FallAllD: A Comprehensive Dataset of Human Falls and Activities of Daily Living

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SALEH, Majd;LE BOUQUIN JEANNES, Régine

Description

DescriptionFallAllD 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.Data AcquisitionThree identical data-loggers have been used. They are waist, wrist and neck-worn devices equipped with:· LSM9DS1: 3-axial accelerometer, 3-axial gyroscope and 3-axial magnetometer.· MS5607-02BA03: barometer.Sensors configurations· Accelerometer: Sampling frequency = 238 Hz. Measurement range = ±8 g.· Gyroscope: Sampling frequency = 238 Hz. Angular rate = ± 2000 dps.· Magnetometer: Sampling frequency = 80 Hz. Full scale magnetic field = ±4 Gauss.· Barometer: Sampling frequency = 10 Hz.

Citations (0)

Mentions (0)

Metrics

Dataset Index

1.3

FAIR Score

58%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

IEEE DataPort

Assigned Domain

Subfield

Psychiatry and Mental health

Field

Medicine

Domain

Health Sciences

Confidence Score

77%

Source

Open Alex

Keywords

HealthWearable Sensingfall detectionfall preventionhuman activity recognitionWearable Sensorselderly health-care

Normalization Factors

FT

15.38

CTw

1.00

MTw

1.00