Author Guidelines Journal of Human Centered Technology (HumEnTech) is an open-access international journal, covering new insight in science and technology related to analysis and application on human. HumEnTech provides a peer-reviewed forum for the publication of research and review articles which encompasses new methods/innovative practices, new discoveries, latest research findings, critical evaluations and critical surveys on human related subjects/topics. The journal welcomes contribution from the following research areas but not limited to: Human-computer interaction Assistive technology Sport technology Rehabilitation Health informatics Human-systems integration Nutritional technology Artificial intelligence for human technology Ergonomics Artificial organs Medical instrumentation & system Technology for Flexible Learning more Information on article submission are available at the Author Guidelines. All authors are required to read the Publication Ethics and the Privacy Statement prior an article submission. Indexed by: MyJurnal Indexed by: MyCite
Physicochemical and Thermal Analysis of Silane-Modified Carbonated Hydroxyapatite Fillers for Periodontal Application
Periodontal disease is a chronic inflammation affecting the teeth’s supporting structures that, if untreated, can lead to tooth loss. To address this issue, biomaterial-based approaches are currently being explored, requiring materials that are biocompatible and capable of supporting bone regeneration. Carbonated hydroxyapatite (CHA) has shown promise for periodontal applications due to its biocompatibility and enhanced bioactivity compared to stoichiometric hydroxyapatite. However, surface modification of CHA is necessary to improve its performance as a filler in composite biomaterials. To address this, two silane coupling agents, tetraethyl orthosilicate (TEOS) and 3-(trimethoxysilyl)propyl methacrylate (TMSPMA) were evaluated at different reaction temperatures. Physicochemical and thermal analyses were conducted to determine the optimal strategy for modifying the CHA surface. The physicochemical analyses confirmed successful carbonation and silanization of CHA, with higher reaction temperatures yielding more extensive and homogeneous silane condensation on the surface. The elemental analysis revealed that the CHA modified with TEOS at 78.3°C possessed the highest silicon content among all groups. Furthermore, silanization process enhanced the thermal stability of CHA. Thus, silanization at elevated temperatures has effectively modified the CHA matrix, with TEOS modification at 78.3°C identified as the optimal strategy for periodontal biomaterial applications.
Copyright (c) 2026 Journal of Human Centered Technology
Improving Right to Left Cursive Handwritten Text Recognition in Historical Manuscripts Using Learnable Edge Features and Channel Attention
Recent Handwritten Text Recognition (HTR) systems for Arabic and other right-to-left historical manuscripts have advanced exploration through convolutional neural network (CNN), recurrent, and Transformer-based models. However, degradation, weak diacritics, unstable baselines, and visually similar cursive letterforms still limit the recognition robustness. This paper presented an edge-aware line-level HTR framework that extends a CNN-Transformer baseline with a learnable edge-extraction channel and Squeeze-and-Excitation (SE) channel attention. The edge module emphasized stroke boundaries and character contours, while SE attention recalibrated feature responses to suppress background artifacts and preserve informative ink patterns. The resulting sequence was modeled by a Transformer encoder and trained using Connectionist Temporal Classification (CTC) with an auxiliary decoder cross-entropy loss. The experiments on the Kalima Arabic manuscript line-image dataset, using Books 1-8 with 86 pages and 1,759 annotated text lines, reduced character error rate from 6.40% to 4.10% and word error rate from 27.43% to 20.43%. These results show that combining learnable structural cues with channel-wise attention has improved robustness for degradation-prone historical manuscript collections.
Enhanced Alzheimer’s Disease Detection Using Transformer-Based GAN and Deep Learning Techniques
Early detection of Alzheimer’s Disease (AD) is essential for timely clinical intervention. However, conventional Magnetic Resonance Imaging-based (MRI-based) diagnostic approaches are often limited by class imbalance, insufficient training data, inter-dataset variability, and inadequate feature extraction capability, which reduce the reliability of early-stage AD identification. This study proposed an enhanced deep learning framework that integrated a Transformer-based Generative Adversarial Network (TGAN) with an attention-enhanced ResNet-50 architecture for robust multiclass classification of Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s Disease (AD) MRI images. The TGAN model was employed to generate anatomically realistic synthetic MRI scans to address dataset imbalance and improve feature diversity, while the enhanced ResNet-50 utilized residual learning and attention mechanisms to improve the extraction of discriminative neuroanatomical features associated with disease progression. A comprehensive preprocessing pipeline was applied to standardize MRI data obtained from the ADNI and OASIS datasets. We conducted a series of experiments on the ADNI and OASIS databases. The proposed system demonstrated a classification accuracy of 94.8% on ADNI and 93.6% on OASIS, greatly surpassing baseline models such as ordinary ResNet-50, DenseNet-121, and traditional GAN-augmented CNNs. Ablation investigations further verified the significance of the Transformer-based generator and attention-enhanced residual blocks, each yielding substantial performance enhancements.
My Loghat!: Design, Development, and Evaluation of a Mobile Multimedia Application for Malay Dialect Learning and Cultural Immersion in Tourism
As global tourism continues to expand, effective communication between tourists and local communities remains a persistent challenge. This paper presents the development and evaluation of My Loghat!, an interactive mobile e-learning application designed to teach Malay language dialects from four Malaysian states — Terengganu, Kedah, Sabah, and Perak — to tourists. Guided by the Multimedia Development Life Cycle (MDLC) methodology, the application integrated audio recordings of native dialect speakers, visual aids, culturally contextualised content (state history, traditional costumes, and notable places), and gamified quizzes. The system was implemented using Adobe Animate, Audacity, and Canva, and deployed as an Android APK. A structured evaluation was conducted involving two multimedia experts and 20 target users, assessed across three dimensions: content quality, effectiveness, and user satisfaction, using a four-point Likert scale. The results demonstrated strong overall acceptance, with 54.1% of target user responded at the 'Agree' level and 45.9% at 'Strongly Agree'. All responses were positive, indicating no disagreement with the application's features. The expert evaluations similarly yielded predominantly positive assessments. The findings suggest that My Loghat! represents a viable, accessible, and engaging tool for improving dialect comprehension and cultural immersion among tourists visiting Malaysia. The study contributes to the growing body of literature on mobile-assisted language learning (MALL) and cultural heritage preservation through technology.
Prediction of Myocardial Infarction Risks Using Support Vector Machine and Hyperparameter Optimization
Cardiovascular diseases, notably myocardial infarction (MI) remain as the primary cause of global mortality, with millions of deaths each year, often attributed to delayed diagnosis and intervention. This study focused on predictive modelling for MI risks using Support Vector Machine (SVM) algorithm, with its hyperparameter tuned by both GridSearchCV and RandomizedSearchCV. Data extracted from UCI Heart Disease Dataset underwent comprehensive preprocessing pipeline including normalization and Chi-Square feature selection to identify key predictors. The predictive accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUROC) were evaluated. Both the Grid Search and Randomized Search tuned models achieved a superior accuracy of 84.21%, with the Grid Search model yielding a slightly higher AUROC of 0.9137, confirming the effectiveness of hyperparameter tuning. This outcome highlights the superior discrimination and classification performance in predicting MI. This finding demonstrates the potential of machine learning to enable timely, accurate diagnosis of cardiovascular diseases and therefore improve patient care.
Expert Validation of a Faith-Informed Sensemaking Model for Online Risk
The digital age brought about increasing concerns among Muslim parents on online risks and its potential impacts on their children. For this purpose, this research addressed the needs in assisting Muslim parents sensemaking of online risks by introducing the integration of online-risk taxonomy and Maqasid al-Shariah into the sensemaking model originally introduced by Pirolli and Card. These two frameworks served as scaffolding, mapped specifically to the ‘Organize’ and ‘Schematize’ phases of the sensemaking model. Within the Organize phase, risks were classified into Content, Contact, Conduct and Contract while within Schematize, each risk’s significance was evaluated with reference to the five Maqasid objectives (Faith, Life, Intellect, Lineage, Wealth). An eight-item instrument mapping these constructs was evaluated by four domain experts. Unanimous relevance ratings were achieved for every item (I-CVI = 1.00; S-CVI/Ave = 1.00), thereby confirming strong content validity. The findings therefore support the model’s conceptual coherence and cultural resonance at this stage, while further empirical testing with Muslim parents is still required.
Orientation-Engineered Sansevieria Fibers for Sustainable Prosthetic Reinforcement
The development of sustainable materials for prosthetic applications has become increasingly important due to concerns related to environmental impact, cost, and accessibility. Natural-fiber-reinforced composites (NFRCs) offer promising alternatives to synthetic composites due to their low density, renewability, and environmentally friendly characteristics. This study investigated the potential of Sansevieria trifasciata fibers as reinforcement in vinyl ester composites for prosthetic socket applications. The fibers were mechanically extracted and treated with 5% sodium hydroxide (NaOH) to improve fiber–matrix adhesion. Composites with a fiber volume fraction of 30% were fabricated using two fiber architectures: unidirectional alignment and woven configurations with weave distances of 0, 2.5, 5.0, and 7.5 mm. The mechanical performance was evaluated using tensile testing (ASTM D3039) and Charpy impact testing (ASTM E23). The unidirectional composite exhibited the highest tensile strength (60.27 MPa) and Young’s modulus (78.71 GPa), indicating efficient load transfer along the fiber direction. Meanwhile, the woven composites with 2.5 mm spacing (W-2.5) showed the highest impact strength (0.093 J/mm²) due to improved energy absorption mechanisms such as crack deflection and fiber pull-out. These results demonstrate that fiber orientation significantly influences the mechanical performance of Sansevieria fiber composites, highlighting their potential as sustainable and cost-effective reinforcement materials for prosthetic socket structures.
Human-Centered Design and Evaluation of a Gamified 2D Animation with Visual Effects for Environmental Awareness
This study examines a human-centered approach to design and evaluate a gamified two dimensional (2D) animation enhanced with dynamic visual effects (VFX) for promoting environmental awareness. Framed within the domain of Human–Computer Interaction (HCI), the research explores how visual design, narrative structure, and embedded gamification elements influence user engagement, usability, and perceived behavioural intention. The development process followed the Multimedia Development Life Cycle (MDLC), integrating core serious game elements such as goal framing, narrative conflict, feedback cues, and reward visualisation into the animated storyline. A mixed-method evaluation was conducted with participants from diverse age groups, including adolescents and adults, combining post-exposure quantitative surveys and qualitative feedback to assess four dimensions: knowledge acquisition, technical readiness and media familiarity, visual and design appeal, and perceived impact. The results indicate strong perceived performance in technical readiness (M = 4.32) and visual engagement (M = 3.94), alongside increased awareness of littering issues (M = 3.87). However, perceived motivation to take direct anti-littering action remained moderate (M = 3.60), highlighting the challenge of translating awareness into behavioural intention. Overall, the findings suggest that gamified 2D animation with dynamic VFX can serve as an effective human-centered communication tool for environmental awareness by fostering engagement and perceived attitudinal influence. The results are interpreted as self-reported perceived impact rather than direct measurement of behavioural change, indicating the need for future studies employing longitudinal or experimental designs to examine actual behavioural outcomes.
Risk Factors for Breast Cancer in Pakistani Women: A Review of Current Evidence
The most reported malignancy in women of Pakistan is breast cancer. About one in nine females suffers from this disease, that contributes largely to mortality. The burden of the disease here is much more than in any other country of South Asia, with such salient features as earlier age of onset and late-stage presentation. This paper aimed to synthesize evidence from recent studies conducted in Pakistan on risk factors for breast cancer. Lifestyle determinants such as obesity, physically inactive or sedentary behavior, and unhealthy dietary patterns prove to be common and strongly associated with high risk. Genetic predisposition, particularly BRCA1 and BRCA2 mutations, plays a key role in increasing susceptibility among high-risk women. Additionally, reproductive factors such as early menarche, late menopause, nulliparity, and limited breastfeeding have been recognized as important contributors. In addition to urban pollution and dietary contaminants, environmental exposure also increases the risk; vitamin D deficiency could compound the disease further. Diagnosis gets late͏ and results get worse due to socioeconomic͏ and cultural barriers, low awareness, stigma, and low access to screening facilities. When compared with the regional population and even the global population, it can be seen that Pakistani women face some unique sociocultural challenges that make them more vulnerable. This review article demonstrates the immediate need for specific public health interventions, better genetic counseling infrastructure, and culturally appropriate mass awareness campaigns to lessen the breast cancer burden at a national level. Identifying key risk factors and leveraging artificial intelligence (AI)-driven technologies can improve prevention strategies and facilitate early detection.
Integrating RNLE and REBA to Evaluate Manual Handling Risks among Semiconductor Technicians
Manual handling of wafer pods persists in semiconductor fabrication despite high levels of automation, exposing technicians to work-related musculoskeletal disorders (WMSDs). This case study evaluated ergonomic risks associated with lift-and-load wafer pod tasks in the Fab Integration Department of a semiconductor facility in northern Malaysia. Eight technicians performing routine transfers from upper and lower trolley levels to tool loading ports were assessed using direct observation, task analysis, and physical measurements. Lifting demands were quantified using the Revised NIOSH Lifting Equation (RNLE) to determine the Recommended Weight Limit (RWL) and Lifting Index (LI), while postural risk was evaluated using the Rapid Entire Body Assessment (REBA). RNLE analysis showed LI values ranging from 1.01 to 2.00, indicating increased to high lifting risk, particularly for lower trolley tasks. Excessive horizontal reach and asymmetric trunk rotation were identified as the main contributors, reflected by reductions in the Horizontal Multiplier (HM) and Asymmetry Multiplier (AM). Postural assessment revealed high REBA risk levels (scores 6–8) associated with trunk flexion, neck extension, and asymmetric lifting. Based on these findings, targeted ergonomic interventions were proposed, including limiting twisting angles, maintaining safe horizontal reach distances, adopting squatting techniques for low-level lifting, and providing ergonomics training. The integrated RNLE–REBA approach offers a practical framework for identifying high-risk manual handling tasks in semiconductor manufacturing. Future research should extend this assessment to other departments and evaluate digital ergonomic monitoring tools for continuous risk management.
Ergonomics and Psychosocial Predictors of Work-Related Musculoskeletal Disorders among Nursing Professionals
Work-related musculoskeletal disorders (WMSDs) are common occupational health issues among nurses resulting from the combined effects of physically demanding tasks and psychosocial pressures. These conditions lead to reduced productivity, increased absenteeism, and strain on healthcare systems. This study aimed to determine the prevalence of WMSDs among nurses in a teaching hospital and to examine demographic, ergonomic, and psychosocial factors associated with their occurrence. A cross-sectional survey was conducted among 45 registered nurses recruited from various wards of a tertiary teaching hospital. Data were collected using a structured self-administered questionnaire, which included the Nordic Musculoskeletal Questionnaire (NMQ) and measures of demographic, ergonomic, and psychosocial variables. Descriptive statistics were applied to summarize characteristics and prevalence, while binary logistic regression was used to identify significant predictors. More than half of the respondents reported WMSDs in the past 12 months, with the ankles/feet (35.6%), neck (33.3%), and lower back (31.1%) most frequently affected. Significant predictors included female (AOR = 6.48, 95% CI: 1.07–39.23), being married (AOR = 5.11, 95% CI: 1.07–24.40), having children (AOR = 5.58, 95% CI: 1.20–25.83), heavy lifting without assistance (AOR = 6.36, 95% CI: 1.31–32.80), high workload (AOR = 5.01, 95% CI: 1.17–21.42), and low supervisory support (AOR = 4.72, 95% CI: 1.04–21.36).In conclusion, WMSDs among nurses are influenced by overlapping personal, ergonomic, and psychosocial risks. Integrated prevention strategies combining ergonomic interventions, such as assistive devices and safe patient-handling, with organizational measures, including workload management, adequate staffing, and supportive supervision, are essential to safeguard nurses’ health and maintain healthcare system resilience. This study contributes novel evidence by integrating both ergonomic and psychosocial predictors within a single framework, providing context-specific insights for developing holistic WMSD prevention strategies among Malaysian nurses.
Musculoskeletal Discomfort among Tahfiz Students in Kelantan: A Cross-Sectional Study Using the Cornell Musculoskeletal Discomfort Questionnaire
Tahfiz educational institutions in Malaysia, especially in the state of Kelantan, show a unique learning practice of sitting cross-legged or kneeling on the floor throughout the learning process. Although this practice aligns with local culture and tradition, it can lead to physical discomfort and musculoskeletal system problems among students. This study aimed to assess the level and distribution of physical discomfort among tahfiz students using the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). A cross-sectional study was conducted among 110 tahfiz students in Kota Bharu, Kelantan, selected through convenience sampling. Data on demographic characteristics and learning posture were collected alongside CMDQ measures covering discomfort location, frequency, severity, and impact on learning. Data were analyzed using SmartPLS 4 (version 4.1.0.9). Of the participants, 80.9% were male and 19.1% were female. The most frequently reported musculoskeletal discomfort was in the lower back (16.9%), followed by the waist (11.2%), the upper back (9.2%), and the lower right leg (8%). SmartPLS analysis revealed that only body weight was significantly associated with the level of musculoskeletal discomfort (β = 0.279, t = 2.214, p = 0.027), indicating that students with higher body weight experienced greater discomfort during prolonged floor-sitting. Lower back discomfort emerged as the most prevalent complaint, highlighting the ergonomic risks associated with traditional learning postures. This study provides novel evidence as one of the first ergonomic assessments conducted in a Malaysian religious education setting using the CMDQ. The findings underscore the need for targeted ergonomic interventions to reduce musculoskeletal discomfort and enhance students’ physical well-being and learning experience in tahfiz institutions.
A Review of the Adoption of Chatbots in E-commerce Platforms for Supporting Customer Relationship Management
While research on artificial intelligence (AI) tools is growing, studies on chatbot integration for customer relationship management (CRM) in E-commerce remain limited. This review addressed this gap by analyzing chatbot features within a unified model. Using a literature review approach, inclusion criteria, and document analysis, 80 scholarly articles published between 2020 and 2025 were synthesized. Chatbots, originating with ELIZA in the 1960s, are categorized as AI-based, rule-based, or voice-activated. They play a substantial role in digital marketing by managing customer expectations, increasing satisfaction, and fostering long-term relationships on major platforms such as Amazon, Shopify, Alibaba, Domino’s Pizza, and eBay. Key functions, including quick responses, personalized interactions, automated replies, and 24/7 support, enhance information quality, customer satisfaction, retention, and loyalty. The review suggesting future research to further explore chatbot integration in E-commerce platforms across industries and contexts.
Preliminary Modelling of Accident and Near Miss Risk Factors Among Gig Delivery Workers in Malaysia
This study explores the risk factors of accidents and near misses among gig delivery motorcyclists in Klang Valley, Malaysia. Using a customized questionnaire, this cross-sectional study collected 235 responses through closed Facebook groups. Sociotechnical aspects such as Rider Behaviour (RB), Rider Safety Awareness (RA), Work Conditions (WC), Environmental Factors (EF), Motorcycle Design and Safety (MDS), and Platform Safety Management (PSM) have been evaluated using an initial 74-item questionnaire. The Partial Least Squares Structural Equation Modelling (PLS-SEM) was utilized resulting a final model with 56 items. The study outcomes have good validity and reliability with Cronbach’s alpha between 0.67 and 0.95, Average Variance Extracted (AVE) > 0.50, and Composite Reliability (CR) > 0.80. Path analysis identified WC (β = -0.337, p < 0.001) and ORU (β = 0.198, p = 0.003) as critical predictors for accident. MDS (β = 0.177, p = 0.026), EF (β = 0.175, p = 0.009), and PSM (β = -0.207, p = 0.001) were significant predictors of near misses. By combining Sociotechnical System Theory and Multiple Causation Theory, the findings showed the complex relationship of various factors in gig delivery workers’ safety. Immediate reforms in platform policies, better road infrastructure, improved motorcycle safety design, and enhancing road users’ safety awareness are much needed. This research contributes to the limited literature on gig worker safety in Malaysia and provides a foundation for future studies and policy recommendations.
