the horizontal plane. The first trial (before practice) and the last trial (after extensive practice) of two subjects have been simulated. The inputs for the simulation were the calculated external moments at the elbow joint. The external moments were countered by the action of three flexor muscles and two extensor ones. The muscles have been modeled as a mixture of MUs of different types. The software has chosen the MU firing times necessary to accomplish the movement. The muscle forces and MUs firing statistics were then calculated. Three hypotheses were tested and confirmed: (1) peak muscle forces and antagonist co-contraction increase during training; (2) there is an increase in the firing frequency and the synchronization between MUs; and (3) the recruitment of fast-twitch MUs dominates the action. Keywords: Fast elbow flexion; Learning; Muscle force; Motor units; Antagonistic co-contraction 1. Introduction The demands of the task are a primary consideration while learning to perform a discrete movement; it may require an increased speed, strength, or precision. The training methods used to make improvements in these criteria are important issues in the area of motor behavior for sport and rehabilitation. Training performed to increase the speed of limb movement can significantly alter the observed kinematics (Gabriel 2002). The measured angular accelerations can then serve to calculate the joint moments, and the individual muscle forces can be computed by applying different optimization techniques for solving the indeterminate problem (Raikova and Prilutsky 2001). Musculoskeletal modeling and surface electromyographic (SEMG) evidence suggest that antag- onist co-activation plays a significant role during maximal effort tasks. Training to improve maximal effort performance can either increase or decrease the antagonist co-contraction, depending on the objectives (strength or speed) and the instructions (for example more accuracy) given to the subject (Gottlieb et al. 1990, Bernardi et al. 1996, Patten and Kamen 2000, Croce and Miller 2003, Gribble et al. 2003). The SEMG signals and/or predicted muscle forces can provide information about any alterations in motor coordination during the training process. However, this is insufficient for investigating the adaptations in the underlying motor units (MUs) firing patterns. This is important because MU firing patterns are the basic mechanism by which the nervous system controls the generation of skeletal muscle force. Unfortunately, the activity of individual MUs is concealed by the interference pattern recorded from the skin surface. Additionally, SEMG recordings do not reflect under- lying differences in muscle fibre composition (Taylor et al. 1997). Indwelling recordings can be used to monitor MUs activity directly but only a small number can be studied due to the limited pick-up volume of needle or wire electrodes. The resulting data do not Computer Methods in Biomechanics and Biomedical Engineering ISSN 1025-5842 print/ISSN 1476-8259 online q 2006 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/10255840600795413 *Corresponding author. Email: rosi.raikova@clbme.bas.bg Computer Methods in Biomechanics and Biomedical Engineering, Vol. 9, No. 4, August 2006, 211–219