Additional details of the fully-connected and convolutional neural network architectures applied for gait recognition. N = number of samples.

Additional details of the convolutional transformer neural network architectures applied for gait recognition. N = number of samples. Transformer encoder layer hyper-parameter settings: d_model = number of input features (varied), nhead = 4, activation = 'gelu', and dim_feedforward = number of output features (varied).

Additional details of the fully-connected and convolutional neural network architectures applied for gait recognition. N = number of samples.

Additional details of the convolutional long short-term memory (LSTM) neural network architectures applied for gait recognition. N = number of samples.

Additional details of the convolutional long short-term memory (LSTM) neural network architectures applied for gait recognition. N = number of samples.

Additional details of the transformer neural network architectures. N = number of samples. Transformer encoder layer hyper-parameter settings: d_model = 6, nhead = 3, activation = 'gelu', and dim_feedforward = 1200.

Additional details of the transformer neural network architectures. N = number of samples. Transformer encoder layer hyper-parameter settings: d_model = 6, nhead = 3, activation = 'gelu', and dim_feedforward = 1200.

Additional details of the convolutional transformer neural network architectures applied for gait recognition. N = number of samples. Transformer encoder layer hyper-parameter settings: d_model = number of input features (varied), nhead = 4, activation = 'gelu', and dim_feedforward = number of output features (varied).

This version (Version 6) is the dataset associated with the article, Duncanson, K.A.; Thwaites, S.; Booth, D.; Hanly, G.; Robertson, W.S.P.; Abbasnejad, E.; Thewlis, D. Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors 2023, 23, 3392. https://doi.org/10.3390/s23073392. Please see Version 3 for the dataset and description relevant to the article, Duncanson, Kayne; Thwaites, Simon; Booth, David; Abbasnejad, Ehsan; Robertson, William; Thewlis, Dominic (2021): The Most Discriminant Components of Force Platform Data for Gait Based Person Re-identification. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.16683229.v1.
Dataset overviewThis dataset was acquired for research on the use of gait as a (soft) biometric for person re-identification (re-ID)/recognition; however, it may be used to answer a variety of research questions. It is one of the largest and most complex force platform datasets purpose built for person re-ID. It contains 5327 walking trials from 184 healthy participants, with inter- and intra-individual variation in clothing, footwear, and walking speed, as well as inter-individual variation in time between trials (data was collected over two sessions separated by 3-14 days depending on the individual). The dataset was generated through a repeated measures experiment (approved by the Human Research Ethics Committee - approval No. H-2018-009) conducted at The University of Adelaide gait analysis laboratory.
Experimental protocolAt the start of each session, age, sex, mass, height, and footwear type were recorded, as participants wore personal clothing and footwear. Footwear was also photographed for future reference. Next, participants walked in one direction along the length of the laboratory (≈10m) five times at three self-selected speeds: preferred, slower than preferred, and faster than preferred. Two in-ground OPT400600-HP force platforms (Advanced Mechanical Technology Inc., USA) in the center of the laboratory measured GRFs and GRMs during left and right footsteps. These measures, along with calculated COP coordinates, were acquired through Vicon Nexus (Vicon Motion Systems Ltd, UK) at 2000 Hz. Of note, all trials in this dataset were complete foot contacts; that is, each foot contacted completely within the area of each force platform, as identified from video footage.
User guideThe code repository to implement this dataset can be found at GitHub - kayneduncanson1/ForceID-Study-1: Repository for the article, 'Deep Metric Learning for Scalable Gait Based Person Re-identification Using Force Platform Data'. The dataset is organised into separate spreadsheets that each contain all samples of a particular component from a given force platform (named as component_platform). Both raw and processed ('pro') versions are available. Within each of the data spreadsheets is accessory information about each trial in the first five columns, followed by the data in column six onward. Within the metadata spreadsheet are the ID numbers and their associated demographics. The ID numbers in the first column are from the private version of the dataset that was implemented for the manuscript. These can be cross-referenced with the ID numbers generated for the public dataset in the code repository. This means that the challenging IDs listed in Supplemental Material - Table VII (in the article) can be located in this dataset.

This version (Version 6) is the dataset associated with the article, Duncanson, K.A.; Thwaites, S.; Booth, D.; Hanly, G.; Robertson, W.S.P.; Abbasnejad, E.; Thewlis, D. Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data. Sensors 2023, 23, 3392. https://doi.org/10.3390/s23073392. Please see Version 3 for the dataset and description relevant to the article, Duncanson, Kayne; Thwaites, Simon; Booth, David; Abbasnejad, Ehsan; Robertson, William; Thewlis, Dominic (2021): The Most Discriminant Components of Force Platform Data for Gait Based Person Re-identification. TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.16683229.v1.
Dataset overviewThis dataset was acquired for research on the use of gait as a (soft) biometric for person re-identification (re-ID)/recognition; however, it may be used to answer a variety of research questions. It is one of the largest and most complex force platform datasets purpose built for person re-ID. It contains 5327 walking trials from 184 healthy participants, with inter- and intra-individual variation in clothing, footwear, and walking speed, as well as inter-individual variation in time between trials (data was collected over two sessions separated by 3-14 days depending on the individual). The dataset was generated through a repeated measures experiment (approved by the Human Research Ethics Committee - approval No. H-2018-009) conducted at The University of Adelaide gait analysis laboratory.
Experimental protocolAt the start of each session, age, sex, mass, height, and footwear type were recorded, as participants wore personal clothing and footwear. Footwear was also photographed for future reference. Next, participants walked in one direction along the length of the laboratory (≈10m) five times at three self-selected speeds: preferred, slower than preferred, and faster than preferred. Two in-ground OPT400600-HP force platforms (Advanced Mechanical Technology Inc., USA) in the center of the laboratory measured GRFs and GRMs during left and right footsteps. These measures, along with calculated COP coordinates, were acquired through Vicon Nexus (Vicon Motion Systems Ltd, UK) at 2000 Hz. Of note, all trials in this dataset were complete foot contacts; that is, each foot contacted completely within the area of each force platform, as identified from video footage.
User guideThe code repository to implement this dataset can be found at GitHub - kayneduncanson1/ForceID-Study-1: Repository for the article, 'Deep Metric Learning for Scalable Gait Based Person Re-identification Using Force Platform Data'. The dataset is organised into separate spreadsheets that each contain all samples of a particular component from a given force platform (named as component_platform). Both raw and processed ('pro') versions are available. Within each of the data spreadsheets is accessory information about each trial in the first five columns, followed by the data in column six onward. Within the metadata spreadsheet are the ID numbers and their associated demographics. The ID numbers in the first column are from the private version of the dataset that was implemented for the manuscript. These can be cross-referenced with the ID numbers generated for the public dataset in the code repository. This means that the challenging IDs listed in Supplemental Material - Table VII (in the article) can be located in this dataset.