Data

ForceID-B - A diverse walking ground reaction force dataset acquired in a public indoor setting

Adelaide University
Duncanson, Kayne ; Badger, Heather ; Krywanio, Matilda ; Abbasnejad, Ehsan ; Hanly, Gary ; Robertson, Will ; Thewlis, Dominic
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25909/33000368.v2&rft.title=ForceID-B - A diverse walking ground reaction force dataset acquired in a public indoor setting&rft.identifier=10.25909/33000368.v2&rft.publisher=Adelaide University&rft.description=Walking ground reaction force (GRF) data is vital for biomechanical gait analysis to gain insight into how biological, task and environmental constraints define human movement. ForceID-B is a diverse large-scale walking GRF dataset collected in a public building on a university campus. The dataset contains 1362 walking trials across 336 sessions from 173 healthy participants. Each trial comprises 3D GRF, 3D moment and 2D center of pressure measurements from three force platforms as multivariate time series signals. The dataset captures complex intra- and inter-individual variations in gait due to factors like clothing, footwear and object carriage, with most participants visiting on 2 to 5 days over the five-day experiment. Accompanying raw data are two processed subsets, detailed metadata on personal attributes and foot contacts (with 65% partial contacts due to unconstrained foot placement), and a comprehensive code repository. ForceID-B bridges laboratory studies and real-world human movement by enabling signal processing, normative gait modelling and machine learning system development on complex field data. The Data Descriptor pre-print article associated with this dataset contains a full description and will be uploaded shortly.&rft.creator=Duncanson, Kayne &rft.creator=Badger, Heather &rft.creator=Krywanio, Matilda &rft.creator=Abbasnejad, Ehsan &rft.creator=Hanly, Gary &rft.creator=Robertson, Will &rft.creator=Thewlis, Dominic &rft.edition=2&rft_rights= https://creativecommons.org/licenses/by/4.0/&rft_subject=Biomechanical engineering&rft_subject=Biomechanics&rft_subject=Stream and sensor data&rft_subject=Sports science and exercise not elsewhere classified&rft_subject=Rehabilitation engineering&rft_subject=Signal processing&rft_subject=walking&rft_subject=gait&rft_subject=ground reaction force&rft_subject=overground&rft_subject=ForceID-B&rft_subject=force plate&rft_subject=force platform&rft_subject=center of pressure&rft_subject=gait recognition&rft_subject=person re-identification&rft_subject=biometric&rft_subject=gait analysis&rft_subject=machine learning&rft_subject=deep learning&rft_subject=biomechanics&rft_subject=footwear&rft_subject=object carriage&rft_subject=clothing&rft_subject=real-world&rft_subject=age recognition&rft_subject=sex recognition&rft_subject=gender recognition&rft.type=dataset&rft.language=English Access the data
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Full description

Walking ground reaction force (GRF) data is vital for biomechanical gait analysis to gain insight into how biological, task and environmental constraints define human movement. ForceID-B is a diverse large-scale walking GRF dataset collected in a public building on a university campus. The dataset contains 1362 walking trials across 336 sessions from 173 healthy participants. Each trial comprises 3D GRF, 3D moment and 2D center of pressure measurements from three force platforms as multivariate time series signals. The dataset captures complex intra- and inter-individual variations in gait due to factors like clothing, footwear and object carriage, with most participants visiting on 2 to 5 days over the five-day experiment. Accompanying raw data are two processed subsets, detailed metadata on personal attributes and foot contacts (with 65% partial contacts due to unconstrained foot placement), and a comprehensive code repository. ForceID-B bridges laboratory studies and real-world human movement by enabling signal processing, normative gait modelling and machine learning system development on complex field data. The Data Descriptor pre-print article associated with this dataset contains a full description and will be uploaded shortly.

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Subjects
Biomechanical engineering | Biomechanics | ForceID-B | Rehabilitation engineering | Signal processing | Sports science and exercise not elsewhere classified | Stream and sensor data | age recognition | biomechanics | biometric | center of pressure | clothing | deep learning | footwear | force plate | force platform | gait | gait analysis | gait recognition | gender recognition | ground reaction force | machine learning | object carriage | overground | person re-identification | real-world | sex recognition | walking |

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Identifiers
ACN 633 798 857