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Nazarudin MN, Abdul Majeed AP, Husin Musawi Maliki AB, Abdullah MR, Kuan G, Muazu Musa R. Disciplinary measures defining referee activity in top-European football leagues: A cross-sectional investigation. Heliyon 2024; 10:e25402. [PMID: 38352766 PMCID: PMC10861984 DOI: 10.1016/j.heliyon.2024.e25402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/25/2024] [Accepted: 01/25/2024] [Indexed: 02/16/2024] Open
Abstract
The success and enjoyment of a football match depend heavily on referees and their ability to ensure fair play and uphold the rules of the game. However, there is limited research investigating the disciplinary measures and booking activities of referees in top European football leagues. In the current investigation, we explored the disciplinary measures and booking activities of top-European football league referees. The dataset of the referee activities concerning 15 indicators containing 602 matches from five consecutive seasons across the five top European leagues, namely, the English Premier League, Spanish Laliga, Italian Serie A, French Ligue1, and German Bundesliga were utilized for this study. K-means cluster analysis was used to define the activity levels of the referees. The Mann-Whitney U test was employed to determine the differences in the levels of the referees' activity with respect to the disciplinary measures, while binary regression analysis was applied to examine the association between the disciplinary measures and the activity levels of the referees. Two groups of activities were defined by k-means, that is, high and low activity. The Mann-Whitney U test revealed statistically significant differences in all 15 indicators examined between high and low activity. However, the regression model demonstrated that only fouls, yellow cards, and air challenges could significantly describe referees' activity levels. These indicators appear to be predictors of high referee activity in elite European Football. Specific training on dealing with increased aggression and foul behaviour coupled with improved game organisational management could be further incorporated into referees' training programmes amongst other measures.
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Affiliation(s)
- Mohamad Nizam Nazarudin
- Center for the Study of Education and Community Wellbeing, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia
| | - Anwar P.P. Abdul Majeed
- School of Robotics, XJTLU Entrepreneur College (Taicang). Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu Province, 215123, China
| | - Ahmad Bisyri Husin Musawi Maliki
- Defense fitness academy, Universiti Pertahanan Nasional Malaysia (UPNM) Sungai Besi Camp, 57000, Sungai Besi, Kuala Lumpur, Malaysia
| | - Mohamad Razali Abdullah
- Faculty of Health Science, Universiti Sultan Zainal Abidin, 21300 Kuala Nerus, Terengganu, Malaysia
| | - Garry Kuan
- Exercise and Sports Science Programme, School of Health Sciences, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, 21030, Kuala Nerus, Terengganu, Malaysia
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Islam MN, Sulaiman N, Farid FA, Uddin J, Alyami SA, Rashid M, P.P. Abdul Majeed A, Moni MA. Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline. PeerJ Comput Sci 2021; 7:e638. [PMID: 34712786 PMCID: PMC8507488 DOI: 10.7717/peerj-cs.638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/21/2021] [Indexed: 05/14/2023]
Abstract
Hearing deficiency is the world's most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain's cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method's performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.
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Affiliation(s)
- Md Nahidul Islam
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Fahmid Al Farid
- Faculty of Computing and Informatics, Multimedia University, Malaysia
| | - Jia Uddin
- Technology Studies Department, Endicott College, Woosong university, Daejeon, South Korea
| | - Salem A. Alyami
- Department of Mathematics and Statistics, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | - Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland St Lucia, Australia
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Abdullah MA, Ibrahim MAR, Shapiee MNA, Zakaria MA, Mohd Razman MA, Muazu Musa R, Abu Osman NA, Abdul Majeed AP. The classification of skateboarding tricks via transfer learning pipelines. PeerJ Comput Sci 2021; 7:e680. [PMID: 34497873 PMCID: PMC8384043 DOI: 10.7717/peerj-cs.680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/28/2021] [Indexed: 06/13/2023]
Abstract
This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.
