Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks

Game Application as Teaching Tool to Assist Mastering Installation of Three-Phase Motor Control Topic: Expert Perception

The learning and teaching process using conventional methods might cause students to be unable to highlight their true potential. Besides, it can be more difficult for teachers to choose the types of games that suit the curriculum and students' needs. Hence, this study aims to develop a game application as a teaching assistant tool for a three-phase direct online starter topic in Electrical Technology Malaysian Vocational Certificate subject for Vocational College students using the Android platform. Learning materials and games are developed using multimedia and interactive elements to attract students to be active in using the learning medium based on game applications as 21st-century learning tools. The development purpose of the game application is to give early exposure to students to the installation of three-phase motor control before going through the laboratory session. The behaviorism learning theory and Game development Life Cycle (GDLC) model, were implemented in the game application development in this research. The researcher used Unity 3D software is online software such as Canva and Microsoft PowerPoint design tools interactively to make a game-based learning application for the three-phase motor control installation subject. Based on the analysis of the findings, the experts agreed that the functionality of this three-phase Motor Control game application can be used well and is suitable to be used as a teaching aid. In addition, these research results can be used by teachers to increase students' interest and further make the learning process more interesting to improve student understanding

Particle swarm optimization: based identification of a bouncing spherical robot

In this paper a spherical robot consists of a spherical steel spokes shell and an inner bouncing mechanism. To control the bouncing mechanism, a system model should be introduced. The system model only deal with the first bounce of the spherical mobile robot to overcome the bouncing restitution for landing. This paper presents a transfer function model prediction with particle swarm optimization for a bouncing spherical mobile robot. The particle swarm optimization technique is used in search for accurate model with capability to adapt with different input height of drop. The model is further validated with autocorrelation and crosscorrelation test and it is proven to give an error tolerance between the 95% confidence limit

Face recognition using assemble of low frequency of DCT features

Face recognition is a challenge due to facial expression, direction, light, and scale variations. The system requires a suitable algorithm to perform recognition task in order to reduce the system complexity. This paper focuses on a development of a new local feature extraction in frequency domain to reduce dimension of feature space. In the propose method, assemble of DCT coefficients are used to extract important features and reduces the features vector. PCA is performed to further reduce feature dimension by using linear projection of original image. The proposed of assemble low frequency coefficients and features reduction method is able to increase discriminant power in low dimensional feature space. The classification is performed by using the Euclidean distance score between the projection of test and train images. The algorithm is implemented on DSP processor which has the same performance as PC based. The experiment is conducted using ORL standard face databases the best performance achieved by this method is 100%. The execution time to recognize 40 peoples is 0.3313 second when tested using DSP processor. The proposed method has a high degree of recognition accuracy and fast computational time when implemented in embedded platform such as DSP processor

The application of support vector machine in classifying potential archers using bio-mechanical indicators

This study classifies potential archers from a set of bio-mechanical indicators trained via different Support Vector Machine (SVM) models. 50 youth archers drawn from a number of archery programmes completed a one end archery shooting score test. Bio-mechanical evaluation of postural sway, bow movement, muscles activation of flexor and extensor as well as static balance were recorded. k-means clustering technique was used to cluster the archers based on the indicators tested. Fine, medium and coarse radial basis function kernel-based SVM models were trained based on the measured indicators. The five-fold cross-validation technique was utilised in the present investigation. It was shown from the present study, that the employment of SVM is able to assist coaches in identifying potential athletes in the sport of archery

Comparison between satellite-derived rainfall and rain gauge observation over Peninsular Malaysia

Validation of the bias-corrected product of National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre Morphing Technique CMORPH-CRT was conducted using gridded rain gauge dataset of Wong et al. (2011) and rain gauge data from meteorological stations throughout Peninsular Malaysia. The CMORPH-CRT was compared for four contrasting topographic sub-regions of Peninsular Malaysia, i.e. west coast (WC), foothills of Titiwangsa range (FT), inland-valley (IN) and east coast (EC). CMORPH-CRT product with grid resolution of 8 km × 8 km at temporal resolution of 1-hour from 00Z January 1998 to 23Z December 2018 was utilized. The results show that CMORPH-CRT are in agreement with the rain gauge data. The CMORPH-CRT performed best over coastal sub-regions but it underestimated over FT sub-region and overestimated at IN. CMORPH-CRT tend to perform better in moderate rather than heavy rainfall events. For extreme weather events, the CMORPH-CRT had shown capability in observing the formation and decay of low-pressure system in Penang during 4th November 2017 and it is in agreement with rain gauge based SPI index i.e. drought conditions over Peninsular Malaysia

