Monday 17 March 2025
1. Noninvasive System for Depression Assessment
Abstract
This final year project focuses on the design and development of a Noninvasive System for Depression Assessment aimed at providing an accessible, efficient, and user-friendly tool for early detection and monitoring of depression. Depression is a prevalent mental health disorder, yet many individuals remain undiagnosed due to stigma, lack of access to healthcare, or inadequate screening methods. This project addresses these challenges by leveraging noninvasive technologies, such as facial expression analysis, voice pattern recognition, and physiological signal monitoring (e.g., heart rate variability), to assess depressive symptoms. The system integrates machine learning algorithms to analyze behavioral and physiological data, providing a comprehensive assessment of an individual's mental state. By utilizing noninvasive sensors and wearable devices, the system ensures minimal discomfort and maximum convenience for users. The project also includes a user-friendly interface for both patients and healthcare providers, enabling seamless data interpretation and actionable insights. Preliminary testing has demonstrated promising results in accurately identifying depressive symptoms, with a focus on improving early intervention and personalized care. This project not only contributes to the field of mental health technology but also highlights the potential of noninvasive methods in revolutionizing depression assessment. Future work will focus on enhancing accuracy, scalability, and integration with existing healthcare systems to support widespread adoption.
Keywords: Depression assessment, noninvasive technology, machine learning, mental health, wearable devices.
2. Design and Development of an IoT-Enabled Smart Hospital Bed for Enhanced Patient Monitoring and Care
Abstract
The rapid evolution of smart healthcare technologies has paved the way for innovative solutions to improve patient care and hospital efficiency. This project focuses on the design and development of an IoT-enabled smart hospital bed, aimed at enhancing patient monitoring, safety, and comfort. The proposed smart bed integrates advanced sensors, real-time data analytics, and IoT connectivity to provide a comprehensive healthcare solution. Key features include pressure sensors to detect and prevent bedsores, weight sensors to monitor patient mobility, and automated bed adjustment mechanisms to support patient positioning and reduce the risk of falls. The bed is equipped with wireless communication modules that transmit real-time patient data, such as heart rate, respiratory rate, and body temperature, to a centralized monitoring system accessible to healthcare providers. This enables timely interventions and reduces the workload on medical staff. Additionally, the system includes an alert mechanism to notify caregivers of critical changes in patient conditions or bed status. The IoT framework allows for remote monitoring, ensuring continuous care even when healthcare providers are not physically present. The project also addresses challenges such as data security, system reliability, and cost-effectiveness to ensure practical implementation in hospital settings. By combining cutting-edge technology with user-centric design, this smart hospital bed aims to improve patient outcomes, reduce hospital stays, and optimize resource utilization. The proposed solution represents a significant step toward modernizing healthcare infrastructure, offering a scalable and efficient approach to patient care in the era of smart hospitals.
Keywords: IoT-enabled smart hospital bed, patient monitoring, healthcare technology, smart healthcare systems, remote patient care, IoT in healthcare, real-time monitoring, patient safety, hospital bed design, medical IoT, smart sensors, healthcare innovation, patient care enhancement, wireless monitoring, health data analytics.
