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12 results for “biosignals”
MAMEM Phase I Dataset - A dataset for multimodal human-computer interaction using biosignals and eye tracking information
<p>This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.</p>
Understanding Fatigue Through Biosignals: A Comprehensive Dataset
<p>Fatigue is a multifaceted construct, that represents an important part of human experience. The two main aspects of fatigue are the mental one and the physical one, that often intertwine, intensifying their collective impact on daily life and overall well-being.<br>To soften this impact, understanding and quantifying fatigue is crucial. Physiological data play a pivotal role in the comprehension of fatigue, allowing a precious insight into the level and type of fatigue experienced.</p> <p>The MePhy dataset includes physiological data gathered while inducing different types of fatigue conditions, in particular mental and physical fatigue. We collected various biosignals closely associated with fatigue (ECG, EDA, EMG and Eye Blinking). Test participants endured a four-part experiment that aimed to elicit mental fatigue, physical fatigue and a combination of both. </p> <p>The main folder contains:</p> <ul> <li>MePhy Dataset folder, which contains the dataset;</li> <li>ReadMe.pdf, which provides more informations about the dataset;</li> <li>Mental Fatigue Inducing Test folder, which includes the HTML application used to simulate mental fatigue in the test participants. </li> </ul> <p> </p> <p>A more in depth description of the MePhy dataset can be found in the following paper <a href="https://doi.org/10.1145/3610977.3637485">https://doi.org/10.1145/3610977.3637485</a>.</p> <p><strong><em>Marta Gabbi, Luca Cornia, Valeria Villani, and Lorenzo Sabattini (2024) Understanding Fatigue Through Biosignals: A Comprehensive Dataset. In Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24).</em></strong></p> <p> </p>
ScientISST MOVE: Annotated Wearable Multimodal Biosignals recorded during Everyday Life Activities in Naturalistic Environments
<p>A multi-modality, multi-activity, and multi-subject dataset of wearable biosignals.</p><p><strong>Modalities:</strong> ECG, EMG, EDA, PPG, ACC, TEMP</p><p><strong>Main Activities:</strong> Lift object, Greet people, Gesticulate while talking, Jumping, Walking, and Running</p><p><strong>Cohort: </strong>17 subjects (10 male, 7 female); median age: 24</p><p><strong>Devices: </strong>2x ScientISST Core + 1x Empatica E4</p><p><strong>Body Locations: </strong>Chest, Abdomen, Left bicep, wrist and index finger</p><p>No filter has been applied to the signals, but the correct transfer functions were applied, so the data is given in relevant unis (mV, uS, g, ºC).</p><p>For more information on background, methods and the acquisition protocol, refer to <a href="https://doi.org/10.13026/0ppk-ha30">https://doi.org/10.13026/0ppk-ha30</a>.</p><p>========</p><p>In this repository, there are two formats available:</p><h4><strong>a) LTBio's Biosignal files. Should be open like:</strong></h4><p><i>x = Biosignal.load(path)</i></p><p>LTBio Package: <a href="https://pypi.org/project/LongTermBiosignals/">https://pypi.org/project/LongTermBiosignals/</a></p><p>Under the directory <i>biosignal</i>, the following tree structure is found: <i>subject/x.biosignal</i>, where <i>subject</i> is the subject's code, and <i>x</i> is any of the following {<i> acc_chest, acc_wrist, ecg, eda, emg, ppg, temp</i> }. Each file includes the signals recorded from every sensor that acquires the modality after which the file is named, independently of the device.</p><p>Channels, activities and time intervals can be easily indexed with the index operator <i>[]</i> ( <a href="https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html">https://ltbio.readthedocs.io/en/latest/learn/basic/ltbio101.html</a> ).</p><p>A sneak peak of the signals can also be quickly plotted with: <i>x.preview.plot()</i></p><p>Any Biosignal can be easily converted to NumPy arrays or DataFrames, if needed.