Wan Zuki Azman Wan Muhamad
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Preferred name
Wan Zuki Azman Wan Muhamad
Official Name
Wan Zuki Azman, Wan Muhamad
Alternative Name
Azman, Wan Zuki
Wan Ahmad, Wan Zuki Azman
Muhamad, Wan Zuki Azmanwan
Main Affiliation
Scopus Author ID
55860800560
Researcher ID
R-4128-2019

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  • Publication
    Effects of Running Surface Stiffness on Three-Segment Foot Kinematics Responses with Different Shod Conditions
    ( 2021-01-01)
    Noor Arifah Azwani Abdul Yamin
    ;
    Khairul Salleh Basaruddin
    ;
    Salleh A.F.
    ;
    Mohammad Shahril Salim
    ;
    Wan Zuki Azman Wan Muhamad
    Objective. The aim of this study was to investigate the effects of surface stiffness on multisegment foot kinematics and temporal parameters during running. Methods. Eighteen male subjects ran on three different surfaces (i.e., concrete, artificial grass, and rubber) in both heeled running shoes (HS) and minimal running shoes (MS). Both these shoes had dissimilar sole profiles. The heeled shoes had a higher sole at the heel, a thick base, and arch support, whereas the minimal shoes had a flat base sole. Indeed, the studied biomechanical parameters responded differently in the different footwear during running. Subjects ran in recreational mode speed while 3D foot kinematics (i.e., joint rotation and peak medial longitudinal arch (MLA) angle) were determined using a motion capture system (Qualysis, Gothenburg, Sweden). Information on stance time and plantar fascia strain (PFS) was also collected. Results. Running on different surface stiffness was found to significantly affect the peak MLA angles and stance times for both HS and MS conditions. However, the results showed that the joint rotation angles were not sensitive to surface stiffness. Also, PFS showed no relationship with surface stiffness, as the results were varied as the surface stiffness was changed. Conclusion. The surface stiffness significantly contributed towards the effects of peak MLA angle and stance time. These findings may enhance the understanding of biomechanical responses on various running surfaces stiffness in different shoe conditions.
  • Publication
    Fibonacci retracement pattern recognition for forecasting foreign exchange market
    ( 2020-01-01)
    Mohd Fauzi Ramli
    ;
    Ahmad Kadri Junoh
    ;
    Mahyun Ab Wahab
    ;
    Wan Zuki Azman Wan Muhamad
    Fibonacci retracement implicates a forecast of future movements in foreign exchange rates (forex) of the previous movement inductive analysis. Fibonacci ratios are used to forecast the retracements level of 0.382, 0.500 and 0.618 and to determine the current trend which provide the mathematical foundation for the Elliott wave theory. K-nearest neighbour (KNN) and linear discriminant analysis (LDA) algorithm are the pattern recognition method for nonlinear feature mining of Elliott wave patterns. Results show that LDA is better than KNN in terms of classification accuracy data which are 99.43%. Among of three levels of Fibonacci retracement results, the 38.2% shows the best forecasting for Great Britain Pound pair to US Dollar currency as major pair by using mean absolute error (MAE), root mean square error (RMSE) and pearson correlation coefficient (r) as the statistical measurements which are 0.001884, 0.000019 and 0.992253 for uptrend and 0.001685, 0.000019 and 0.998806 for downtrend.
  • Publication
    Effect of Arm Swing Direction on Forward and Backward Jump Performance Based on Biomechanical Analysis
    ( 2021-11-25)
    Teoh L.M.
    ;
    Hamzah Sakeran
    ;
    Ahmad Faizal Salleh
    ;
    Mohammad Shahril Salim
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Shapie M.A.M.
    Previous studies have examined the role of arm swing for various types of jumping technique, but none have been found to study about the gender differences in term of the role of arm swing on forward and backward jump. This study aimed to compare the jumping performance between male and female for forward and backward jump. Seven male and seven female subjects performed four trials of forward and backward jump with (FJA, BJA) and without arm swing (FJ, BJ) respectively. Qualisys Track Manager System, EEGO Sports, Visual3D and MATLAB software was used to record and analyze the performance. According to the result, the triceps brachii muscle is the most active muscle compared to other muscles during jumping. The normalized vGRF showed significant correlation with jump height when jumping forward and backward (p<0.01). The arm swing enhanced the jumping performance by increasing the jump height. Males demonstrated greater vGRF and jump height than females. When jump with arm swing, the left knee flexion angle of males increased whereas females decreased. These findings concluded there is different between males and females during jumping.
  • Publication
    Statistical analysis in clinical gait analysis using Kinovea between normal and simulated abnormal gaits
    ( 2023-02-21)
    Rahim S.A.
    ;
    Hamzah Sakeran
    ;
    Ahmad Faizal Salleh
    ;
    Mohammad Shahril Salim
    ;
    Wan Zuki Azman Wan Muhamad
    ;
    Shapie M.A.M.
    Kinematic analysis of human gait is an effective strategy to detect and assess an individual's gait to diagnose and develop and guide follow-on rehabilitation protocols. So, an accurate, objective gait analysis system has potential to facilitate rehabilitation process. System using smartphone-Kinovea represent an emerging technology for physical activity assessment and that may be relevant for gait analysis. The objective of this study was to determine gait displacement, speed and joint angle by using smartphone-Kinovea software system - to compare the normal gait with four distinct simulated gait abnormalities. Also, to assess validity of the proposed system by compared with QTM as gold standard. 30 participants completed an experiment in which they completed several gait trails on single day. Gait types were analyzed using statistical analysis (two-way MANOVA). As for validation assessment was analyzed using paired t-test by comparing proposed system with QTM. Results shows that joint angles for abnormal gaits are higher mean (Standard Deviation) compared to normal gait during HS and TO. While, normal gait exhibits higher mean (Standard Deviation) for d and s during both IDS and TDS phases compared to other four abnormal gaits in both genders. Also, there are significant different (p<0.05) of gait for all gait comparisons for all parameters, except hip angle of normal-HP with p=0.495. Moreover, there is some gait was similar with other gait due to they shared underlying kinematic aspects such as BA and DP. The validation of the system gives moderate result. These support that the smartphone-Kinovea system have potential in detecting and identifying abnormal gaits, and for future implementation in diagnosis and rehabilitation.