Visual3D Tutorial: Looking at Large Public Datasets: Difference between revisions
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== Abstract == | == Abstract == | ||
Biomechanics is coming into a new age, where large datasets are the norm and individually processing the files can be tiresome and time consuming. Captured over long periods of time, datasets can be inconsistent, which makes it difficult to automate working with them. | |||
In this tutorial, we will be going through common issues seen while working with large public biomechanics datasets, through the use of a large dataset released which tracks the full body gait of both stroke-victims and healthy participants. | |||
== Large Datasets == | |||
The dataset used for this tutorial is the raw motion data collected by (Van Criekinge et al , [reference]), which includes the full-body motion capture gait of 138 able-bodied adult participants and 50 stroke-survivors. They include Full Body Kinematics (PiG Model), Kinetics, and EMG Data across a wide variety of ages, heights and weights. This is an open sourced dataset, which allows for free and open science, supports the growing biomedical research community and increases the ability for scientists to duplicate results of tests. | |||
This dataset is unique in its size and quality for an open source dataset. Large datasets are essential for scientific observations to be able to accurately draw conclusions, and are becoming the norm within the biomechanical community. Because of their nature as large collections of data, there are often unique problems relating to working with such datasets, which is to be explored here. Datasets such as these can be generated over a long period of time, where people, standards or equipment may change. Large datasets can also be the culmination of several different projects, which further leads to inconsistencies. The most frequent issues we found were due to inconsistencies within the measured dataset: it can be difficult to keep a rigid structure to any large dataset, but inconsistencies can wreck the ability to automate tasks, so we’ll show how you can effectively process the data in large datasets like this. | |||
Revision as of 14:41, 12 January 2024
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Abstract
Biomechanics is coming into a new age, where large datasets are the norm and individually processing the files can be tiresome and time consuming. Captured over long periods of time, datasets can be inconsistent, which makes it difficult to automate working with them.
In this tutorial, we will be going through common issues seen while working with large public biomechanics datasets, through the use of a large dataset released which tracks the full body gait of both stroke-victims and healthy participants.
Large Datasets
The dataset used for this tutorial is the raw motion data collected by (Van Criekinge et al , [reference]), which includes the full-body motion capture gait of 138 able-bodied adult participants and 50 stroke-survivors. They include Full Body Kinematics (PiG Model), Kinetics, and EMG Data across a wide variety of ages, heights and weights. This is an open sourced dataset, which allows for free and open science, supports the growing biomedical research community and increases the ability for scientists to duplicate results of tests.
This dataset is unique in its size and quality for an open source dataset. Large datasets are essential for scientific observations to be able to accurately draw conclusions, and are becoming the norm within the biomechanical community. Because of their nature as large collections of data, there are often unique problems relating to working with such datasets, which is to be explored here. Datasets such as these can be generated over a long period of time, where people, standards or equipment may change. Large datasets can also be the culmination of several different projects, which further leads to inconsistencies. The most frequent issues we found were due to inconsistencies within the measured dataset: it can be difficult to keep a rigid structure to any large dataset, but inconsistencies can wreck the ability to automate tasks, so we’ll show how you can effectively process the data in large datasets like this.