Modelling and investigation on bouncing mechanism of a sphere robot

Spherical rolling mechanisms (SRMs) exhibit a number of advantages with respect to wheeled and legged mechanisms. In fact if the SRM is combined with the power of bouncing mechanism, it will produce an exciting phenomena that can be contributed to applications such as security surveillance, search and rescue. There is not much research done in both fields, especially in the bouncing mechanism. In fact to the best of authors’ knowledge no of research has been done on integrating both mechanism to produce a spherical system that is capable of rolling and bouncing, which can produce a very significant mobile robot. Therefore, this research deals with the modeling and development of a bouncing spherical robot using computational intelligent technique, i.e. Particle Swarm Optimization technique (PSO). A 3D virtual prototype of a spherical robot was developed in Visual Nastran as a platform for input and out data acquisition. Different simulations environment have been created, such as the free fall bouncing, shooting up and projectile type of environment to investigate the bouncing profile affected by different forces. The data obtained were then used for system identification using PSO technique with mean square error (MSE) of 0.0004%. The transfer function representing the bouncing mechanism of the sphere robot was then obtained. Next, the prototype of the sphere robot with bouncing capability was developed. Open loop tests have been conducted and the results show that the hardware developed can produce the bouncing mechanism at its promising capability. Future works need to be conducted to re-visit the hardware, particularly on the body of the sphere robot such that maximum bouncing can be achieved

Battery cell balancing optimisation for battery management system

Battery cell balancing in every electrical component such as home electronic equipment and electric vehicle is very important to extend battery run time which is simplified known as battery life. The underlying solution to equalize the balance of cell voltage and SOC between the cells when they are in complete charge. In order to control and extend the battery life, the battery cell balancing is design and manipulated in such way as well as shorten the charging process. Active and passive cell balancing strategies as a unique hallmark enables the balancing of the battery with the excellent performances configuration so that the charging process will be faster. The experimental and simulation covers an analysis of how fast the battery can balance for certain time. The simulation based analysis is conducted to certify the use of optimisation in active or passive cell balancing to extend battery life for long periods of time

The classification of elbow extension and flexion: A feature selection investigation

Nowadays, the worldwide primary reasons of long-term disability is stroke. When the blood supply to your brain is interupted and reduced, stroke occurs as it depriving brain tissue of nutrients and oxygen. In the modern world, advanced technology are revolutionizing the rehabilitation process. This research uses mechanomyography (MMG) and machine learning models to classify the elbow movement, extension and flexion of the elbow joint. The study will aid in the control of an exoskeleton for stroke patient's rehabilitation process in future studies. Five volunteers (21 to 23 years old) were recruited in Universiti Malaysia Pahang (UMP) to execute the right elbow movement of extension and flexion. The movements are repeated five times each for two active muscles for the extension and flexion motion, namely triceps and biceps. From the time domain based MMG signals, twenty-four features were extracted from the MMG before being classified by the machine learning model, namely k-Nearest Neighbors (k-NN). The k-NN has achieved the classification accuracy (CA) with 88.6% as the significant features are identified through the information gain approach. It may well be stated that the suggested process was able to classify the elbow movement wel

The classification of skateboarding tricks: A support vector machine hyperparameter evaluation optimisation

The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as previous techniques in the identification of the tricks was often inadequate in providing accurate evaluation during competition. Therefore, there is a need to introduce an unprejudiced method of evaluation in skateboarding competitions. This paper presents the classification of five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) through the identification os significant frequency-domain signals collected via Inertial Measurement Unit (IMU) and the use of machine learning models. Onemale skateboarder (age: 23 years old) performed five different tricks repeatedly for several times. The time-domain data acquired from the IMU were converted to frequency-domain by employing Fast Fourier Transform (FFT) and a number of statistical features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x-y-z-axis of the IMU) were then extracted. Significant features were then identified from the Information Gain (IG) scoring. It was shown from the study that the Naïve Bayes (NB) classifier is able to acquire the highest classification accuracy of 100% on the test data compared to the other evaluated classifiers, namely Artificial Neural Network (ANN) and SupportVector Machine (SVM), by utilising the selected features, suggesting that the proposed methodology could provide an objective-based evaluation of the trick