Biophysiological Signals

The Ground Reaction Forces are generated when walking, specifically when the foot contacts the ground, this information is directly related to the physical characteristics of each person, on the other hand, the growth that has the implementation of artificial intelligence algorithms for the diagnosis or evaluation of both pathologies and rehabilitation that occur in the lower limb are a very large area of opportunity.

 

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Electromyography is useful for those interested in the study of biological signals and their processing, identifying the characteristics of these signals is valuable for the design of prosthetic and robotic systems or in rehabilitation for patients with pathologies of the lower limb. In this case, in particular, the EMG signals of the biceps femoris generated during the gait cycle are intended to characterize the muscle activation signals during walking. For the development of this database, one hundred users were invited to participate.

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This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four di erent motor imagery tasks, namely the imag ination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on di erent days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.

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This dataset is developed to support research on early warning of pilots' cognitive collapse. It encompasses the recordings of 10 participants over 3 separate sessions in the simulated flight experiment paradigm. The experiment aims to elicit the pilots' cognitive collapse state and to early warn of the tipping points, for details you can refer to the paper "An Early Warning Approach for Pilots’ Cognitive Tipping Points Based Multi-modal Signals". Each trial consists of 3 stages that can induce cognitive states at low, medium, and high level with a cognitive collapse.

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The dataset encompasses the recordings of 10 participants over 3 separate sessions in the simulated flight experiment paradigm. The experiment aims to elicit the pilots' cognitive collapse state and to early warn of the tipping points, for details you can refer to the paper "An Early Warning Approach for Pilots’ Cognitive Tipping Points Based Multi-modal Signals". The dataset includes EEG, eye movement, task performance data, and experimenter's recording. It has a total of 30 trials and is approximately 4.7G in size.EEG signal.

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This dataset protocol details the acquisition of a surface electromyography (sEMG) dataset from the Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) muscles of 20 healthy adults, with an equal distribution of 10 male and 10 female participants. The data was collected using the Delsys Trigno wireless EMG system during a 30-second walking session. Proper electrode placement on the specified muscles was ensured for accurate signal capture. Ethical considerations were addressed, with approval from the Institutional Review Board and informed consent from participants.

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Blood pressure (BP) measurement is an indispensable parameter for diagnosing many diseases, e.g., heart attack, stroke, vascular disease, and kidney disease. All these disease sometimes lead to fatal injuries due to the failure of vital human organs. The measurement of BP using BP device has several inaccuracies due to the non-availability of SI traceable calibration systems, which can also meet the criteria of International Organization of Legal Metrology (OIML) particularly OIML R 148 and OIML R 149 guidelines.

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Wearable and low power devices are vulnerable to side-channel attacks, which can retrieve private data (like sensitive data or the private key of a cryptographic algorithm) based on externally measured magnitudes, like power consumption. These attacks have a high dependence on the data being encrypted -- the more variable it is, the more information an attacker will have for performing it. This database contains ECG data measured with a wearable sensorized garment during different levels of activity.

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Dataset for validation of a new magnetic field-based wearable breathing sensor (MAG), which uses the movement of the chest wall as a surrogate measure of respiratory activity. Based on the principle of variation in magnetic field strength with the distance from the source, this system explores Hall effect sensing, paired with a permanent magnet, embedded in a chest strap.

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Dataset for validation of a new magnetic field-based wearable breathing sensor (MAG), which uses the movement of the chest wall as a surrogate measure of respiratory activity. Based on the principle of variation in magnetic field strength with the distance from the source, this system explores Hall effect sensing, paired with a permanent magnet, embedded in a chest strap.

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