Power enhancement of weightlifters during snatch through Reducing torque on joints by particle swarm optimization

American Journal of Applied Sciences, Dec, 2008 by Firooz Salaami, Nima Jamshidi, Mostafa Rostami, Siamak Najarian

INTRODUCTION

Study of athletic movement from biomechanical view is one of the important research fields. Majority of research in this field is for recognition and optimization of movement. The recognition requires a true definition of a dynamic model of athlete's body with respect to biomechanical limitations. In order to find out the optimized athletic movement, various optimization methods have been applied. In this research, a new application of artificial intelligence in the scope of sport biomechanics has been introduced which has a wide application for optimization of athlete's movement in various sports. The findings of this research significantly improve the recognition and optimization of athlete's movement and prompt the rank of athlete's in global championships. Previous studies on the dynamic model of weightlifting are being briefly mentioned in the following part. A mathematical model on the sagittal plane was defined by five links. Through received information from camera and Newton-Euler formula, the values of force and torque were determined during snatch movement in the tension phase (1). The bar trajectory and joint angles of athletes with different anthropometric factors were analyzed (2). A new procedure for calculating the power production during Olympic lifting movements was developed (3). Data obtained from 16-mm film of weightlifters were analyzed to study energy changes during body segment and barbell movements, energy transfer to the barbell and energy transfer between segments during the lifting movements contested. The results provided a detailed understanding of the magnitude and temporal input of energy from dominant muscle groups during a lift (4). A multi segment model of the lifters' movement in the sagittal plane was developed by using equations of motion, force and moments from applied data film. Analysis was limited to body segment orientations, vertical bar accelerations, vertical joint reaction forces, segmental angular accelerations, horizontal moment arms of the bar to selected joints and inter-segmental resultant moments. Maximum vertical bar acceleration and angular acceleration of the trunk tended to occur near lift-off in the skilled lifters. Within each subject, the hip joint experienced the greatest torque because of the relatively large horizontal moment arm of the bar to this joint (5). Escamilla conducted a research to quantify biomechanical parameters employing two-dimensional and three-dimensional analyses while performing the squat with varying stance widths (6). Ankle plantar flexor (10-51 N.m), knee extensor (359-573 N.m) and hip extensor (275-577 N.m) net muscle moments were generated for the narrow stance squat, whereas ankle dorsiflexor (34-284 N.m), knee extensor (447-756 N.m) and hip extensor (382-628 N.m) net muscle moments were generated for the medium stance squat and wide stance squat. Later, the snatch movements of two female athletes were examined in junior world weightlifting competition by means of camera techniques and through motion analysis software, speed versus time and bar trajectory were determined (7).

In previous research studies, we minimized the generated torque during snatch movement by neural networks and fuzzy logic (8), (9). In majority of pervious works, the focus was on studying and modeling of movement in order to recognize and calculate kinematic parameters of weightlifter but there was less consideration given to power enhancement of weightlifter. It seems that the work on power enhancement can be divided in two branches: the first one focused on optimizing the effect of body parameters and sport facilities like weightlifters belt (10-14) and the second focused on the technique modification of weightlifter through mathematical modeling and various optimization methods (8), (9), (15-19).

There are two approaches for optimization. The first approach is artificial intelligent. The other one is the optimal control and classical numerical method. Application of neural networks in the optimization of sport technique is a new discussion. The finding of this research will help the trainers to improve the technique of Olympic weightlifter.

MATERIALS AND METHODS

Mechanical modeling of body: Until now, various dynamic models with different inputs and outputs for simulation of human movement have been developed. Since internal forces and torques in joints could not be measured directly in a biological system, the kinematical and anthropometric parameters have been used because they were calculated indirectly. The mass of body segments have been determined through the Zatsirosky approach (20). In order to estimate the length of each segment, Muftic et al. formulattions have been used (21). The mass calculation table of each segment have been computed based on Zatsirosky formulation in Matlab 7.0 in which the input variables are mass and height of the athlete. In order to estimate the moment of inertia around the center of mass, Chaffin et al. formula has been used (22). where, M is mass of segment and L is the length of the segment. The results of computation are shown in Table 1. The athlete of this research has 80 (kg) weight and 171 (cm) height. A 90 (kg) weight was lifted by the athlete.