Biophysiological Signals
The dataset contains physiological data collected using a wearable device from 5 children with autism (all males) during interaction sessions with different stimuli. The dataset (QU_Autism_dataset.csv) is related to our investigations of using wearable devices to detect the occurrence of challenging behaviors among children with autism. The study used a wearable device that acquired the acceleration (ACC) (i.e., in X, Y, Z), electrodermal activity (EDA), temperature (TEMP), heart rate (HR), and blood volume pulse (BVP).
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We are presenting electromyography (EMG) and metabolic cost data collected during the optimization of a semi-active hip exoskeleton concept using impedance control at varying walking speeds. We collected 2-minute estimations of metabolic cost across 30 combinations of impedance parameters (stiffness and reference angle) to predict the most metabolically beneficial parameter set.
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The respiratory includes selected files related to a study of physiological changes recorded by wearable devices during physical exercise on a home exercise bike. It is focused on testing the effect of face masks and respirators on blood oxygen concentration, breathing frequency, and the heart rate changes.
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The use of modern Mobile Brain-Body imaging techniques, combined with hyperscanning (simultaneous and synchronous recording of brain activity of multiple participants) has allowed researchers to explore a broad range of different types of social interactions from the neuroengineering perspective. In specific, this approach allows to study such type of interactions under an ecologically valid approach.
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The electrooculography signal is widely used to analyze eye movements, with an emphasis on its use in human-computer interaction. Various techniques based on artificial intelligence have been used for signal processing. These methods require a specific dataset to train algorithms capable of detecting eye movements. We designed an experiment in which horizontal and vertical eye movements were recorded in conjunction with different movement angles.
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EEG consists of collecting information from brain activity in the form of electrical voltage. Epileptic Seizure prediction and detection is a major sought after research nowadays. This dataset contains data from 11 patients of whom seizures are observed in EEG for 2 patients.
The total duration of seizures is 170 seconds. The number of channels is 16 and data is collected at 256Hz sampling rate.
The final dataset files in .csv format contain 87040 rows x 17 columns,
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The design and implementation of an anthropomorphic robotic hand control system for the Bioengineering and Neuroimaging Laboratory LNB of the ESPOL were elaborated. The myoelectric signals were obtained using a bioelectric data acquisition board (CYTON BOARD) using six channels out of 8 available, which had an amplitude of 200 [uV] at a sampling frequency of 250 [Hz].
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This dataset has been employed in the following articles:
https://ieeexplore.ieee.org/document/9682692
https://ieeexplore.ieee.org/document/9871051
https://content.iospress.com/articles/technology-and-health-care/thc202198
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