Wearable Sensing
BS-HMS-Dataset is a dataset of the users' brainwave signals and the corresponding hand movement signals from a large number of volunteer participants. The dataset has two parts; (1) Neurosky based Dataset (collected over several months in 2016 from 32 volunteer participants), and (2) Emotiv based Dataset (collected from 27 volunteer participants over several months in 2019).
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We provide a large benchmark dataset consisting of about: 3.5 million keystroke events; 57.1 million data-points for accelerometer and gyroscope each; and 1.7 million data-points for swipes. Data was collected between April 2017 and June 2017 after the required IRB approval. Data from 117 participants, in a session lasting between 2 to 2.5 hours each, performing multiple activities such as: typing (free and fixed text), gait (walking, upstairs and downstairs) and swiping activities while using desktop, phone and tablet is shared.
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This dataset comprises supplementary materials to accompany "Flexible Iridium Oxide based pH sensor Integrated with Inductively Coupled Wireless Transmission System for Wearable Applications" by Marsh et al. Included are processing details and images, collected calibration data, and analysis procedures.
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This study was conducted in Mayaguez – Puerto Rico, and an area of around 18 Km2 was covered, which were determined using the following classification of places:
· Main Avenues: Wide public ways that has hospitals, vegetation, buildings, on either side
· Open Places: Mall parking lots and public plazas
· Streets & Roads: Dense residential and commercial areas on both sides
Vendor Equipment Description
KEYSIGHT® N9343C Handheld Spectrum Analyzer
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This database contains the results of an experiment were healthy subjects played 5 trials of a rehabilitation-based VR game, to experience either difficulty variations or presence variations.
Colected results are demogrpahic information, emotional emotions after each trial and electrophysiological signals during all 5 trials.
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Multi-modal Exercises Dataset is a multi- sensor, multi-modal dataset, implemented to benchmark Human Activity Recognition(HAR) and Multi-modal Fusion algorithms. Collection of this dataset was inspired by the need for recognising and evaluating quality of exercise performance to support patients with Musculoskeletal Disorders(MSD).The MEx Dataset contains data from 25 people recorded with four sensors, 2 accelerometers, a pressure mat and a depth camera.
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A new wearable sensing system of respiration rate based on a piezoresistive FlexiForce sensor has been developed. The 3D casing of the system has been designed and printed with a 3D printer. The design of the casing has a direct impact on sensor accuracy. The casing was designed to house all elements of the sensing system in a compact way: microcontroller, battery, conditioning circuit, Bluetooth module and battery charger. The sensing system was validated with twenty-one subjects using a metronome as a reference.
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The data is obtained from electrocardiography, using flexible electrode, Ag/AgCl electrode and Metal Clamp electrode of a femal subject, age 22 years old.
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