Real-world measurements of ground reaction forces of normal gait of young adults wearing various footwear

Derlatka, M.; Parfieniuk, Marek

doi:10.1038/s41597-023-01964-z

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…It should be noted that almost all studies in this field have utilized private datasets with fewer than 80 IDs [ 6 , 17 , 19 , 20 , 24 ]. Exceptions to this are Horst et al [ 18 ], who used data from 57 IDs that is available in in their public database [ 41 ], and Derlatka and Borowska [ 39 ], who used data from 322 IDs (that may be from their public dataset [ 42 ], though it was not mentioned). This makes ForceID A the second largest dataset to be implemented for force platform-based re-ID and by far the largest dataset of repeated measures over separate days.…”

Section: Methodsmentioning

confidence: 99%

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Duncanson

Thwaites

Booth

et al. 2023

Sensors

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Walking gait data acquired with force platforms may be used for person re-identification (re-ID) in various authentication, surveillance, and forensics applications. Current force platform-based re-ID systems classify a fixed set of identities (IDs), which presents a problem when IDs are added or removed from the database. We formulated force platform-based re-ID as a deep metric learning (DML) task, whereby a deep neural network learns a feature representation that can be compared between inputs using a distance metric. The force platform dataset used in this study is one of the largest and the most comprehensive of its kind, containing 193 IDs with significant variations in clothing, footwear, walking speed, and time between trials. Several DML model architectures were evaluated in a challenging setting where none of the IDs were seen during training (i.e., zero-shot re-ID) and there was only one prior sample per ID to compare with each query sample. The best architecture was 85% accurate in this setting, though an analysis of changes in walking speed and footwear between measurement instances revealed that accuracy was 28% higher on same-speed, same-footwear comparisons, compared to cross-speed, cross-footwear comparisons. These results demonstrate the potential of DML algorithms for zero-shot re-ID using force platform data, and highlight challenging cases.

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Section: Methodsmentioning

confidence: 99%

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Duncanson

Thwaites

Booth

et al. 2023

Sensors

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“…The people taking part in the research were at ages 21.34 ± 1.16, body weight: 74.32 ± 16.63 kg and body height 174.39 ± 9.49 cm. The investigations were performed according to the procedure described in [18]. During the tests, participants walked through a testing path whose length exceeded 10 meters and within which there were hidden two Kislter's force plates.…”

Section: The Study Groupmentioning

confidence: 99%

Automatic gender and unilateral load state recognition for biometric purposes

Derlatka

2023

THC

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BACKGROUND: Automatic recognition of a person’s gender as well as his or her unilateral load state are issues that are often analyzed and utilized by a wide range of applications. For years, scientists have recognized human gait patterns for purposes connected to medical diagnoses, rehabilitation, sport, or biometrics. OBJECTIVE: The present paper makes use of ground reaction forces (GRF) generated during human gait to recognize gender or the unilateral load state of a walking person as well as the combination of both of those characteristics. METHODS: To solve the above-stated problem parameters calculated on the basis of all GRF components such as mean, variance, standard deviation of data, peak-to-peak amplitude, skewness, kurtosis, and Hurst exponent as well as leading classification algorithms including kNN, artificial neural networks, decision trees, and random forests, were utilized. Data were collected by means of Kistler’s force plates during a study carried out at the Bialystok University of Technology on a sample of 214 people with a total of 7,316 recorded gait cycles. RESULTS: The best results were obtained with the use of the kNN classifier which recognized the gender of the participant with an accuracy of 99.37%, the unilateral load state with an accuracy reaching 95.74%, and the combination of those two states with an accuracy of 95.31% which, when compared to results achieved by other authors are some of the most accurate. CONCLUSION: The study has shown that the given set of parameters in combination with the kNN classifying algorithm allows for an effective automatic recognition of a person’s gender as well as the presence of an asymmetrical load in the form of a hand-carried briefcase. The presented method can be used as a first stage in biometrics systems.

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Effects of foot exercises and customized arch support insoles on foot posture, plantar force distribution, and balance in people with flexible flatfoot: A randomized controlled trial

Kirmizi,

Sengul,

Akcali

et al. 2024

Gait & Posture

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Real-world measurements of ground reaction forces of normal gait of young adults wearing various footwear

Cited by 5 publications

References 22 publications

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Deep Metric Learning for Scalable Gait-Based Person Re-Identification Using Force Platform Data

Automatic gender and unilateral load state recognition for biometric purposes

Effects of foot exercises and customized arch support insoles on foot posture, plantar force distribution, and balance in people with flexible flatfoot: A randomized controlled trial

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