The number of research papers on Motion Capture technologies published in conferences and journals has been rapidly increasing due to the emerging of new technologies, software and hardware which create new challenges and opportunities for Martial Arts research. Current trend of the Martial Arts using Motion Capture technologies (MAMoCap) researches consists of phases of MoCap-Processing and Post-MoCap-Processing; contexts of algorithms, performance and system development; and feedbacks of intrinsic and extrinsic. The purpose of this paper is to study and explore the potential future trend of research and publications pertaining to MAMoCap researches. A systematic survey of research publications was conducted through the topic of Martial Art (MA) and Motion Capture (MoCap) in order to retrieve the scientific articles published in FOUR (4) established publishers including SPRINGERLINK, SCIENCEDIRECT, IEEE and ACM. Search refinements were done by the inclusions criteria of document types of academic journals and conference proceedings; and by the exceptions criteria of letters, editorials and book reviews. The findings show that only 27% of the publications have been selected while other 73% have been classified as irrelevant contents due to none significance and relevance to the MAMoCap researches. Analysis on the research phases, contexts and feedbacks has been conducted and discussed in detailed for pertaining knowledge gaps and future research agenda. Based on the preliminary study, a framework of EFs-Based Automated Evaluation System for the martial arts should be proposed.

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer.

No abstract

Martial art (MA) is considered a conserved heritage primarily for promoting certain level of identities and cultures. With the technology advancement, motion capture has been widely used in MA to capture and evaluate human performance. However, methods to create motion templates (templates) of MA techniques from scratch are rarely exposed because there is no complete evaluation system framework suggested for MA. This paper presents in detaile how to create the MA templates using framework of extrinsic feedback-based evaluation system generally and R-GDL specifically. To create more robust templates, GDL has been used in validation tests. SSCM becomes the case study in this research. The main contribution in this paper is introduction of new datasets of templates for SSCM techniques. Evaluation of

Martial arts (MAs) are considered as a preserved heritage primarily due to the fact that it promotes certain level of identities of a culture. MA refers to the art of combat and self-defense which normally combines offensive and defensive techniques. Technology advancements have made motion capture (MoCap) to be widely used in MA to capture and evaluate human performance. Nevertheless, researches on extrinsic feedbacks (EFs) of MA through the developed evaluation system are scarce. Furthermore, there is no complete framework of evaluation system suggested for MA. This paper presents the theoretical framework of EF-based automated evaluation system in the context of traditional local MA. The framework contains three modules including MoCap, recognition and evaluation. The MoCap module tracks human body accurately in order to generate skeleton, tune focused target, and record human movements. Recognition module develops a script of motion for templates and classification purposes using Reverse-Gesture Description Language (R-GDL) and GDL respectively. Evaluation module produces the extrinsic feedback in terms of pattern and score for the performed movements. This theoretical framework will be used in the development of the digital tool to measure the accuracy and effectiveness of motions performed by one of the traditional local MAs.

No abstract

Martial art (MA) is considered a conserved heritage primarily for promoting certain level of identities and cultures. With the technology advancement, motion capture has been widely used in MA to capture and evaluate human performance. However, methods to create motion templates (templates) of MA techniques from scratch are rarely exposed because there is no complete evaluation system framework suggested for MA. This paper presents in detaile how to create the MA templates using framework of extrinsic feedback-based evaluation system generally and R-GDL specifically. To create more robust templates, GDL has been used in validation tests. SSCM becomes the case study in this research. The main contribution in this paper is introduction of new datasets of templates for SSCM techniques. Evaluation of

No abstract