Field
Value
Language
dc.contributor.author
Robinovitch, Stephen
datacite.creator.affiliationIdentifier
https://ror.org/0213rcc28
en_US
datacite.creator.affiliation
Simon Fraser University
en_US
datacite.creator.nameIdentifier
https://orcid.org/0000-0003-3881-6227
en_US
dc.contributor.author
Aziz, Omar
datacite.creator.affiliationIdentifier
https://ror.org/0213rcc28
en_US
datacite.creator.affiliation
Simon Fraser University
en_US
datacite.creator.nameIdentifier
en_US
dc.date.accessioned
2021-04-01T22:49:57Z
dc.date.available
2021-04-01T22:49:57Z
dc.date.issued
2017-12-14
dc.identifier.uri
https://www.frdr-dfdr.ca/repo/dataset/6998d4cd-bd13-4776-ae60-6d80221e0365
dc.identifier.uri
https://doi.org/10.25314/35954791-2754-47d7-893a-39430535dfdd
dc.description
Inertial Measurement Unit Fall Detection Dataset (IMU Dataset) is a dataset devised to benchmark fall detection and prediction algorithms based on acceleration, angular velocity and magnetic fields of body-worn APDM Opal IMU sensors recording at 128 Hz at 7 body locations (right ankle, left ankle, right thigh, left thigh, head, sternum, and waist). Detailed description of the dataset and column names are in README.txt file.
Use of this dataset in publications must be acknowledged by referencing the following publication: - Omar Aziz, Magnus Musngi, Edward J. Park, Greg Mori, Stephen N. Robinovitch. "A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials". SpringerLink Med Biol Eng Comput (2017) 55: 45.
We also appreciate if you drop us an email (stever@sfu.ca and oaziz@sfu.ca) to inform us of any publication using this dataset, so we can point to your publication on our webpage.
Format of data is tabular and content type is sensor data. Software used was Excel.
Confidentiality declaration: The dataset does not contain personal identifiable information. All human subjects provided written consent prior to data collection.
This dataset was originally deposited in the Simon Fraser University institutional repository.
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Attribution-NoDerivatives 4.0 (CC BY-ND 4.0)
en_US
dc.rights.uri
https://creativecommons.org/licenses/by-nd/4.0/
en_US
dc.subject
Sensor
en_US
dc.subject
Fall detection
en_US
dc.subject
IMU
en_US
dc.subject
Biomedical Physiology and Kinesiology
en_US
dc.subject
Inertial Measurement Unit
en_US
dc.title
Inertial Measurement Unit Fall Detection Dataset
en_US
globus.shared_endpoint.name
f163c1b3-9c88-42f6-a7bb-5839ed6c4063
globus.shared_endpoint.path
/8/published/publication_335/
frdr.preservation.status
AIP generation and transfer successful
frdr.preservation.datetime
2021-05-18
datacite.publicationyear
2017
datacite.date.Collected
2011-05/2011-05
datacite.resourcetype
Dataset
en_US
datacite.relatedidentifier.IsCitedBy
https://doi.org/10.1007/s11517-016-1504-y
datacite.fundingReference.funderName
Canadian Institutes of Health Research (CIHR)
en_US
datacite.fundingReference.awardNumber
en_US
datacite.fundingReference.awardTitle
en_US
frdr.crdc.code
RDF3030301
frdr.crdc.group_en
Health sciences
en_US
frdr.crdc.class_en
Rehabilitation medicine
en_US
frdr.crdc.field_en
Kinesiology
en_US
frdr.crdc.group_fr
Sciences de la santé
fr_CA
frdr.crdc.class_fr
Médecine de la réadaptation
fr_CA
frdr.crdc.field_fr
Kinésiologie
fr_CA
Appears in Collections: