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All Journal Bulletin of Electrical Engineering and Informatics Journal of Applied Data Sciences
Amirkhanova, Gulshat
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : Journal of Applied Data Sciences

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Development of a Smart Lung Health Monitoring System Using Sensors and Data Analytics for Early Disease Detection Tyulepberdinova, Gulnur; Kunelbayev, Murat; Amirkhanova, Gulshat; Sakypbekova, Meruyert; Adilzhanova, Saltanat; Tolepberdinova, Ardak
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.857

Abstract

This study introduces a novel multimodal wearable sensor system for real-time monitoring and analysis of respiratory and cardiac activity. The primary objective is to facilitate the early detection of cardiopulmonary abnormalities by integrating electrical (ECG) and acoustic data. A total of 30 participants, aged 25 to 50 years, were involved in controlled breathing experiments, which included deep (1000 ml, 15 breaths/min), moderate (750 ml, 20 breaths/min), and shallow (500 ml, 30 breaths/min) breathing, as well as coughing simulations. Signal processing using a 7th-order polynomial approximation yielded the lowest modeling error at 6.8%, ensuring precise waveform reconstruction. The system demonstrated a clear differentiation of respiratory patterns via Area Under the Curve (AUC) metrics, with average AUC values increasing from 1200 µV·s during shallow breathing to 3200 µV·s during deep breathing. Further analysis of the first derivative of AUC values revealed a strong correlation (r = 0.89) between respiratory volume and ECG amplitude fluctuations, highlighting robust cardiorespiratory coupling. Notably, the system achieved a 92% accuracy in detecting abnormal breathing events, such as shallow breathing and coughing fits. By combining ECG-derived heart rate variability with respiratory data, the system offers a comprehensive assessment of cardiopulmonary interaction. The key contribution of this work lies in its real-time, continuous monitoring capability using a compact wearable form factor, which distinguishes it from existing single-modality systems. This approach represents a significant advancement in non-invasive health monitoring, with strong potential for application in clinical diagnostics and home-based tracking of chronic conditions, such as asthma, COPD, and cardiac dysregulation.
Analyzing the Efficacy of Pose Recognition, YOLOv3, and Deep Learning Techniques for Human Activity Recognition Zhumasheva, Ainur; Mansurova, Madina; Amirkhanova, Gulshat; Tyulepberdinova, Gulnur
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.797

Abstract

The global increase in life expectancy, driven by increased nutrition, healthcare, and living conditions, has resulted in a significant growth in the senior population, notably in Kazakhstan, where the number of people aged 60 and more currently exceeds 2.7 million. This demographic transition poses considerable public health problems, particularly the high prevalence and severity of falls in older persons. Falls are currently the second largest cause of unintentional mortality for more than 87% of the elderly, with 28-34% falling at least once per year. As the worldwide population of people aged 65 and more is predicted to exceed 1.5 billion by 2050, there is an urgent need for precise, real-time fall detection systems. This work uses standardized datasets to conduct a complete evaluation of three fall detection methodologies: posture recognition, YOLOv3-based detection, and deep learning. Deep learning models attained the best accuracy of 92.0% by utilizing their capacity to learn complex spatial-temporal information, but at the cost of increased computing burden and slower inference times (40 ms). YOLOv3 provided competitive accuracy (90.2%) and quicker processing (25 ms), making it suitable for real-time deployment, although with a larger false positive rate. Pose identification, while highly interpretable due to its emphasis on skeletal key points, performed less well in crowded or obscured settings. The findings highlight the possibility for combining the capabilities of each technique to create hybrid systems with adaptive, resource-efficient architectures. Future research should focus on sensor fusion and optimization methodologies to improve accuracy and scalability across a variety of scenarios.
Enhancing Sustainable Biogas Generation Through a Real-Time Digital Twin of a Modular Bioreactor Amirkhanov, Bauyrzhan; Kunelbayev, Murat; Issa, Sabina; Amirkhanova, Gulshat; Nurgazy, Tomiris; Zhumasheva, Ainur; Alipbeki, Ongarbek
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.779

Abstract

This article presents the design and research of a modular horizontal tubular bioreactor for efficient biogas production based on anaerobic digestion technology. The study combines a digital twin implemented in the MATLAB/Simulink environment with a physical bioreactor equipped with a sensor and control system. The developed mathematical model describes the biochemical processes of acidogenesis and methanogenesis, the thermal regime and the sensitivity of the system to key parameters. Numerical modeling and visualization methods were used for the analysis. The experiments were carried out for 30 days at a mesophilic temperature of 37 ° C, repeated three times to increase reliability. The raw material used was a mixture of cattle manure and food waste in a 3:1 ratio, with a total volume of 60 liters. Readings from temperature, pH, and methane sensors were taken every 10 minutes. Experimental data confirmed the high efficiency of the design: removal of up to 70.5% of volatile substances and methane yield of up to 80.5%. Predictive analysis has shown that the digital twin is able to predict the behavior of the system and apply corrective actions in real time. The novelty of the work lies in the integration of a digital twin with a physical bioreactor in real time through industrial communication protocols.
Co-Authors Abduvalova, Ainur Adilzhanova, Saltanat Aidynuly, Azim Alipbeki, Ongarbek Amirkhanov, Bauyrzhan Ishmurzin, Timur Issa, Sabina Kunelbayev, Murat Mansurova, Madina Nurgazy, Tomiris Sakypbekova, Meruyert Tolepberdinova, Ardak Tyulepberdinova, Gulnur Zhumasheva, Ainur