</p><h4><strong>b) CSV files. Can be open like:</strong></h4><p><i>x = pandas.read_csv(path)</i></p><p>Pandas Package: <a href="https://pypi.org/project/pandas/">https://pypi.org/project/pandas/</a></p><p>These files can be found under the directory <i>csv</i>, named as <i>subject.csv</i>, where <i>subject</i> is the subject's code. There is only one file per subject, containing their full session and all biosignal modalities. When read as tables, the time axis is in the first column, each sensor is in one of the middle columns, and the activity labels are in the last column. In each row are the samples of each sensor, if any, at each timestamp. At any given timestamp, if there is no sample for a sensor, it means the acquisition was interrupted for that sensor, which happens between activities, and sometimes for short periods during the running activity. Also in each row, on the last column, is one or more activity labels, if an activity was taking place at that timestamp. If there are multiple annotations, the labels are separated by vertical bars (e.g '<i>run | sprint</i>'). If there are no annotations, the column is empty for that timestamp.</p><p>In order to provide a tabular format with sensors with different sampling frequencies, the sensors with sampling frequency lower than 500 Hz were upsampled to 500 Hz. This way, the tables are regularly sampled, i.e., there is a row every 2 ms. If a sensor was not acquiring at a given timestamp, the corresponding cell with be empty. So, not only the segments with samples are regularly sampled, but the interruptions are also discretised. This means that if, after an interruption, a sensor starts acquiring at a non regular timestamp, the first sample will be written on the previous or the following timestamp, by half-up rounding. Naturally, this process cumulatively introduces lags in the table, some of which cancel out. Each individual lag is no longer than half the sampling period (1 ms), hence negligible. The cumulative lags are no longer than 48 ms for all subjects, which is also negligible. Nevertheless, only the LBio's Biosignal format preserves the exact original timestamps (10E-6 precision) of all samples and the original sampling frequencies.</p><p>================</p><p>Both include annotations of the activities, however LTBio bio signal files have better time resolution and include clinical data and demographic data as well.</p>
Data set for comparison between two biosignals acquisition systems – BioNomadix and BITalino
<p>The data was collected in order to compare quality of the signal acquired by two devices – BITalino (Da Silva, Guerreiro, Lourenço, Fred, & Martins, 2014) and BioNomadix (BIOPAC Systems Inc., Goleta, CA, USA).</p>
Biomarker Signature of Stroke Aetiology Study: The BIOSIGNAL-Study
ClinicalTrials.gov study NCT02274727. IPD Sharing: Not stated. Countries: 4. Publications: 2.
Blood Pressure Estimation Using Noninvasive Biosignals During Pediatric Anesthesia
ClinicalTrials.gov study NCT04087070. IPD Sharing: NO. Countries: 1. Publications: 1.
Cardiac Anesthesia Registry Including Biosignal Database for Prognostication of Postoperative Renal Outcome
ClinicalTrials.gov study NCT06581289. IPD Sharing: NO. Countries: 1. Publications: 0.
Mindfulness Training and Respiration Biosignal Feedback - Study 2
ClinicalTrials.gov study NCT07136948. IPD Sharing: YES. Countries: 1. Publications: 0.
Biosignals by Wearable Devices in Hypothyroidism
ClinicalTrials.gov study NCT04332770. IPD Sharing: NO. Countries: 1. Publications: 0.
Mindfulness Training and Respiration Biosignal Feedback - Study 1
ClinicalTrials.gov study NCT06625697. IPD Sharing: YES. Countries: 1. Publications: 0.
Prediction of the Spontaneous Breathing Test Success Using Biosignal and Biomarker in Critical Care Unit by a Machine Learning Approach
ClinicalTrials.gov study NCT05886803. IPD Sharing: NO. Countries: 1. Publications: 0.
Biosignals by Wearables in Thyroid Dysfunction
ClinicalTrials.gov study NCT04806269. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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