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Affiliation(s)
- Muhammad Amirul Abdullah
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Muhammad Ar Rahim Ibrahim
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Muhammad Nur Aiman Shapiee
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Muhammad Aizzat Zakaria
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Noor Azuan Abu Osman
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Kuala Lumpur, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Malaysia
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Bari BS, Islam MN, Rashid M, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, P.P. Abdul Majeed A. A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput Sci 2021; 7:e432. [PMID: 33954231 PMCID: PMC8049121 DOI: 10.7717/peerj-cs.432] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 02/17/2021] [Indexed: 05/25/2023]
Abstract
The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
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Affiliation(s)
- Bifta Sama Bari
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Md Nahidul Islam
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mamunur Rashid
- Faculty of Electrical & Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Md Jahid Hasan
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pahang Darul Makmur, Pekan, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pahang Darul Makmur, Pekan, Malaysia
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Mahendra Kumar JL, Rashid M, Muazu Musa R, Mohd Razman MA, Sulaiman N, Jailani R, P.P. Abdul Majeed A. The classification of EEG-based winking signals: a transfer learning and random forest pipeline. PeerJ 2021; 9:e11182. [PMID: 33850667 PMCID: PMC8019310 DOI: 10.7717/peerj.11182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 03/08/2021] [Indexed: 11/20/2022] Open
Abstract
Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.
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Affiliation(s)
- Jothi Letchumy Mahendra Kumar
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
| | - Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Liberal Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
| | - Norizam Sulaiman
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rozita Jailani
- Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Gambang, Malaysia
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Rashid M, Bari BS, Hasan MJ, Razman MAM, Musa RM, Ab Nasir AF, P.P. Abdul Majeed A. The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN. PeerJ Comput Sci 2021; 7:e374. [PMID: 33817022 PMCID: PMC7959631 DOI: 10.7717/peerj-cs.374] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/06/2021] [Indexed: 05/27/2023]
Abstract
Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.
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Affiliation(s)
- Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Md Jahid Hasan
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Rabiu Muazu Musa
- Centre for Fundamental and Continuing Education, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia
| | - Ahmad Fakhri Ab Nasir
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Anwar P.P. Abdul Majeed
- Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Centre for Software Development & Integrated Computing, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
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Mohd Khairuddin I, Sidek SN, P.P. Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H. The classification of movement intention through machine learning models: the identification of significant time-domain EMG features. PeerJ Comput Sci 2021; 7:e379. [PMID: 33817026 PMCID: PMC7959624 DOI: 10.7717/peerj-cs.379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/07/2021] [Indexed: 05/29/2023]
Abstract
Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
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Affiliation(s)
- Ismail Mohd Khairuddin
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Shahrul Naim Sidek
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Anwar P.P. Abdul Majeed
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Asmarani Ahmad Puzi
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Hazlina Md Yusof
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
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Taha Z, Mohd Razman MA, Adnan FA, P.P. Abdul Majeed A, Musa RM. The Development of the Putt.It.In Monitoring Device and the Establishment of Its Reliability: A Solution for Putting-In Analysis in Golf. MoHE 2017. [DOI: 10.15282/mohe.v6i1.136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: The accurate transfer of information on the athletes’ performance in any sport is essential in enhancing the performance and overall coaching process. The provision of such information is favourable only if it is reliable. A cost-effective golf putting monitoring device namely the Putt.It.In was developed for analysing a golfers’ putting performance. Objectives: This study aims to investigate the reliability of the device in measuring the backswing distance, front swing distance, clubhead speed, ideal front swing distance and swing angle. Methods: A semi- professional golfer (30 years of age ± 5.0 years’ experience) executed four strokes repeatedly from a distance of 2 m and 1 m using a Ram Zebra Mallet putter on a PGM golf mat. Kolmogorov/Smirnov test was utilised to ascertain the reliability of the application in measuring the aforementioned parameters over test re-test between first two strokes of 2 m distance and the last two strokes of 1 m distance. Results: The Kolmogorov/Smirnov test re-test suggests that there is no significant difference between first two meters strokes p > 0.05, and second 1-meter strokes p > 0.05 highlighting its ability to recognise the pattern of the strokes applied in the four successive strokes. Conclusion: The Putt.It.In monitoring device is found to be reliable in measuring the backswing distance, front swing distance, clubhead speed, ideal front swing distance and swing angle. Professional and semi-professional golfers as well coaches could consider Putt.It.In device in monitoring strokes related parameters to enhance their performance due to its effectiveness in providing information on putting performance.
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