LATEST PROJECTS
The Sehat Bot is a state-of-the-art telepresence robot designed for remote patient monitoring. It seamlessly integrates three crucial systems - Remote Patient Monitoring (RPM), Live Video Conference, and Navigation and Mobility - to provide an unparalleled healthcare experience.
The Remote Patient Monitoring (RPM) system enables real-time sharing of the patient's vital signs with remote doctors through a user-friendly web interface. This system plays a critical role in ensuring the continuous monitoring of the patient's health status.
The Live Video Conference system establishes a high-quality, low-latency video call between the doctor and the patient, enabling them to communicate and interact in real-time. This system is crucial in providing remote consultation services to patients in remote and underserved areas.
Finally, the Navigation and Mobility system provides the robot with the ability to navigate its surroundings and detect obstacles using a camera setup and computer vision. The doctor can control the robot's movements remotely, ensuring that they have an optimal view of the patient and their surroundings. read more
2019 SHU Video.mp4As a biomedical engineer, I have dedicated my career to leveling the playing field for individuals with disabilities. My passion for this field was driven by a personal experience with a loved one who suffered from right-side hemiplegia after a stroke. As someone with both technical knowledge and a personal connection to the issue, I felt a strong sense of responsibility to use my skills and knowledge to help my loved one in their recovery journey.
In 2019, we embarked on a project to design and create a hand rehabilitation glove that would aid stroke patients in their recovery process. The result of this project was the Stroke Rehabilitation Glove, a device consisting of two components: a glove that mimics the movements of the patient's unaffected hand and a 3D-modeled exoskeleton for the affected hand, which is controlled by an Arduino microcontroller. The exoskeleton was designed to mimic the movements of the flex sensors attached to the unaffected hand glove, providing patients with an effective tool for rehabilitation.
As the creator of the Stroke Rehabilitation Glove, we are proud to offer an affordable device that leverages cutting-edge technology to help stroke patients regain their hand functionality. I am confident that this device has the potential to make a meaningful difference in the lives of those who have experienced a stroke and I am excited to share my work with the world.
The "Breast Cancer Detection using ML" project is an innovative system designed to detect and classify breast cancer as malignant or benign with a high degree of accuracy. The system was developed using Python programming language and utilizes three powerful machine learning algorithms, KNN, Decision Tree, and Logistic Regression Classifiers to achieve an accuracy rate of 98.6%.
This project was programmed on Google Colab, a cloud-based platform for machine learning and data science, and is available as an open-source project for the public to access and use. The goal of this project was to create a tool that would make breast cancer detection more efficient, accurate, and accessible, and the high accuracy rate achieved demonstrates the success of this goal. The dataset for this project was taken from Kaggle and can be accessed here.
As an AI system for breast cancer detection, "Breast Cancer Detection using ML" has the potential to revolutionize the healthcare industry and make a meaningful difference in the lives of individuals affected by breast cancer. The open-source nature of this project encourages collaboration and the continued development of this technology, with the ultimate aim of improving the accuracy and availability of breast cancer detection worldwide. read more
The "Gestational Diabetes Mellitus Detection Using ML" project is a sophisticated system designed for the early detection and classification of Gestational Diabetes Mellitus (GDM). The system was developed using Python programming language and utilizes three powerful machine learning algorithms, KNN, Decision Tree, and Logistic Regression Classifiers. The KNN machine learning algorithm produced results with an accuracy rate of 81%.
This project was implemented on a cloud-based platform, providing broad accessibility and collaboration opportunities. As an open-source project, the "Gestational Diabetes Mellitus Detection Using ML" system offers a valuable resource for advancing medical technology and improving the lives of individuals affected by GDM. The dataset for this project was taken from Kaggle and can be accessed here.
The accurate and reliable results provided by this project serve as a valuable tool for medical professionals in the early detection and treatment of GDM. The "Gestational Diabetes Mellitus Detection Using ML" project is a testament to the commitment of its creators to advancing medical technology and promoting the well-being of individuals affected by GDM.
As a runner, I recognized the need for prosthetic feet that can withstand the stress and force exerted during high-performance physical activities.
To address this need, I utilized Fusion 360, a state-of-the-art 3D modeling software, to design and develop a prosthetic foot that could support high-performance physical activities. I conducted simulations under various stress, weather, and track conditions to ensure the reliability and strength of the prosthetic foot.
The results of my project demonstrate the feasibility of utilizing 3D printing technology to create customized prosthetic limbs that can support high-performance physical activities. I am proud to have made a valuable contribution to the field of prosthetic design and the well-being of individuals with prosthetic limbs.
As a passionate and dedicated athlete and an engineer, I am committed to exploring new and innovative ways to improve the quality of life for individuals with prosthetic limbs.
Fabricated biodegradable bioplastic using renewable sources like cornstarch, vinegar, glycerol, and water. Bioplastics are a sustainable alternative to conventional fossil fuel-based plastics, which have a detrimental impact on the environment and take hundreds of years to degrade. The bioplastic developed in this project degrades into water, carbon dioxide, and biomass within a matter of months, leaving a smaller carbon footprint and reducing pollution.
The process of making bioplastic involved the use of cornstarch, vinegar, glycerol, and water, which were mixed to form a homogenous solution. The solution was then heated to a specific temperature and allowed to cool, resulting in a solid bioplastic material. The bioplastic was then subjected to various physical and mechanical tests to determine its strength and durability.
In addition to its applications as a replacement for conventional plastic, bioplastic also has a wide range of applications in the field of biomedical engineering, including tissue engineering, gene delivery, drug delivery, implants, and biocontrol agents. This project highlights the potential of biodegradable bioplastic as a sustainable and environmentally-friendly alternative to conventional plastic.