Incorporating Anthropometric Measurement and Pressure Distribution Assessment in Car Seat for Fatigue Management: An Exploratory Ergonomic Analysis
An ergonomically designed driver's seat is essential for assuring quality, comfort, and safe driving. Comprehending the influence of anthropometric characteristics on seat-body contact can facilitate the development of comfort-optimized seating systems. This exploratory study examined the relationship between anthropometric dimensions and pressure distribution patterns in drivers deployed within a simulated driving environment. A pressure mapping system (Tactilus®, SPI, USA) was used to capture seat pan pressure profiles, while key anthropometric variables were recorded using an anthropometer. Eleven participants completed the driving posture protocol, and multiple regression analysis was applied to model pressure distribution outcomes. Results revealed a strong, statistically significant positive relationship between buttock-popliteal length and pre-buttock pressure (r = 0.914, p<0.01), with the regression model explaining 81.8% of the variance in pre-buttock pressure. The results emphasize buttock-popliteal length as an important indicator of localized seat pressure, underscoring the necessity for seat designs that accommodate variations in thigh length. These findings establish an ergonomic foundation for incorporating anthropometric-pressure mapping assessments into the development of proactive driver assistance systems, aimed at preventing fatigue occurrences.
Sperm Detection Based on You Only Look Once-Neural Architecture Search (YOLO-NAS)
Computer-Aided Sperm Analysis (CASA) systems are vital for male reproductive diagnostics. However, the CASA systems are prone with detection challenges. Therefore, this study explores You Only Look Once-Neural Architecture Search (YOLO-NAS) for precise, rapid sperm analysis, addressing its underexplored efficacy in this domain. Using the Sperm Videos and Images Analysis (SVIA) dataset (3,590 images, 125,000 objects), XML annotations were converted to YOLO TXT format. YOLO-NAS models (S, M, L) were trained for 40 epochs on Google Colab Pro using AdamW, batch size 32, and learning rates 0.002 (initial 0.0005). The performance was evaluated using mAP@0.50, Recall@0.50, Precision@0.50, and F1 Score@0.50, with an IoU threshold of 50%. The YOLO-NAS-L was most effective for sperm/impurity detection, achieving 0.8163 mAP@0.50 and 0.8203 precision@0.50. Increased model complexity enhances feature extraction, especially for small objects while higher precision may decrease recall. From the outputs, this study has successfully developed and evaluated a YOLO-NAS model for sperm detection. Future works include more epochs, diverse datasets, and offline training in sperm clustering, as well morphology and motility analyses.
Validity and Reliability of the Commuting Safety Management Practices Questionnaire
Commuting accidents are a major contributor to worker fatalities and economic losses in Malaysia. Despite the seriousness of this issue, many organizations lack structured approaches to manage commuting risks. This study aimed to develop and evaluate the validation and reliability of the Commuting Safety Management Practices Questionnaire (CoSMaP-Q), an instrument designed to assess organizational practices in commuting safety. Content validation was carried out using a two-round modified Delphi method involved five road safety experts. In the first round, seven items with Content Validity Index (CVI) scores below 1.0 were revised, removed, or reclassified based on expert input. Five additional items were refined for clarity, one new item was added, and a new domain, “Continual Improvement,” was introduced. In the second round, the revised 28-item questionnaire achieved full consensus, with I-CVI, S-CVI/Ave, and S-CVI/UA scores of 1.00. To assess reliability, a pilot test was conducted with 30 organizations, resulted in an overall Cronbach’s Alpha value of 0.74, indicating acceptable internal consistency for the 28-item instrument. In conclusion, the CoSMaP-Q demonstrates strong content validity and acceptable reliability, offering a practical tool for assessing how organizations manage commuting safety. Its application includes evaluating the efficacy of pre- and post-intervention programs and establishing a foundation for future research in commuting safety management.
Copyright (c) 2025 Journal of Human Centered Technology
Development and Optimization of Wearable Sensors for Vital Signs Monitoring Using the Internet of Things
As the global elderly population grows, there is an increasing need for reliable and easy to use health monitoring tools that support independent living and reduce strain on healthcare system. This study presents the development of a wearable, Internet of Things (IoT) enabled device designed to monitor vital signs and environmental conditions in real time. The system integrated two low-cost sensors: the MAX30100 for tracking blood oxygen saturation (SpO₂) and heart rate, and the BME680 for measuring temperature, humidity, gas resistance (for VOCs), and air pressure. These sensors were connected to ESP8266 microcontroller that transmitted data through Wi-Fi to a mobile dashboard using the Blynk platform. To improve accuracy, especially during motion, signal processing techniques such as a moving average filter were implemented. The device was tested against commercial health monitoring tools and demonstrated strong performance, with an average error of ±2% for SpO₂ and ±5 BPM for heart rate. It also included threshold-based alerts to notify users or caregivers when readings exceed safe limits. This study contributes to the advancement of affordable, remote health monitoring systems by offering a practical solution that combines physiological and environmental sensing in a compact, user-friendly design. Its potential applications include elderly home care, telehealth services, and mobile health clinics.
