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Comparative Analysis of Linear and Nonlinear sEMG Methods for Detecting Muscle Fatigue During Dynamic Biceps Curls
No full textMuscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established linear and nonlinear methods in measuring muscle fatigue caused by dynamic contractions through surface electromyography (sEMG) signals. A group of twelve healthy individuals completed biceps curls at a consistent pace of one repetition per four seconds, which constituted 75% of their 10-repetition maximum. Concurrently, sEMG signals were captured from the biceps brachii muscle at 1000 Hz. To assess the sEMG signals during the initial, middle, and final sets of 10 repetitions, three linear metrics—mean frequency, median frequency, and spectral moment ratio (SMR)—along with two nonlinear approaches, namely sample entropy and detrended fluctuation analysis (DFA), were utilized. The study's outcomes indicated notable shifts in the SMR values and the two DFA-derived scaling exponents across the exercise sets. These results indicated that SMR, sample entropy, and DFA are effective in gauging muscle fatigue, with sample entropy and DFA demonstrating heightened sensitivity to the fatigue levels when compared to the linear metrics
Development of Virtual Reality-Based Left Brain System
No full textThis paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and lightweight haptic glove was invented which provides the sense of touch using vibrations while interacting with VR contents. An experimental study was conducted to assess the efficacy of BrainUp World compared to traditional PC-based training approaches. Participants were randomly assigned to either the VR-based or PC-based training group and underwent 6 different games to test a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill, and memory. The results revealed a statistically significant improvement in left brain function in the VR-based training group compared to the PC-based group, with a T-score growth of 4.73. Analysis using ANOVA confirmed the significance of this difference (p-Value = 0.04). Notably, the study also identified age-related differences in thinking fluency, highlighting the importance of personalized cognitive training interventions. In conclusion, BrainUp World demonstrates the potential of VR technology in promoting left brain development, as evidenced by empirical findings from the conducted study. By offering immersive and interactive cognitive training experiences, VR-based systems hold promise for enhancing various aspects of cognitive function associated with left hemisphere dominance. Further research in this area is encouraged to explore the full potential of VR-based interventions for cognitive enhancement
Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance
No full textThese days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting (LGB). The F1-score, recall, accuracy, and precision of these models were evaluated. According to the findings, the results indicated that LR, with its use of the Elastic Net parameter, outperformed the others with 96% accuracy, 97% precision, 94% recall, and 96% F1-score. Other models like XGBoost, LGB, and SVM also showed strong performance, whereas KNN had the poorest performance. The study highlights the importance of selecting the appropriate model for accurate mobile price prediction. Among all the machine learning used in this paper, the LR classifier outperforms the other state-of-the-art models because of the elastic Net parameter used for mobile phone price prediction.
Conditional Deployable Biometrics: Matching Periocular and Face in Various Settings
No full textIn this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model’s capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios
Design and Development of an Arduino Based Automated Solar Grass Trimmer
No full textThis paper focuses on the design of an Arduino-based Automated Solar Grass Trimmer with a primary emphasis on achieving high operational efficiency. A solar panel is utilized to automatically charge the battery when its level is low. A voltage level indicator circuit is incorporated to assess various battery voltage levels. The prototype integrates ultrasonic sensors for obstacle detection and inductive sensors for boundary detection. To ensure safe operation in the field, a perimeter signal generator circuit is constructed for boundary detection using inductive sensors. Ultrasonic sensors are employed for obstacle detection. The paper introduces an algorithm for detecting obstacles, boundaries, and other impediments in the path of the solar grass trimmer, illustrated through a comprehensive flowchart. Detailed discussions on the voltage level indicator circuit and boundary detection circuits are provided. The implemented algorithm, utilizing an Arduino microcontroller, is tested in the field, and the results are explained in the paper. Tabulated data from the prototype testing, specifically focusing on boundary detection and obstacle detection, demonstrate satisfactory performance.
