Abstract
Hammer-throw has a long-standing history in track and field, but unlike some other sports events, men’s hammer throw has not seen a new world record since 1986. One of the possible reasons for this stagnation could be the lack of real-time biomechanical feedback training. In this study, we proposed to establish scientifically described training targets and routes, which in turn required tools that could measure and quantify characteristics of an effective hammer-throw. Towards this goal, we have developed a real-time biomechanical feedback device—a wireless sensor system—to help the training of hammer-throw. The system includes two sensors—an infrared proximity sensor for tracing the hip vertical movement and a load cell for recording the wire tension during a hammer-throw. The system uses XBees for data transmission and an Arduino processor for data processing and system control. The results revealed that the wire tension measurement could supply sufficient key features for coaches to analyze hammer-throw and give real-time feedback for improving training efficiency.
Highlights
Effective human motor skill learning/training benefits athletes but can promote more active lifestyles in the general population (Chen and Ennis 2004; Li et al 2016; Wan and Shan 2016)
The reasons for the current situation could be the following points: (1) effective biomechanical feedback should relate to the invisible forces controlling the limb movement of human motor skills (Shan and Westerhoff 2005), and (2) motor skill learning and optimization must be tailored to an individual body structure and the activity being examined
Through a pilot study (Fig. 1), we found that wire tension and vertical hip displacement measurements might be sufficient to substitute 3D motion capture when analyzing the hammer throw
Summary
Effective human motor skill learning/training benefits athletes but can promote more active lifestyles in the general population (Chen and Ennis 2004; Li et al 2016; Wan and Shan 2016). Previous studies have shown that, when properly understood and applied, biofeedback can strongly enhance the practice of human motor skills (Shan et al 2004; Visentin et al 2008). There are three types of biofeedback: physiological (e.g. heart rate), neurological (e.g. EEG/brain-wave), and biomechanical (e.g. joint angles and force applied) (Tate and Milner 2010). The reasons for the current situation could be the following points: (1) effective biomechanical feedback should relate to the invisible forces (i.e. we can only feel the effect of a force, but cannot see it; the only way for its visualization/quantification is through a force measurement device, such as a scale) controlling the limb movement of human motor skills (Shan and Westerhoff 2005), and (2) motor skill learning and optimization must be tailored to an individual body structure and the activity being examined
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