Biomechanics Measurements in Archery

Ahmad, Zair Asrar; Taha, Zahari; Hassan, Hasnun Arif Hassan; Hisham, Mohd Azrul; Johari, Nasrul Hadi; Kadirgama, K.

doi:10.15282/jmes.6.2014.4.0074

Cited by 35 publications

(34 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Muscles forces may not be linear. Therefore, quadratic ( 2) n and cubic functions ( 3) n are used to represent a model with 12 muscles representing the upper body which consists of forearms, upper arms and shoulder as been illustrated in Figure (1 ( 2 ) 030030-3 Similarly calculation for cubic function was done by following the same steps. The signs of muscle forces depend on the sign of Lagrange multiplier coefficient.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of upper extremity muscles using compound bow via Lagrange multiplier method (LMM)

Ariffin

Rambely²

2016

AIP Conference Proceedings

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…There are many studies on archery sport [2][3][4][5][6][7][8][9][10][11]. However, these studies focused on targeting accuracy, muscle fatigue, and joint injuries along with the measures to overcome it.…”

Section: Introductionmentioning

confidence: 99%

Optimization of upper extremity muscles using compound bow via Lagrange multiplier method (LMM)

Ariffin

Rambely²

2016

AIP Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…It is widely used in the clinical (Hawkes et al, 2015), rehabilitation (Elamvazuthi et al, 2015), ergonomics (Jia & Nussbaum, 2016) and sports applications (Taha et al, 2017;Wang, Hong, & Li, 2014). Most of the sEMG used in the previous research are from commercial sensor systems such as Shimmer (Ahmad et al, 2014;Taha et al, 2017), Step 32 (Di Nardo et al, 2016, and Noraxon TeleMyo (Noraxon USA Inc., Scottsdale, USA) (Sterzing, Frommhold, & Rosenbaum, 2016). Nevertheless, this sEMG device is possible to be developed to allow for customization of the algorithm used (Ganesan, Gobee, & Durairajah, 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of wearable electromyogram for the physical fatigue detection during aerobic activity

Ahmad

Omar

2018

MoHE

Self Cite

View full text Add to dashboard Cite

Physical fatigue or muscle fatigue is a common problem that affects people who are vigorously involved in activities that require endurance movements. It becomes more complicated to measure the fatigue level when the dynamic motion of the activity is included. Therefore, this paper aims to develop a wearable device that can be used for monitoring physical fatigue condition during aerobic exercise. A 10-bit analog to digital converter (ADC) micro-controller board was used to process the data sensed by Ag/AgCl electrodes and real-time transmitted to the computer through Bluetooth's technology. The wearable was attached to the knee and connected to the biopotential electrodes for sensing the muscle movement and convert it into the electrical signal. The signal then processed by using the fourth-order Butterworth filter to filter the low-pass filter frequency and eliminate the noise signal. The results reveal that the fatigue level increased gradually based on the rating of perceived exertion (RPE), using 10-point Borg's scale, which is rated by the subject’s feeling. Both muscle's activities in lower limb rise as speed is increased, and it was also observed that the rectus femoris is functioning more than gastrocnemius due to the size of muscle fiber. Furthermore, it was established that the maximum volumetric contraction (MVC) could be used as a reference and indicator for measuring the percentage of contraction in pre-fatigue but not to fatigue induced experiment. However, this wearable device for EMG is promising to measure the muscle signal in the dynamic motion of movement. Consequently, this device is beneficial for a coach to monitor their athlete's level of exhaustion to be not over-exercise, which also can prevent severe injury.

show abstract

“…The correlation between EMG signals and muscle force has been reviewed during isometric contractions and dynamic contractions. Many methods and approaches to modelling for computing the muscle's force have been suggested, but these models could not be validated due to the lack of an accurate and efficient experimental procedure to compute the muscle's force [4][5][6]. Recently, most muscle coordination is reviewed from the surface EMG activity.…”

Section: Introductionmentioning

confidence: 99%

Electromyography Signal on Biceps Muscle in Time Domain Analysis

et al. 2014

View full text Add to dashboard Cite

Features extraction is important for electromyography (EMG) signal analysis. The paper's objective is to evaluate the features extraction of the EMG signal. The experimental setup for EMG signal acquisition followed the procedures recommended by Europe's Surface Electromyography for Non-invasive Assessment of Muscle (SENIAM) project. The EMG signal's data were analysed in the time domain to get the features. Four features were considered based on the analysis, which are IEMG, MAV, VAR and RMS. The average muscle force condition can be estimated by correlation between the EMG voltage amplitude with linear estimation with the full-wave rectification method. The R-squared value determined the correlation between the EMG voltage amplitude with the loads. IEMG was chosen as the reference feature for estimation of the muscle's force due to its R-squared value equal to 0.997. By referring to the IEMG, the linear equation obtained from the correlation was used for estimation of the muscle's force. These findings can be integrated to design a muscle force model based on the biceps muscle.

show abstract

Biomechanics Measurements in Archery

Cited by 35 publications

References 7 publications

Optimization of upper extremity muscles using compound bow via Lagrange multiplier method (LMM)

Optimization of upper extremity muscles using compound bow via Lagrange multiplier method (LMM)

Development of wearable electromyogram for the physical fatigue detection during aerobic activity

Electromyography Signal on Biceps Muscle in Time Domain Analysis

Contact Info

Product

Resources

About