SmartResume: A Resume Parser Application using Natural Language Processing
In today’s dynamic business environment, Information and Communication Technology (ICT) serves as a pivotal force driving innovation, particularly in human resource management. One notable advancement is the emergence of smart resume applications, which are reshaping traditional recruitment practices. These systems utilize advanced technologies such as artificial intelligence (AI), machine learning, and natural language processing (NLP) to facilitate the automation of resume screening and candidate evaluation processes. This paper presents the development of an Automated Resume Parsing designed to reduce the operational load on human resource professionals. By utilizing NLP techniques, the system extracts key candidate information including name, contact details, educational background, and skills from the resume documents. A custom Named Entity Recognition (NER) model is employed to enhance the accuracy and relevance of extracted data. The model was trained using the SpaCy NLP framework to achieve an overall accuracy of 92.4% and an F1-score of 0.90. The extracted data are presented through an interactive web interface for HR personnel, enabling structured and efficient review of applicant information. The results demonstrate that integrating NLP and machine learning in recruitment systems can significantly enhance automation, consistency, and fairness in candidate evaluation processes. The system's effectiveness was demonstrated through successful extraction and structured presentation of applicant information, aligned with organizational hiring criteria. Beyond technical functionality, this study highlights the potential of such human-centered technologies to enhance decision-making, increase recruitment efficiency, and transform the recruiter-applicant interaction by fostering transparency and fairness in the hiring process.
Honey Hydrogel Patch Assisted by Ultraviolet Exposure: Characterization and Adhesion Analyses
Wound treatment is one of the challenging issues, faced in healthcare despite the development of various advanced technologies. Hydrogels are known to be used in wound treatment and received a lot of attention in the biomedical field due to its ability to store and absorb a significant amount of water which is advantageous for wound healing. The incorporation of honey within the hydrogel increases the resistance towards bacterial infection and induce advanced cell proliferation. However, the fragile structure of honey hydrogel has limited its placement on wounds. The utilization of ultraviolet (UV) light to harden the physical properties of honey hydrogels able to improve and assist the formation of honey hydrogel patches. Therefore, this study aimed to fabricate honey hydrogel patches through UV exposure and evaluate the physio-chemical properties and mucous adhesion strength of the hydrogel patches. The stingless bee honey was incorporated into the matrix of cellulose hydrogel, followed by the exposure of 365 nm UV light at different distances of 1, 2 and 6 cm. The morphology of the hydrogel patches at 1 cm UV exposure was observed to be flatten and possessed more regular structure compared to the other variation of hydrogel patches. The most hydrophilic surface (26.1 ± 0.8)˚ and the greatest ultimate strength (0.52 ± 0.3´10-3 kPa) were also recorded on the similar sample. Therefore, at a shorter UV light exposure distance, distinct hydrogel patches with greater adhesion capability were fabricated for the use in wound healing management.
Design and Implementation of IoT-Based Remote Vital Sign Monitoring System
The ability to save data on physiological parameters in a database has become more important in modern healthcare since it paves the way for analysis, enabling long-term patient monitoring, personalized medication, remote patient monitoring, and collaborative medical decision-making. This study presents the development of an online monitoring system that able to remotely monitor the patient at any time via web server. Using internet of things (IoT) technology, it is possible to develop an online patient monitoring system that read parameters, transmit data to database, store data, and display data. The system utilized the ESP32 microcontroller to collect physiological parameters through various sensors. The sensors for each parameter were selected: AD8232 was used for the Electrocardiogram (ECG), GY-MAX30100 was used for the Heart Rate and Blood Oxygen Saturation (SpO2), and MLX90614 was used for the body temperature. In addition, Google Firebase was adopted as the Web Server for this system, along with the implementation of statistical method. The accuracy test was then performed to check the accuracy of the reading. The physiological data were collected and monitored where the developed device was able to display the collected data in a connection with the database. The development of an online patient monitoring system using IoT technology enables remote monitoring, accurate data storage, and analysis of physiological parameters, leading to improved healthcare outcomes and reduced workload for medical professionals.
