Omkar2118/datasets huggingface dataset:
UAV-PDD2023

UAV-PDD2023: A benchmark dataset for pavement distress detection based on UAV images
JASMIN-CGN
Full name: JASMIN-spraakcorpus

A corpus of about 115 hours of Dutch speech from juveniles, non-native speakers and seniors, consisting of read text and man-machine dialogues.
SUMS
Full name: Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes

Here is SUMS dataset collected by Qinghai University. It is a multi-camera and multi-signal Biosensing dataset collected at high altitudes, which includes 80 synchronized non-contact facial and contact finger videos from 10 subjects during exercise and oxygen recovery scenarios. This dataset captures PPG, respiration rate (RR), and SpO2, and is designed to validate video vitals estimation algorithms and compare facial rPPG with finger cPPG. Our results demonstrate that fusing videos from different positions (face and finger) reduces the mean absolute error (MAE) of SpO2 predictions by 7.6% and 10.6% compared to using only face or only finger data. Additionally, training on multiple indicators such as PPG and blood oxygen simultaneously reduces SpO2 estimation MAE by 17.8%.
We recruited ten participants living on the Qinghai Plateau to collect hypoxia data in a real high-altitude environment. Data collection utilized two Logitech C922 cameras to capture videos of participants’ faces and fingers, along with a CMS50E+ oximeter for measuring PPG signals and blood oxygen levels. Respiratory patterns were monitored using an HKH-11C respiratory sensor. Video recordings were captured at a resolution of 1280x720 pixels, running at 60 frames per second. The PPG signals were recorded at a frequency of 20 Hz, while respiratory waves were captured at 50 Hz. The experiment setup is shown as follows.

Introducing paper: Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes
RLAP
Full name: Remote Learning Affect and Physiologic dataset

The Remote Learning Affect and Physiologic (RLAP) dataset is a dataset applied to remote learning affect and engagement, which contains learners' blood volume pulse (BVP) signals that are highly synchronized. This dataset is suitable for training neural rPPG algorithms.

In recent years, remote physiological sensing has received increasing attention, especially the algorithms using neural networks. However, training a model for extracting physiological signals is not easy. The lack of high-quality datasets limits the development of advanced algorithms, and many models have to add some modules to fight against the "noise" in the dataset. The main noises are: offset caused by no strict synchronization between cameras and physiological signal sensors; data loss during video compression due to device's unsupported or incorrect settings. The RLAP dataset is collected using PhysRecorder to minimize any adverse factors for model training as much as possible. According to PhysBench's experimental results, this is known as the best training set so far.

Introducing paper: Camera-Based HRV Prediction for Remote Learning Environments
ANFC
Full name: A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis

Nailfold capillaroscopy stands as a traditional and classical method for health condition assessment. However, manually employing this method poses challenges as the process of human measurement is not only time-consuming but also hindered by subjective criteria. In this research, we are pioneering the construction of a large dataset comprising 321 capillaroscopy images, 219 videos, and 68 clinic reports. This dataset includes annotations such as segmentations and keypoints from experts, forming a crucial resource for training deep-learning models. Leveraging our dataset, we introduce an end-to-end nailfold capillary analysis pipeline capable of automatically detecting and measuring a comprehensive set of morphological and dynamic features. The experiments demonstrate that our automated analysis algorithms achieve remarkable accuracy, which holds promise for quantitative medical research and pervasive computing in human health. We plan to open-source our datasets and code soon to facilitate further study.

Introducing paper: A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
ALPHA
Full name: ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models

This study concentrates on evaluating the efficacy of Large Language Models (LLMs) in healthcare, with a specific focus on their application in personal anomalous health monitoring. Our research primarily investigates the capabilities of LLMs in interpreting and analyzing physiological data obtained from FDA-approved devices. We conducted an extensive analysis using anomalous physiological data gathered in a simulated low-air-pressure plateau environment. This allowed us to assess the precision and reliability of LLMs in understanding and evaluating users' health status with notable specificity. Our findings reveal that LLMs exhibit exceptional performance in determining medical indicators, including a Mean Absolute Error (MAE) of less than 1 beat per minute for heart rate and less than 1% for oxygen saturation (SpO2). Furthermore, the Mean Absolute Percentage Error (MAPE) for these evaluations remained below 1%, with the overall accuracy of health assessments surpassing 85%. In image analysis tasks, such as interpreting photoplethysmography (PPG) data, our specially adapted GPT models demonstrated remarkable proficiency, achieving less than 1 bpm error in cycle count and 7.28 MAE for heart rate estimation. This study highlights LLMs' dual role as health data analysis tools and pivotal elements in advanced AI health assistants, offering personalized health insights and recommendations within the future health assistant framework.

Introducing paper: ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models
Raw data of baseline experimental results

This dataset contains raw data of baseline experimental results.
Link:
https://drive.google.com/drive/folders/1KBRcPLbM2a8Li3HQ92lGTEfizOGtZ7OM

Introducing paper: From homeostasis to resource sharing: Biologically and economically aligned multi-objective multi-agent AI safety benchmarks