Field
Value
Language
dc.contributor.author
Gholami, Mohsen
datacite.creator.affiliationIdentifier
https://ror.org/0213rcc28
en_US
datacite.creator.affiliation
Simon Fraser University
en_US
datacite.creator.nameIdentifier
en_US
dc.contributor.author
Menon, Carlo
datacite.creator.affiliationIdentifier
https://ror.org/0213rcc28
en_US
datacite.creator.affiliation
Simon Fraser University
en_US
datacite.creator.nameIdentifier
en_US
dc.coverage.temporal
2018-01-01/2018-12-31
dc.date.accessioned
2024-01-25T20:01:46Z
dc.date.available
2024-01-25T20:01:46Z
dc.date.issued
2024-01-25
dc.identifier.uri
https://www.frdr-dfdr.ca/repo/dataset/23f6f689-de40-48fe-bc3e-b4588870657e
dc.identifier.uri
https://doi.org/10.20383/103.0871
dc.description
Background: Soft strain sensors can be integrated into clothing in a very unobtrusive fashion and may be used for kinematics measurement of runners in the field. This study collected data to train and test a machine learning model that predicted running kinematics from wearable strain sensor measurements.
Objective: Evaluate whether soft fibre strain sensors worn in a tight-fitting garment around unilateral lower limb joints could be used to reconstruct running kinematics. The resisitve strain sensor signals (after processing with a machine learning model) were compared to the gold-standard optical motion capture reference which was collected simultaneously.
Research outcomes: These data were collected for the study in [1].
Data intepretations: This dataset may be useful for others comparing resistive strain sensors in running, as there exists few public datasets that include both strain sensors and gold-standard motion capture data.
Methods: Data was collected as described in [1]. Twelve subjects ran on an instrumented treadmill at five speeds (8, 9, ..., 12 km/h) wearing tights that included nine piezoresistive strain sensors. An optical motion capture system recorded the "gold standard" joint angles. Shortcomings of the dataset include:
- Strain sensors placed only on the left leg.
- Only the left side ground reaction forces were measured.
[1] Gholami, M.; Rezaei, A.; Cuthbert, T.J.; Napier, C.; Menon, C. Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks. Sensors 2019, 19, 5325–5343, doi:10.3390/s19235325.
en_US
dc.publisher
Federated Research Data Repository / dépôt fédéré de données de recherche
dc.rights
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)
en_US
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/4.0/
en_US
dc.subject
wearable sensors
en_US
dc.subject
human motion tracking
en_US
dc.subject
textile sensors
en_US
dc.title
Treadmill Running with IMUs and Custom Piezoresistive Strain Sensors on one Lower Limb
en_US
globus.shared_endpoint.name
f163c1b3-9c88-42f6-a7bb-5839ed6c4063
globus.shared_endpoint.path
/8/published/publication_866/
datacite.publicationyear
2024
datacite.contributor.DataCollector
Mohsen Gholami
datacite.contributor.Supervisor
Carlo Menon
datacite.date.Collected
2018-01-01/2018-12-31
datacite.resourcetype
Dataset
en_US
datacite.relatedidentifier.IsDerivedFrom
https://doi.org/10.3390/s19235325
datacite.fundingReference.funderIdentifier
https://ror.org/000az4664
en_US
datacite.fundingReference.funderName
Canada Foundation for Innovation
en_US
datacite.fundingReference.awardNumber
Project Number 36347
en_US
datacite.fundingReference.awardTitle
Centre for Wearable Biomedical Technologies
en_US
frdr.crdc.code
RDF2070102
en_US
frdr.crdc.group_en
Medical and biomedical engineering
en_US
frdr.crdc.class_en
Medical and biomedical engineering
en_US
frdr.crdc.field_en
Biomechanical engineering
en_US
frdr.crdc.group_fr
Génie médical et biomédical
fr_CA
frdr.crdc.class_fr
Génie médical et biomédical
fr_CA
frdr.crdc.field_fr
Génie biomécanique
fr_CA
Appears in Collections:
