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Animals : an open access journal from MDPI2023; 13(3); 390; doi: 10.3390/ani13030390

Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture.

Abstract: Computer vision is a subcategory of artificial intelligence focused on extraction of information from images and video. It provides a compelling new means for objective orthopaedic gait assessment in horses using accessible hardware, such as a smartphone, for markerless motion analysis. This study aimed to explore the lameness assessment capacity of a smartphone single camera (SC) markerless computer vision application by comparing measurements of the vertical motion of the head and pelvis to an optical motion capture multi-camera (MC) system using skin attached reflective markers. Twenty-five horses were recorded with a smartphone (60 Hz) and a 13 camera MC-system (200 Hz) while trotting two times back and forth on a 30 m runway. The smartphone video was processed using artificial neural networks detecting the horse's direction, action and motion of body segments. After filtering, the vertical displacement curves from the head and pelvis were synchronised between systems using cross-correlation. This rendered 655 and 404 matching stride segmented curves for the head and pelvis respectively. From the stride segmented vertical displacement signals, differences between the two minima (MinDiff) and the two maxima (MaxDiff) respectively per stride were compared between the systems. Trial mean difference between systems was 2.2 mm (range 0.0-8.7 mm) for head and 2.2 mm (range 0.0-6.5 mm) for pelvis. Within-trial standard deviations ranged between 3.1-28.1 mm for MC and between 3.6-26.2 mm for SC. The ease of use and good agreement with MC indicate that the SC application is a promising tool for detecting clinically relevant levels of asymmetry in horses, enabling frequent and convenient gait monitoring over time.
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Publication Date: 2023-01-24 PubMed ID: 36766279PubMed Central: PMC9913208DOI: 10.3390/ani13030390Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The study aims to assess the efficacy of a smartphone-based computer vision application in the diagnosis of lameness in horses by comparing it with the traditional multi-camera system. Its findings suggest that the smartphone-based computer vision tool stands promising for frequent and convenient gait monitoring over time due to its ease of use and level of agreement with the traditional method.

Objective of the Study

  • The primary goal of this research was to evaluate a smartphone single camera (SC) markerless computer vision application’s capacity to assess lameness in horses.
  • The researchers sought to ascertain the reliability of the said smartphone application by comparing its metrics of vertical head and pelvis movement with that of a traditional optical multi-camera (MC) system which makes use of skin attached reflective markers.

Methodology

  • A total of 25 horses were employed for this experiment, each trotting twice on a 30-meter runway while being recorded with a smartphone at 60 Hz and a 13 camera MC-system at 200 Hz.
  • Using artificial neural networks, the recorded smartphone video was processed to detect and document the horse’s direction, action, and motion of body segments.
  • Following this, the vertical displacement curves from the head and pelvis were synchronized between the SC and MC systems using cross-correlation. This sequence generated 655 and 404 matching stride segmented curves for the head and pelvis, respectively.

Results and Findings

  • The trial mean difference between the two systems was found to be 2.2 mm (range 0.0-8.7 mm) for head movements and 2.2 mm (range 0.0-6.5 mm) for pelvis movements.
  • Within-trial standard deviations ranged between 3.1-28.1 mm for MC and between 3.6-26.2 mm for SC.
  • The agreement between the readings of the SC and MC systems suggested that a smartphone-based application could potentially serve as a valid tool for detecting asymmetry in horses at clinically significant levels.

Conclusion

  • The study concludes that the smartphone-based computer vision application can be a practical and promising tool for routine and convenient gait monitoring over time, due to its ease of use and its ability to produce results that are in good agreement with traditional, multi-camera systems.

Cite This Article

APA
Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M, Andersen PH, Kjellström H, Hernlund E. (2023). Is Markerless More or Less? Comparing a Smartphone Computer Vision Method for Equine Lameness Assessment to Multi-Camera Motion Capture. Animals (Basel), 13(3), 390. https://doi.org/10.3390/ani13030390

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 13
Issue: 3
PII: 390

Researcher Affiliations

Lawin, Felix Järemo
  • Sleip AI, Birger Jarlsgatan 58, 11426 Stockholm, Sweden.
Byström, Anna
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.
Roepstorff, Christoffer
  • Sleip AI, Birger Jarlsgatan 58, 11426 Stockholm, Sweden.
Rhodin, Marie
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.
Almlöf, Mattias
  • Sleip AI, Birger Jarlsgatan 58, 11426 Stockholm, Sweden.
Silva, Mudith
  • Sleip AI, Birger Jarlsgatan 58, 11426 Stockholm, Sweden.
Andersen, Pia Haubro
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.
Kjellström, Hedvig
  • KTH Royal Institute of Technology, Division of Robotics, Perception and Learning, 10044 Stockholm, Sweden.
Hernlund, Elin
  • Sleip AI, Birger Jarlsgatan 58, 11426 Stockholm, Sweden.
  • Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, 75007 Uppsala, Sweden.

Grant Funding

  • 2018.4.2-2084 / Marie-Claire Cronstedts Stiftelse
  • 2018- 00737 / Swedish research council FORMAS

Conflict of Interest Statement

The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Authors affiliated to the company Sleip AI (F.J.L., C.R., M.A., M.S. and E.H.) declare a conflict of interest since the company provides a commercially available diagnostic tool for detecting lameness in horses from a smartphone. The developed computer vision technique is validated in the current paper.

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