Abstract
The quantification of ground reaction forces (GRF) is a standard tool for clinicians to quantify and analyze human locomotion. Such recordings produce a vast amount of complex data and variables which are difficult to comprehend. This makes data interpretation challenging. Machine learning approaches seem to be promising tools to support clinicians in identifying and categorizing specific gait patterns. However, the quality of such approaches strongly depends on the amount of available annotated data to train the underlying models. Therefore, we present GaitRec, a comprehensive and completely annotated large-scale dataset containing bi-lateral GRF walking trials of 2,084 patients with various musculoskeletal impairments and data from 211 healthy controls. The dataset comprises data of patients after joint replacement, fractures, ligament ruptures, and related disorders at the hip, knee, ankle or calcaneus during their entire stay(s) at a rehabilitation center. The data sum up to a total of 75,732 bi-lateral walking trials and enable researchers to classify gait patterns at a large-scale as well as to analyze the entire recovery process of patients.
Highlights
Background & SummaryThe quantification of ground reaction forces (GRF) is a standard tool for clinicians to objectively measure human locomotion and to describe and analyze a patient’s gait performance in detail
Machine learning methods employed in this context comprise, but are not limited to, neural networks[4,5,6], support vector machines[7,8,9], nearest neighbor classifiers[10,11], and different clustering approaches[12]
We have developed a machine learning framework for gait classification and have performed comprehensive experiments[13,14,15,16]
Summary
Background & SummaryThe quantification of ground reaction forces (GRF) is a standard tool for clinicians to objectively measure human locomotion and to describe and analyze a patient’s gait performance in detail. The presented dataset comprises completely anonymized GRF measurements from 2085 patients with different musculoskeletal impairments (“gait disorders”, GD) and data from 211 healthy controls (HC) including additional metadata such as age, sex, shod condition, walking speed condition, etc.
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