3. Design and Implementation of a Quantum-Enhanced Cyber-Physical System for Smart Biomedical Devices Using Machine Learning
Abstract
The rapid advancement of technology in healthcare has created opportunities for innovative solutions that integrate cyber-physical systems (CPS), quantum computing, and machine learning (ML) to revolutionize biomedical devices. This project proposes the design and implementation of a quantum-enhanced CPS for smart biomedical devices, leveraging ML to improve functionality, accuracy, and patient outcomes. Biomedical devices, such as wearable health monitors, insulin pumps, and pacemakers, are inherently CPS, combining physical components with computational algorithms to monitor and regulate physiological processes. By incorporating quantum computing, these devices can achieve unparalleled computational power, enabling real-time processing of complex biological data and optimization of treatment protocols. Quantum algorithms can address computationally intensive tasks such as protein folding, drug discovery, and genetic analysis, which are beyond the capabilities of classical systems. Additionally, ML algorithms enhance the predictive and adaptive capabilities of biomedical devices, allowing them to learn from patient data, detect anomalies, and personalize treatments. For instance, ML models can predict cardiac arrhythmias or glucose fluctuations, enabling proactive interventions. The proposed system also addresses critical challenges such as data security, energy efficiency, and scalability, ensuring robust and reliable operation in healthcare environments. This interdisciplinary approach not only improves patient outcomes but also paves the way for next-generation biomedical devices that are smarter, faster, and more adaptive. By bridging the gap between physical systems, advanced computing, and intelligent algorithms, this project contributes to the evolution of precision medicine and the development of sustainable healthcare solutions, aligning with global efforts to harness technology for improved well-being. The proposed system represents a significant step toward transforming healthcare delivery through innovation and integration.
Keywords: Quantum-enhanced systems, cyber-physical systems, smart biomedical devices, machine learning in healthcare, quantum computing, biomedical engineering, IoT in healthcare, smart health monitoring, quantum algorithms, medical device innovation, real-time health analytics, AI-driven healthcare, quantum machine learning, biomedical signal processing, advanced healthcare technology.
Generative Technology: DeepSeek
December 30-31, 2024
Hamdard University BME alumni from abroad presented their research work in poster form at the 26th IEEE INMIC conference, organized by SHU. I co-chaired a session with Dr. Raja Masood Larik of NED UET on the second day of the conferece.
10 March 2025
The CPS course completed after about three months of learning. This is a very good and hands-on course. The resource person was Prof. Ricardo Sanfelice, Chair at the Department of Electrical and Computer Engineering, University of California at Santa Cruz. In order to complete the course, I have to complete another course to refresh MATLAB and to gain insights of Simulink. Thanks to HEC Digital Learning & Skills Enrichment Initiative Pakistan and Hamdard University for providing the opportunity to learn from the top of line academicians and researchers, aka, subject experts.
20th February 2025
Hamdard University Biomedical Engineering students: Ms. Ramsha Qayyum, Ms. Khadija Razi, and Ms. Fakiha Arshad awarded Gold Category for the oral presentation of accepted research paper entitled, "Skin Disease Detection through Hybrid Deep Learning Method," at 40th All Pakistan IEEEP Students' Seminar, held at Salim Habib University, Karachi, on 20th February 2025. The research work was supervised by Dr. Muhammad Faris, Assistant Professor BME.
25th February 2025
The 8th All Pakistan DUHS-DICE Health Innovation Exhibition 2025 was organized collaboratively by the Dow University of Health Sciences and DICE Foundation, USA. The event was held at DUHS Ojha Campus on 25th February 2025.
Tuesday 28th January 2025
Thursday 30th January 2025
Dr. Tariq Javid attended a one-day session entitled, Masterclass in Health Information Systems: Global Perspective." The session was organized by the Department of Biomedical Engineering, NED University of Engineering and Technology, LEJ Campus, Karachi; and held on Thursday 30th January 2025. The session started with a welcome speech by Prof. Dr. Eraj Humayun Mirza, Chair BME, NED UET, and a brief speech by Prof. Dr. Saad Ahmed Qazi, Meritorious Prof. & Dean Faculty of Electrical and Computer Engineering, NED UET. Prof. Dr. Maryati Mohd Yusof from Universiti Kebangsaan Malaysia was the resource person. The session was about the use of three life cycles: Plan-Do-Act-Check (PDAC) - a quality management methodology, Software Development Life Cycle (SDLC), and Business Process Management (BPM); as an integrated tool for identifying the errors and addressing the root cause. The first half was on the introduction of the health information system (HIS). After the prayer break and lunch, the second session - activity-based - was held. The session concluded by awarding certificates to participants and a group photograph.
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