Effects of Intermittent Fasting on Body Compositions Using Bioimpedance Analyzers
Advancements in bioimpedance technology have enabled convenient tools such as smart scales for monitoring body composition. This study aimed to evaluate the accuracy of bioimpedance analyses using two types of bioimpedance analyzers (BIAs) during alternate day fasting (ADF). In this study, Bioimpedance Analyzer 450 (a professional-grade device) and the Mi Body Composition Scale (a consumer-grade device) were used to assess changes during a four-week ADF intervention. Six healthy participants, aged 20–30 years underwent ADF: Alternating between 500 kcal intake on fasting days and unrestricted eating on non-fasting days. Body fat percentage, lean mass, and total body water were measured before and after the intervention using both devices. The results showed no statistically significant changes across all parameters (p>0.05); however, the Biodynamics Analyzer detected +0.56% increment in the body fat and −0.56% decrement in the lean mass. While the Mi Scale showed −0.16% changed in the body fat and +0.13% in the lean mass. These discrepancies highlight the higher sensitivity of the professional-grade BIA in capturing subtle physiological changes. The total body water remained stable in both groups. The results suggest that although consumer-grade devices offer accessibility, they may lack the precision needed for detecting minor shifts during dietary interventions. This study emphasizes the importance of using validated tools in clinical and research settings. It also provides preliminary insights into the metabolic effects of ADF, including possible compensatory eating behavior on non-fasting days and minor fluid shifts. These findings underscore the limitations of consumer-grade BIAs in scientific monitoring and support further research to validate their use in long-term health tracking.
Vision Based Screening of Children with Autism Spectrum Disorders Via Deep Learning Approach
Researchers have discerned unique facial characteristics in children with autism, leading to the application of face recognition methods for early diagnosis. Notwithstanding the efficacy of deep learning (DL) models in image classification, issues such as overfitting and limited training datasets continue to exist. This research intends to develop a Convolutional Neural Network (CNN) Deep Learning model for the detection of faces of children with autism. The study employed the Kaggle Autistic Children Facial Dataset to create CNN models through transfer learning, utilizing MobileNet, GoogleNet, and VGG-16. Their performance was assessed using measurements of accuracy, sensitivity, and specificity. The results indicated that MobileNet attained the superior overall performance with accuracy of 0.94, sensitivity of 0.97, and specificity of 0.90, exhibiting robust generalization capabilities. Nonetheless, limits including dataset size and resource limitations were recognized, indicating that future research should prioritize the augmentation of the dataset and the utilization of cloud computing resources for enhanced model training efficiency.
Development of Mobile and Real-Time Vital Signs Monitoring System with Camera Integration
Pre-hospital and emergency department staff play important roles to treat patients where these two departments are correlated to each other. However, there are few misalignments between them such as dissimilar training and clinical abilities, language barriers, and high-risk clinical environments which are characterized by noise, interruption, and time restriction. The objective of this study was to develop an Internet of Things (IoT) based health monitoring system with real-time camera inside an ambulance. With this system, the emergency department can monitor the current patient’s condition in an ambulance while measuring data when paramedics are having difficulties. In conducting the study, the IoT health monitoring system was developed by using ESP32 as the main microprocessor. Few sensors were used including DS18B20, MAX30102, and ESP32-CAM. In addition, the Blynk application was used as the IoT and connected through Wi-Fi, provided by the ESP32. Five respondents were assigned for the data measurement of vital signs specifically heart rate, body temperature, and SpO2 using both standard and proposed monitoring system to calculate average and percentage error. From the results, the percentage error for heart rate was recorded as 21.56 ± 8.40%, body temperature was measured as 3.14 ± 3.41%, and SpO₂ was recorded as 2.13 ± 0.87%. Moreover, during the device construction, the camera functional was delayed for approximately 5 second. In conclusion, integrating real-time video with vital sign data in IoT-based health monitoring systems provides a comprehensive view of a patient's condition, enabling proactive treatment adjustments and better outcomes.
Image Colour Correction of Smartphone-based Imaging Devices for Medical Application: A Literature Review
Smartphone technology has revolutionised the field of digital imaging, especially in healthcare and medical applications. Smartphones equipped with built-in high-resolution imaging capabilities, smartphone-based imaging devices have become cost-effective and powerful tools for health monitoring and diagnosis. However, the colour information of the images captured by different smartphone models varies from one to another, which introduces the colour distortion. These colour distortions may negatively impact image interpretation, diagnosis accuracy, and subsequent deep learning based medical analysis. In this context, the field of colour constancy algorithms has become extremely important in providing accurate and consistent colour representation in digital images. Colour constancy has gained significant scientific interest in the last two decades due to its crucial role in mitigating colour distortions caused by varying image acquisition conditions. These algorithms are essential pre-processing steps to standardise colour representation across different devices and imaging conditions, thus reproducing images that accurately represent real-world scenes. Over the years, many studies have been conducted on the colour constancy topic to improve its effectiveness and accuracy in various domains. This paper reviews significant contributions to colour constancy algorithms over the past 10 years, including the fundamentals of image formation, colour correction algorithms in conventional applications, and colour correction algorithms in the medical field. By offering a comprehensive overview of these key topics, the paper highlights the potential of smartphone-based imaging and colour constancy algorithms in advancing healthcare applications and enhancing diagnostic accuracy.
