A1 Journal article (refereed)
Using deep neural networks for kinematic analysis : challenges and opportunities (2021)


Cronin, N. J. (2021). Using deep neural networks for kinematic analysis : challenges and opportunities. Journal of Biomechanics, 123, Article 110460. https://doi.org/10.1016/j.jbiomech.2021.110460


JYU authors or editors


Publication details

All authors or editors: Cronin, Neil J.

Journal or series: Journal of Biomechanics

ISSN: 0021-9290

eISSN: 1873-2380

Publication year: 2021

Volume: 123

Article number: 110460

Publisher: Elsevier BV

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1016/j.jbiomech.2021.110460

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/75928


Abstract

Kinematic analysis is often performed in a lab using optical cameras combined with reflective markers. With the advent of artificial intelligence techniques such as deep neural networks, it is now possible to perform such analyses without markers, making outdoor applications feasible. In this paper I summarise 2D markerless approaches for estimating joint angles, highlighting their strengths and limitations. In computer science, so-called “pose estimation” algorithms have existed for many years. These methods involve training a neural network to detect features (e.g. anatomical landmarks) using a process called supervised learning, which requires “training” images to be manually annotated. Manual labelling has several limitations, including labeller subjectivity, the requirement for anatomical knowledge, and issues related to training data quality and quantity. Neural networks typically require thousands of training examples before they can make accurate predictions, so training datasets are usually labelled by multiple people, each of whom has their own biases, which ultimately affects neural network performance. A recent approach, called transfer learning, involves modifying a model trained to perform a certain task so that it retains some learned features and is then re-trained to perform a new task. This can drastically reduce the required number of training images. Although development is ongoing, existing markerless systems may already be accurate enough for some applications, e.g. coaching or rehabilitation. Accuracy may be further improved by leveraging novel approaches and incorporating realistic physiological constraints, ultimately resulting in low-cost markerless systems that could be deployed both in and outside of the lab.


Keywords: kinematics; motion analysis; machine learning; neural networks (information technology)

Free keywords: motion analysis; kinematics; deep neural network; markerless tracking; artificial intelligence


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Ministry reporting: Yes

Reporting Year: 2021

Preliminary JUFO rating: 2


Last updated on 2021-20-09 at 16:24