Biophysiological Signals

Driving practices while HR physiology and pre- and post-EDA were acquired. Stress levels are also rated on a 1-5 scale. The gamer's steering wheel angle, pedals, and steering wheel buttons associated with the driving activity are tracked every 10 msec. The normalized data were stored in Figure 1 in the .xlsx file. Using the Balanced Latin Square method, participants develop each level to avoid level learning when designing experiments with multiple conditions.

 

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We present our FEEL (Force, EEG and Emotion-Labelled) dataset, a collection of brain activity, and keypress force data, labelled with self-reported emotion during tense videogame play (N=16).

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847 Views

Five users aged 23, 25, 31, 42, and 46 participated in the experiment. The users sat comfortably in a chair. A green LED of 1 cm diameter was placed at a distance of about 1 meter from a person's eyes. EEG signals were recorded using g.USBAmp with 16 active electrodes. The users were stimulated with flickering LED lights with frequencies: 5 Hz, 6 Hz, 7 Hz, and 8 Hz. The stimulation lasted 30 seconds. The recorded signals were divided into the data used for training, the first 20 seconds, and the data used for testing, the next 10 seconds, for each signal.

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The Research Paper "Detection of Bicep Form Using Myoware and Machine Learning" based on the novel dataset has been recently accepted in September 2022 and is being published in SCOPUS Indexed SPRINGER Book Series “Lecture Notes in Networks and Systems”

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1013 Views

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder with resting tremor (RT) being it's most common motor symptom. This study aimed to determine the features of wrist velocity and acceleration that can be used as objective, reliable, and sensitive detectors of RT. Forty-five healthy young adults imitated RT in both hands after observing a video of RT in a person with PD. Inertial measurement units placed on both wrists recorded the linear acceleration and angular velocity, which were used to calculate linear velocity and angular acceleration.

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This data set contains information on cardiopulmonary signals that were recorded simultaneously. The signals are separated into two folders, one titled heart sounds and the other lung sounds. In addition, two matlab programs are included, one with which the signals can be recorded and another to make graphs in time and frequency. It also has a pdf file that details the nomenclature of the signals.

This data set can be useful for various signal processing algorithms: filtering, PCA, LDA, ICA, CNN, etc.

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This dataset contains information about published papers on how biological signals (ECG, EEG, EDA and MG + eye-tracking) are being used and collected in the field of video games. This dataset reflects the information published including the choice of signals, the devices used to collect them (e.g., wearables), the purposes for which they are collected, and the main results reported from their use.

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This dataset containg EMG data recorded with a sample rate of 1kHz. Data was collected from  the brachioradialis, flexor carpi ulnaris and common extensor digitorum muscles.

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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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