Adapting The Bruininks-Oseretsky Test of Motor Proficiency (BOT-2) for Young Athletes
The original Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2) is heavily used to detect motor impairments. It has shown limitations when applied to athletic population due to ceiling effects and low score variability among skilled youth. This pilot study aimed to evaluate an adapted version of BOT-2 tailored for young athletes aged 13–17 by incorporating dynamic and time-based components to enhance discrimination and engagement. The adapted BOT-2 included tasks spanning four motor domains: Drawing Lines and Folding Paper (Fine Motor Precision), and Transferring Pennies (Manual Dexterity), modified with time constraints. Descriptive statistics, reliability analysis (Cronbach’s Alpha), ceiling effect inspection, and correlation matrix analysis were conducted to assess the performance variation and internal consistency. The obtained results revealed that specific retained tasks, particularly Drawing Lines and Folding Paper, exhibited strong ceiling effects. In contrast, the time-adapted tasks such as Dribbling and One-Legged Hop showed greater score variability and stronger differentiation. Principal Component Analysis further supported the multifactorial nature of motor proficiency, identifying four distinct components that accounts for 82.23% of total variance. These component groupings reflected agility, balance-related control, and fine motor integration. These findings highlight the potential of sport-specific BOT-2 adaptations and offer insights into optimizing motor proficiency assessments for developmental athletic populations, meriting further investigation.
An UX Evaluation Framework for Islamic Design and Content-Based Digital Therapeutics for Mental Health: A Proposed Approach
With the rise in the occurrence of mental health issues among youths, especially students, an urgent need might have arisen for intervention strategies that respect cultural intricacies. Digital Therapeutic (DTx) quite interestingly serves as a frontier wherein technology is used to give evidence-based support for mental health. In an unfortunate case of neglect, however, there has been a lack of adaptation of these platforms for the considered needs of Muslim users in terms of accommodating Islamic principles and understanding. This paper took a huge jump forward in filling this gap by offering an innovative and thorough User Experience (UX) evaluation framework that holistically integrates Islamic design architecture featuring geometric patterns, calligraphy, and content imbued with spiritual significance. It also offers places for gateways into Islamic teachings, reminders to pray, and guided meditation. The framework looks very closely at and gives guidelines on important elements for Turkish developer to create DTx solutions within an Islamic framework such as usability, accessibility, engagement, and user satisfaction. Having developed through an exhaustive literature review and established frameworks, this model acts as a basic tool for appraising and thereby improving systematically the user experience of digital therapeutics within Islamic contexts. This research contribution attempts to improve the mental health outcomes for Muslim youth through digital health technologies alongside cultural inclusivity to suggest the importance of culturally attuned mental health interventions in today's digital realm.
Extensive Review on Humidity and Moisture on Skin Device Analysis
Moisture and humidity influence the skin properties, particularly in the coefficient of friction (CF). In the process of dexterous manipulation, changing in skin hydration will trigger the mechanical properties of object interface and alter the required minimum force to slip any object on the surface. The moisture level of the skin can be obtained based on two principles either the principle of capacitance or conductance. However, there are only few studies reported on the device to measure skin moisture with limited information on the device capacity. On the other hand, currently available devices in the market are mostly manual reported, and it is difficult to find a complete research study on the device prior to marketing. Other devices that are familiar in the aforementioned fields are normally established based on the lab scale standard in comparison with the developed devices, which are not for universal use. For example home-based monitoring or personal skin care routine. Not to mention, the measurement varies from image sensor and many others. Therefore, in this study, the fundamental concept of a device based on pressure sensor was discussed. The information on the quantitative skill measurement was also provided. In the last part, all the devices that are related to moisture and humidity measurement were tabulated and their performances were highlighted.
Online Platform for Home-Based Medical Device Lifecycle Management: A User-Centric Approach
The growing access to medical devices in home settings that is driven by the advancements in telemedicine and home monitoring technologies, has significantly increased their usage, especially in post-pandemic era. However, home-based medical device users often face challenges in accessing reliable information and support for device operation, maintenance and training. This study addressed these issues by developing a user-friendly online platform using Wix to support individuals managing medical devices in home settings. The platform integrates multiple functionalities, including access to product specification, warranty management, rental request, maintenance request submission, regulatory compliance information and user training resources. The usability testing with 40 diverse participants revealed high satisfaction rates with 92% agreeing that the platform effectively supports medical device management and 96% acknowledge the clarity and value of the educational resources. This study highlights the platform’s potential to empower home users by improving their competency in medical device utilization and improve health outcomes in home-care environments.
Eating Disorder Biomarkers: Macronutrient Regulation of Ghrelin and Leptin
This review examines the potential of ghrelin and leptin as biomarkers in eating disorders by analysing their roles in appetite regulation, energy balance, and eating behaviour. Ghrelin stimulates hunger, whereas leptin suppresses appetite and maintains energy homeostasis. Dysregulation of these hormones, often influenced by high-fat and high-sugar diets, is linked to overeating, obesity, and eating disorders. Imbalances in ghrelin and leptin signalling are associated with conditions such as hyperphagia and binge eating. This review also explores effects of stress and hormonal resistance on these hormones. Understanding how ghrelin and leptin interact with central and peripheral systems offers insights into appetite regulation and potential therapeutic targets for metabolic and psychological disorders.