https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4141 Thu, 16 Apr 2026 09:52:34 GMT 2026-04-16T09:52:34Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4229 Title: Identifying Muscle Imbalances in Athletes via Motion Analysis using Sensory Inputs Authors: Vithanage, S.S.; Ratnadiwakara, M.S. Abstract: The balance of muscle strength and length between muscles surrounding a joint should be maintained to have a healthy movement and function in human body. Muscle imbalances are mainly caused due to repetitive movement in one direction or sustained posture and the tendency of certain muscle groups to be tight or weak. In the context of collegiate athletes, the healthy practice of movements is essential since the incorrect biomechanics can result in injuries that would take a considerable amount of time to recover through rehabilitation. The current clinical methods of identifying muscle imbalances such as Movement analysis, gait and posture analysis, joint range of motion analysis and muscle length analysis are all observational evaluations that entirely depends on the domain knowledge and experience of the observer. Technical methods such as Electromyography and CT scan require expensive equipment and patients would only care to examine after having pains or dysfunctions. To address the limitations of current muscle imbalance evaluation techniques such as time, cost and domain expertise, this research propose a solution that can incorporate an athlete to self-identify the certain overractive and underractive muscle groups in their body in order to follow intervention exercises. Movement analysis; one of the commonly used methods in the context of physiotherapy to identify dysfunctions in the human musculoskeletal system, is the method used in this research to identify muscle imbalances. The Overhead Squat is used as the movement pattern to be analyzed with the aid of the motion tracking device; Orbbec Astra Pro. Two mathematical models are used to calculate the joint angles and joint distances relating to the overhead squat. The joint positional data taken from Orbbec Astra Pro is used as the input for the mathematical formula. Overhead squat movement pattern of a healthy subject is represented as graphs in order to compare graphs of a new subject to identify deviations. By identifying deviations the research propose potential overractive and underractive muscle groups that causes the muscle imbalances. The accuracy of the proposed method was more than 75%, which is a satisfactory outcome. This method will facilitate the self-detection of imbalances in the whole body at a low-cost and with less time. Further enhancements to this proposed method can bring forth benefits to the fields of sports medicine, physiotherapy as well as physical fitness. Mon, 26 Jul 2021 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4229 2021-07-26T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4228 Title: The Influence of Community Interactions on User Affinity in Social Networks: A Facebook Case Study Authors: Senaweera, M.; Chamindi, M.M.N.D.; Dissanayake, S.A.D.Y.R. Abstract: With the advent of social media, one of the biggest concerns have been its suspected impact on democracy by influencing users’ opinions during elections. Impact of the social networks (and/or media) has been the center of that discussion due to well-known cases of using social networks to sway people’s opinion in events crucial to the democracy such as the election. The accusations vary from the propagation of fake news to concerns about Facebook having unfettered power over its users’ content. As a step towards understanding the true nature of social media’s influence, we started collecting data prior to Local Election in Sri Lanka, 2018 and continued the collection until the election was over; we collected the data through Facebook API and all of the data is completely anonymized. The dataset covers 44K users from Sri Lanka and their interactions with 44 Facebook groups. The dataset also includes all of the associated events related to each group. As a preliminary step, we have analyzed how the user affinity surrounding these groups have changed surrounding the period of the local election. Our analysis also gives a concrete and quantitative evidence of how users are communicating on Facebook and how active they are surrounding a sensitive event. Further, there is a significant change of affinity of a set of individuals over the time corresponding to the external event. Additionally, we find that there are users migrating among the Facebook groups during the election period. Further, we show that when the migration network, viewed as a weighted network, displays features which are significantly different from a comparable random network. In particular, we show that user migrations within Sri Lankan political groups during the election period show a non-random behavior potentially motivated by the real world events. We believe our analysis is the first of its kind in providing scientific analysis of the influence of social media in Sri Lanka. Mon, 26 Jul 2021 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4228 2021-07-26T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4227 Title: An Enhanced Model for Wildfire Propagation Prediction Using GIS Authors: Dantanarayana, A. V.; Perera, K. K. C.; Wickramathilaka, B. S. Abstract: Wildfire modeling and simulation has been one of the major subjects under intense experimental research and theoretical work to address the ever-growing crisis of wildfire. Many researchers have benefitted from these work and have developed multiple wildfire propagation prediction systems for decision support. Despite the large-scale effort undertaken by the scientific community, it can be also observed that these advancements have become limited to the developed countries of the world. This can be attributed to the fact that a reliably accurate wildfire behavior model requires many input variables and acquiring these variables requires a great deal of infrastructure already in place. These infrastructures can be quite costly, making it infeasible for the developing countries to develop a wildfire propagation prediction system. The purpose of this research is to enhance an existing wildfire model in a manner that it requires less infrastructure at an acceptable accuracy level. The study was begun by analyzing the existing models for extensibility and enhanceability. It was discovered that the Rothermel’s Surface Fire behavior model can be enhanced by eliminating some of its many variables. Therefore a set of variables were selected through some rationale and were experimented upon using GIS platforms to observe the effect they have on the Rothermel’s model. The study was conducted using historical wildfire data and the primary measure used was the Jaccard Similarity Coefficient. To assess the practicality of the model, a novel framework named ‘MOD (Most Occurring Data) Sign’ analysis was proposed. The results of the study show that ‘fuel particle moisture’ and ‘live fuel load’ variables have significantly less effect on the Rothermel’s model. It was also discovered through the MOD Sign Analysis that ‘fuel particle moisture’ was the more practical variable to eliminate rather than ‘live fuel load’. Finally, it was concluded that a simplified model can be derived from the Rothermel’s model by eliminating ‘fuel particle moisture’ variable and while ‘live fuel load’ may also be eliminated, the resulting model will not be suitable for decision making. Mon, 26 Jul 2021 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4227 2021-07-26T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4226 Title: Behavioral Analysis of Bitcoin Users on Illegal Transactions Authors: SAMSUDEEN, F. Z. Z. M.; PERERA, H. P. D. U. Abstract: Bitcoin is a popular crypto currency that is used as a mode of investment and a medium for trading goods and services. The features such as anonymity, security and decentralization are the significant characteristics of Bitcoin. These features attract different types of users to adapt to this payment mechanism since 2009. In particular, it create several opportunities for criminals to involve in illegal and fraudulent activities. Thus, this research is based on the negative discussion around the Bitcoin. The thesis pays particular attention on identifying user behavioral patterns and significant facts among illegal incidents that are of varied nature. The study examines each illegal incident of different nature so that it is able to identify common spending patterns among illegal users. The motivation for choosing this study lack of literature that covers illegal incidents of various nature and analysis for patterns and significant facts in patterns. The analysis recognized spending pattern, popular exchanges used, and notable techniques used to cash out tainted Bitcoins. It shows that the illegal users utilize several techniques to cash out tainted Bitcoins. One way is to directly transfer to exchanges or Bitcoin washers or services which is an evaluation of a previous research. Another way is to transfer the Bitcoins in small amounts to several other their own addresses or to fresh wallets in subsequent transactions and thereafter transfer to exchanges. Bitcoins are transferred to fresh wallets in small amounts. They either transfer a constant amount of Bitcoins or in a certain proportion. These attempts are taken by illegal users with the intention of prevent being tracked by investigators. In addition, popular services such as Xapo.com, Helix Mixer and Poloniex.com were discovered as frequently used services. Mon, 26 Jul 2021 00:00:00 GMT https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4226 2021-07-26T00:00:00Z