Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter λ influences data smoothness and fitting, which is governed by selecting automatic methods, namely, cross-validation (CV) and generalized cross-validation (GCV) or subjective assessment. However, previous biomechanics research has only applied subjective assessment in choosing optimal λ without using any automatic methods beforehand. None of that research demonstrated how the subjective assessment was made. Thus, the goal of this research was to apply the FDA method to smoothing and differentiating kinematic data, specifically right hip flexion/extension (F/E) angle during the American kettlebell swing (AKS) and determine the optimal λ . CV and GCV were applied prior to the subjective assessment with various values of λ together with cubic and quintic spline (B-spline) bases using the FDA approach. The selection of optimal λ was based on smoothed and well-fitted first and second derivatives. The chosen optimal λ was 1 × 10 − 12 with a quintic spline (B-spline) basis and penalized fourth-order derivative. Quintic spline is a better smoothing and differentiation method compared to cubic spline, as it does not produce zero acceleration at endpoints. CV and GCV did not give optimal λ , forcing subjective assessment to be employed instead.

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Abstract. This study aimed to optimize muscle stress forces which are capable of doing the work during.the archery activity. The developed upper limb model of a body comprised of 12 muscles and six joints of arm segments and the upper trunk. Optimization method using the Lagrange multiplier has been used to obtain the muscle stress during the performance of archery. The involved objective functions are non-linear functions of quadratic and cubic. It has been found that the muscle stress forces which are obtained in the release phase of the draw arm (part E) and bow arm (part H), of an amature are potential in reducing the injuries at the shoulder joint.

The purpose of this study was to investigate muscles activity during archery by carrying out an electromyography (EMG) experiment towards 12 muscles and six joints involving two types of subject (skilled and recreational). EMG is used to detect muscle signals during any particular activity. There were two types of data recorded which were maximum voluntary contraction (MVC) and archery activity. The skilled archer was found to produce 280 N of biceps brachii, 213.9 N of the deltoid, 123.4 N of trapezius forces compare to that of the recreational archer with 371.1 N, 164.9 N and 163.8 N, respectively for the draw arm during drawing phase. It is concluded that the recreational archer tends to a muscle fatigue phenomenon thus may contribute to possible serious injuries.

This study investigated the behaviour of archer's muscle in six phases of archery cycle known as stance (S), knocking arrow (NA), pre-drew (PD), full draw (FD), release (R) and follow through (FT). Firstly, muscle data at bow arm and draw arm are collected from a world class archer that performed archery with the 45lb traditional composite bow with Khatrah technique. The data are collected by using Electromyography (EMG) sensor. The sensor is placed on ten significant muscles. In order to standardise the time frame of data and suppress noise, the extracted data is normalised and smoothed respectively. Further, muscle forces are gauged and compared in each phase. Result obtained shows that high muscle force at upper and lower arm acquired at bow arm, while trunk body part for draw arm. Furthermore, using Khatrah technique, the most affected muscle is flexor carpi radialis at the bow arm. This study provides the foundation for the development of system to monitor the muscle condition of archers.