Wearable Sensing

This dataset presents the measurements corresponding to the article "Validation of a Velostat-Based Pressure Sensitive Mat for Center of Pressure Measurements". You will find the data corresponding to an affordable commercial mat, a Velostat-based mat prototype, and a commercial force platform. The results obtained in the above-mentioned article can be reproduced with them.

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303 Views

Stair ambulation of 21 healthy subjects collected in Monash University for validating gait phase detection algorithms. raw text files and matlab data files are included. 

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47 Views

    

Dataset used for "A Machine Learning Approach for Wi-Fi RTT Ranging" paper (ION ITM 2019). The dataset includes almost 30,000 Wi-Fi RTT (FTM) raw channel measurements from real-life client and access points, from an office environment. This data can be used for Time of Arrival (ToA), ranging, positioning, navigation and other types of research in Wi-Fi indoor location. The zip file includes a README file, a CSV file with the dataset and several Matlab functions to help the user plot the data and demonstrate how to estimate the range.

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1660 Views

This dataset contains cardiovascular data recorded during progressive exsanguination in a porcine model of hemorrhage. Both wearable and catheter-based sensors were used to capture cardiovascular function; the wearable system contained a fusion of ECG, SCG, and PPG sensors while the catheter-based system was comprised of pressure catheters in the aortic arch, femoral artery, and right and left atria via a Swan-Ganz catheter.

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724 Views

This repository introduces a novel dataset for the classification of Chronic Obstructive Pulmonary Disease (COPD) patients and Healthy Controls. The Exasens dataset includes demographic information on 4 groups of saliva samples (COPD-HC-Asthma-Infected) collected in the frame of a joint research project, Exasens (https://www.leibniz-healthtech.de/en/research/projects/bmbf-project-exasens/), at the Research Center Borstel, BioMaterialBank Nord (Borstel, Germany).

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1982 Views

The data set is collected from MyNeuroHealth Application developed for the detection of Seizures and Falls. Data is gathered using tri-axial accelerometer placed at the upper left arm of an individual in an unconstraint environment.

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1163 Views

This dataset provides the magneto-inertial signals from six MIMU (2 Xsens, 2 APDM, 2 Shimmer) and orientation from 8 reflective markers (VICON) at 3 different speeds (slow, medium, fast). Marker trajectories are provided. Proprietary orientations from MIMU vendors are also included. All data are synchronized at 100 Hz.

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898 Views

This dataset consists of sensory data of digits, i.e., from 0 to 9. The dataset is collected from 20 volunteers by using a 9−axis Inertial Measurement Unit (IMU) equipped marker pen. The objective of this dataset is to design classification algorithms for recognizing a handwritten digit in real-time.

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1563 Views

The dataset comprises up to two weeks of activity data taken from the ankle and foot of 14 people without amputation and 17 people with lower limb amputation.  Walking speed, cadence, and lengths of strides taken at and away from the home were considered in this study.  Data collection came from two wearable sensors, one inertial measurement unit (IMU) placed on the top of the prosthetic or non-dominant foot, and one accelerometer placed on the same ankle.  Location information was derived from GPS and labeled as ‘home’, ‘away’, or ‘unknown’.  The dataset contains raw acce

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959 Views

FallAllD is a large open dataset of human falls and activities of daily living simulated by 15 participants. FallAllD consists of 26420 files collected using three data-loggers worn on the waist, wrist and neck of the subjects. Motion signals are captured using an accelerometer, gyroscope, magnetometer and barometer with efficient configurations that suit the potential applications e.g. fall detection, fall prevention and human activity recognition.

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3592 Views

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