Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, compiled from 60 participants, recorded in two sessions labelled ’rest’ and ’exercise’. This dataset incorporates a variety of data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. Additionally, these data are enriched with reference values from the NHANES III (Hankinson- 1999) distribution.
The dataset, developed at the National Institute of Neurology and Neurosurgery in Mexico, encapsulates crucial gait biomarkers associated with neurodegenerative diseases. This invaluable compilation serves as a comprehensive resource for understanding and analyzing the distinctive gait patterns exhibited by patients grappling with neurological disorders. By delving into these intricate biomarkers, researchers gain insights into the nuanced manifestations of conditions impacting the nervous system.
We obtained this dataset as part of a project to generate a realistic speed profile on a trip specified by GPS coordinates. Specifically, we focused on generating the speed profile for a passenger car traveling on an unfamiliar route, i.e., a route the machine-learning model has yet to see.
The dataset contains 5973 rides of five different passenger cars, with a total length of 9049.3 km. The data was collected during 2021 in the Czech Republic and includes municipal and non-municipal trips.
Egocentric video and Inertial sensor data Kitchen activity dataset is the first V-S-S interaction-focused dataset for the ego-HAR task.
It consists of sequences of everyday kitchen activities involving rich interactions among the subject's body, object, and environment.
The evaluations are modified with the feed back mechanism based on optimal model in Large Scale Group Decision Making (LSGDM) usually, the intelligent decision making cannot be achieved with end-to-end. The application of LSGDM is limited, such as the customer evaluation to sales factors, the most customers would not modify the provided evaluations. A novel method combining Conditional Variational Auto-Encoder (CVAE) and self attention mechanism is developed to conduct the intelligent decision making with end-to-end.
Please cite the following paper when using this dataset:
N. Thakur, S. Cui, K. A. Patel, N. Azizi, V. Knieling, C. Han, A. Poon, and R. Shah, “Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior,” Journal of Computation, Vol. 11, Issue. 11, Article. 234, Nov. 2023, DOI: http://dx.doi.org/10.3390/computation11110234
Abstract
The paper presented by Samar Mahmoud; and Yasmine Arafaf et, al a novel dataset called the "Abnormal High-Density Crowd Dataset," addresses the challenge of anomaly detection in crowded environments, particularly focusing on high-density crowds—an area that has received limited exploration in computer vision and crowd behaviour understanding. The dataset is introduced with considerations for privacy, annotation accuracy, and preprocessing.
The dataset introduces a novel physics-embedded deep learning neural network for accelerating traditional FWI algorithms, thereby reducing the required imaging time while overcoming the challenge of needing a high-quality initial model for traditional FWI inversion. The provided dataset includes training, validation, and testing sets, along with executable files related to PEN-FWI network training and validation.