Manuscript received: 17 Nov 2023 | Revised: 29 Jan 2024 | Accepted: 19 Feb 2024 | Published: : 30 Apr 202
Complement Properties of Pythagorean Co-Neutrosophic Graphs
No full textThe origination of graphs with neutrosophic type where membership of indeterminacy expels the vague results, by increasing the accuracy is used to extend application through the graphical environment. Since it is an extension of the intuitionistic type, there comes an immediate need to extend its findings and application to the neutrosophic type. Reversing the conditions of neutrosophic graphs by introducing the anti-behavior properties will produce an adequate number of new results and data, breaking the backlog in approaching decision-making problems and other real-world applications. This research aims to recognize the complementation concept in the Pythagorean co-neutrosophic graph, which has not been dealt with yet. The co-neutrosophic graph is the reversal concept of neutrosophic graphs, where the vertex and edge membership conditions are reversed, but the total sum of these memberships remains the same. Here, the discussion about complementation, co-complementation, and its properties are carried out on a Pythagorean co-neutrosophic Graph. As a result, an application with improved accuracy result will be obtained as an outcome.
Manuscript received: 15 May 2024 | Revised: 25 June 2024 | Accepted: 15 July 2024 | Published: : 30 Sep 202
Design and Development of Electrical Go Kart
No full textThis study explores the complex process of designing, developing, and building an electric go-kart with a focus on performance and sustainability. Using a multidisciplinary methodology, the research maximizes the vehicle's efficiency and environmental friendliness by integrating the principles of mechanical engineering, electrical engineering, and sustainable design. The study assesses many design factors, including motor power, battery capacity, and chassis materials, to find an ideal balance between performance and environmental impact through methodical experimentation and analysis. In order to improve the kart's energy economy and agility, the project also investigates cutting-edge technologies including lightweight composite materials and regenerative braking systems. The results of this study offer significant contributions to the subject of sustainable transportation, as well as to the development of electric car technology. Through the demonstration of the viability and efficiency of electric go-karts in comparison to their conventional gasoline-powered equivalents, this study highlights the significance of adopting renewable energy solutions within the automotive sector. In the end, the journal clarifies how electric go-karts can transform both competitive and recreational racing, making a strong argument for the broad use of clean energy technology in the quest for a more sustainable and environmentally friendly future.
Manuscript received: 6 May 2024 | Revised: 23 July 2024 | Accepted: 3 Aug 2024 | Published: : 30 Sep 202
Indirect Effect of Customer Relations on Leadership/Top Management, Employee Relations and Process Management in Examining the Readiness of SMEs to Implement Lean Initiatives: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.4
No full textBefore deployment of lean initiative, it is of great significance to examine the readiness of manufacturing SMEs to ensure they have the prerequisite for successful implementation. Consequently, in order to determine how prepared Nigerian manufacturing SMEs are to successfully adopt lean initiatives, the study measures the indirect effects of customer relations on leadership/top management, employee relations, and process management. Data for the study was collected from manufacturing SMEs and analyzed using Smart PLS-SEM 4.1. The study's conclusions demonstrate the substantial indirect effects that customer relationships have on employee relations, leadership/top management, and process management. Additionally, a positive correlation between employee relations and leadership/top management and process management is indicated by the direct effect findings. The study reiterates the imperatives of customers at the center of lean philosophy and emphasizes the need for synergy between leadership and employees in effective process management. The study also provides a new insight into the indirect effect of customer relations and the need for top management and employee relations in assessing the manufacturing SMEs' preparedness to implement lean initiatives for the actualization of quality and continuous improvement
Impact of Social Media Marketing on Gen Z's Cosmetic Brand Awareness: DOI: https://doi.org/10.33093/ijomfa.2024.5.1.3
No full textIn this era of rapid technological advancement, the number of consumers browsing online information steadily increases. Social media platforms have emerged as a vital channel for communication and sharing information, making them the greatest option for conducting cosmetic business. Due to the scarcity of existing studies discussing the factors affecting brand awareness of cosmetic products, this research paper aims to examine the impact of social media marketing on brand awareness for cosmetic products among Gen Z in Kuala Lumpur. A total of 275 responses were collected using the snowball sampling technique. The data were analyzed through the utilization of multiple regression analysis. The results showed that three dimensions of social media marketing- customization, interaction, and electronic word-of-mouth significantly affect brand awareness. This study contributes to the present body of knowledge by confirming the stimulus-organism-response model. Our study also suggests effective social media marketing strategies can generate brand awareness
