Wearable Sensing

These CSV files contain the wearable sensor data (RIP and IMU )collected from forty subjects during multiple cigarette smoking sessions.

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534 Views

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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3990 Views

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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10326 Views

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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309 Views

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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832 Views

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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597 Views

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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1680 Views

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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1567 Views

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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763 Views

In an aging population, the demand for nurse workers increases to care for elders. Helping nurse workers make their work more efficient, will help increase elders quality of life, as the nurses can focus their efforts on care activities instead of other activities such as documentation.
Activity Recognition can be used for this goal. If we can recognize what activity a nurse is engaged in, we can partially automate documentation process to reduce time spent on this task, monitor care plan compliance to assure that all care activities have been done for each elder, among others.

Last Updated On: 
Fri, 12/06/2019 - 03:40

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