Biomedical and Health Sciences
Seismocardiography (SCG) Signal Processing Dataset is a comprehensive collection of data samples to simulate the real-world application of the advanced technique in cardiac health monitoring. The dataset has been collected in different medical conditions of the patient in a real-time medical environment at varying timestamps. This dataset contains 1,000 samples collected over a period from 10 November 2023 to 10 January 2024, providing a robust timeframe in various conditions.
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658 Views
Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation.
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1247 Views
While deep learning has catalyzed breakthroughs across numerous domains, its broader adoption in clinical settings is inhibited by the costly and time-intensive nature of data acquisition and annotation. To further facilitate medical machine learning, we present an ultrasound dataset of 10,223 Brightness-mode (B-mode) images consisting of sagittal slices of porcine spinal cords (N=25) before and after a contusion injury.
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144 Views
Data sources of MKG with structured medical knowledge database and unstructured scientific publications
Source Type
Name
Related researches
Structured medical knowledge database
KEGG
[20]
SIDER
[21]
ICD-10
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190 Views
This dataset named "Chest X-ray images for Multiple diseases" is a medium sized dataset we collected and produced in 2024 from various sources to predict various Chest-X-ray diseases using Deep learning techniques, primarily from Radiopaedia.org, coronacases.org, Kaggle contains 1000 images for each of the disease namely TB,pneumonia,Covid-19,Normal. This dataset is designed to support the evaluation and development of algorithms to predict various chest x-ray diseases.
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1405 Views
This project contains files related to the paper, "SynergyFF: a method for simulating crouch gait using muscle synergy feedforward control as a CPG".
Our study developed a predictive simulation method using muscle-synergy feedforward control as the CPG, and explored the advantages provided by this method for simulating plantarflexor muscle weakness gait.
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32 Views
Brain-Computer Interface (BCI) is a technology that enables direct communication between the brain and external devices, typically by interpreting neural signals. BCI-based solutions for neurodegenerative disorders need datasets with patients’ native languages. However, research in BCI lacks insufficient language-specific datasets, as seen in Odia, spoken by 35-40 million individuals in India. To address this gap, we developed an Electroencephalograph (EEG) based BCI dataset featuring EEG signal samples of commonly spoken Odia words.
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362 Views
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128 Views
This dataset consists of inertial, force, color, and LiDAR data collected from a novel sensor system. The system comprises three Inertial Measurement Units (IMUs) positioned on the waist and atop each foot, a color sensor on each outer foot, a LiDAR on the back of each shank, and a custom Force-Sensing Resistor (FSR) insole featuring 13 FSRs in each shoe. 20 participants wore this sensor system whilst performing 38 combinations of 11 activities on 9 different terrains, totaling over 7.8 hours of data.
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364 Views