Tuesday, April 13, 2021
  • Setup menu at Appearance » Menus and assign menu to Top Bar Navigation
Advertisement
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News
No Result
View All Result
NikolaNews
No Result
View All Result
Home Machine Learning

Scientists apply machine learning to improve our understanding of horse gaits

November 15, 2020
in Machine Learning
Scientists apply machine learning to improve our understanding of horse gaits
586
SHARES
3.3k
VIEWS
Share on FacebookShare on Twitter
The locations of the sensors used in the study, which involved 120 horses. Photo: Serra Braganca et al. https://doi.org/10.1038/s41598-020-73215-9

Researchers have employed machine learning in combination with inertial measurement units to accurately classify the various gaits of horses.

“The human eye has thus far served as the ‘gold standard” for gait classification,” Dr Filipe Serra Braganca and his fellow researchers noted in the journal Scientific Reports.

You might also like

Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU

A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review

Artificial Intelligence Research at Duke

“It is clear from the current study, however, that human visual and subjective assessment is not optimal for this purpose.

“This observation is in line with other studies evaluating human assessment of equine locomotion, mainly in relation to the evaluation of lameness in clinical situations,” the multidisiplinary study team reported.

There, too, human subjective assessment proves less than optimal, as it is affected by the limitations of the human eye and the proneness to bias.

The authors noted that, for centuries, humans have been fascinated by the natural beauty of horses in motion and their different gaits.

Scientific work on gaits in animals was pioneered by Milton Hildebrand. In a ground-breaking article published in Science in 1965, he described a gait classification paradigm. Using manually and subjectively digitized high-speed films, Hildebrand and his colleagues categorized four-legged locomotion into walking and running, and into symmetrical and asymmetrical gaits.

“These relatively simple classification categories have, however, been questioned as to how accurate they are in reliably distinguishing gaits and to what extent they can explain the complex gait patterns generated by the multiple components of the locomotor apparatus of quadrupedal animals.

“More recently, multidimensional approaches have been used, challenging the old dogma.”

The researchers said the development of reliable, automated methods for real-time objective gait classification in horses is warranted.

Four breeds analysed

For their study, they used a full-body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait.

In all, they used data from 120 horses equipped with seven motion sensors. The work focused on 7576 strides involving eight gaits — the walk, trot, left canter, right canter, tölt, pace, paso fino and trocha.

Four breeds were used: the Colombian Paso, Icelandic, Warmblood and Franche Montagne.

Several machine-learning approaches were used, both from feature-extracted data and from raw sensor data. The best model achieved 97% accuracy, they reported.

“Our technique facilitated accurate, gait classification that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding.”

The authors said the footfall pattern and the sequence of footfalls can be defined for each gait.

“Some specific features of the gaits can easily be identified, such as symmetry and laterality,” they said. “However, for some gaits such as walk, tölt and paso fino, these variables do not fully discriminate between the gait classes.”

Similarly, the time between strides can be enough to differentiate between some gaits, such as for the walk and trot. “But in other gaits, some of these features overlap, such as stance duration for paso fino and trocha.”

This, they said, highlights the need for multidimensional classification models for the comprehensive classification of all gaits.

The authors said they were able to use technology to extend Hildebrand’s original equine gait paradigm from 1965, showing that reality is more complex and ambiguous, and less straightforward than the original concept.

“This is not unexpected, since Hildebrand’s original model was two-dimensional.

“Our results confirm that gaits are in fact separated by multidimensional planes and that accurate classification can be achieved for this unique diverse gait data using automated approaches that include minimal preprocessing of the signal.”

The researchers say the models used in the study open a new world of possibilities, such as research into the genetics of gait.

“Most equine genetic studies focusing on locomotion, either related to gait or sports performance, require precise phenotyping in order to discriminate between trends in populations or sub-populations.

“Gait phenotyping is still performed subjectively in most of these studies and thus much less accurate than desirable; we therefore believe that our more accurate methods will allow forthcoming studies to understand the genotype–phenotype association of gaits in greater detail.”

They said their approach had the potential for use in other quadrupedal species without the need for developing gait/animal specific algorithms.

Serra Bragança, F.M., Broomé, S., Rhodin, M. et al. Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning. Sci Rep 10, 17785 (2020). https://doi.org/10.1038/s41598-020-73215-9

The study, published under a Creative Commons License, can be read here. 


Credit: Google News

Previous Post

Amazon files lawsuit against Instagram, TikTok influencers over 'dupe' sales scam

Next Post

New ModPipe malware targets hospitality, hotel point of sale systems

Related Posts

Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU
Machine Learning

Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU

April 13, 2021
A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review
Machine Learning

A.I. For Raspberry Pi Pico: Uctronics TinyML Learning Kit Review

April 13, 2021
Artificial Intelligence Research at Duke
Machine Learning

Artificial Intelligence Research at Duke

April 13, 2021
AI, Machine And Deep Learning: Filling Today’s Need for Speed And Iteration
Machine Learning

AI, Machine And Deep Learning: Filling Today’s Need for Speed And Iteration

April 12, 2021
Analyttica Datalab Introduces LEAPS Programs on Applied Data Science and Machine Learning
Machine Learning

Analyttica Datalab Introduces LEAPS Programs on Applied Data Science and Machine Learning

April 12, 2021
Next Post
New ModPipe malware targets hospitality, hotel point of sale systems

New ModPipe malware targets hospitality, hotel point of sale systems

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

Plasticity in Deep Learning: Dynamic Adaptations for AI Self-Driving Cars

January 6, 2019
Microsoft, Google Use Artificial Intelligence to Fight Hackers

Microsoft, Google Use Artificial Intelligence to Fight Hackers

January 6, 2019

Categories

  • Artificial Intelligence
  • Big Data
  • Blockchain
  • Crypto News
  • Data Science
  • Digital Marketing
  • Internet Privacy
  • Internet Security
  • Learn to Code
  • Machine Learning
  • Marketing Technology
  • Neural Networks
  • Technology Companies

Don't miss it

Apple looking to close the gap between web and app privacy
Internet Security

Who do I pay to get the ‘phone’ removed from my iPhone?

April 13, 2021
Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft
Data Science

Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft

April 13, 2021
Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU
Machine Learning

Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU

April 13, 2021
How to Change the WordPress Admin Login Logo
Learn to Code

Intl.NumberFormat

April 13, 2021
Criminals spread malware using website contact forms with Google URLs
Internet Security

Criminals spread malware using website contact forms with Google URLs

April 13, 2021
Trends in custom software development in 2021
Data Science

Trends in custom software development in 2021

April 13, 2021
NikolaNews

NikolaNews.com is an online News Portal which aims to share news about blockchain, AI, Big Data, and Data Privacy and more!

What’s New Here?

  • Who do I pay to get the ‘phone’ removed from my iPhone? April 13, 2021
  • Robust Artificial Intelligence of Document Attestation to Ensure Identity Theft April 13, 2021
  • Data Science And Machine Learning Service Market Growth Due to COVID-19 Spread | ZS, LatentView Analytics, Mango Solutions, Microsoft, International Business Machine – KSU April 13, 2021
  • Intl.NumberFormat April 13, 2021

Subscribe to get more!

© 2019 NikolaNews.com - Global Tech Updates

No Result
View All Result
  • AI Development
    • Artificial Intelligence
    • Machine Learning
    • Neural Networks
    • Learn to Code
  • Data
    • Blockchain
    • Big Data
    • Data Science
  • IT Security
    • Internet Privacy
    • Internet Security
  • Marketing
    • Digital Marketing
    • Marketing Technology
  • Technology Companies
  • Crypto News

© 2019 NikolaNews.com - Global Tech Updates