Research trends in equine movement analysis, future opportunities and potential barriers in the digital age: A scoping review from 1978 to 2018

Egan, Sonja; Brama, P. A. J.; McGrath, Denise

doi:10.1111/evj.13076

Cited by 31 publications

(32 citation statements)

References 76 publications

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“…The introduction of sensor technology in motion studies allows easy collection of large amounts of high-resolution, high-sample rate data 8 that can be used to train models for gait classification. Here, we used sensor-based data to investigate the accuracy of different classification models, based on machine learning technology.…”

Section: Introductionmentioning

confidence: 99%

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Bragança

Broomé

Rhodin

et al. 2020

Sci Rep

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For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.

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Section: Introductionmentioning

confidence: 99%

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Bragança

Broomé

Rhodin

et al. 2020

Sci Rep

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“…There are currently three scoping reviews reported in the equine veterinary literature, all published between 2017–2019. These include a scoping review of equine movement/gait analysis [66], a scoping review of systematic reviews and meta-analyses for bovine and equine veterinarians [67], and a scoping review of acupuncture in companion animals [68].…”

Section: Discussionmentioning

confidence: 99%

Risk factors for acute abdominal pain (colic) in the adult horse: A scoping review of risk factors, and a systematic review of the effect of management-related changes

et al. 2019

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Acute abdominal pain (colic) is the most common reason for emergency veterinary treatment in the horse. Consolidation of data through a systematic review is important to inform evidence-based medicine and clinical guidelines, but there are currently no published systematic reviews on colic in the horse. The aim of this study was to identify, categorize and appraise the evidence on factors associated with increased risk of developing abdominal pain (colic) due to gastrointestinal disease in the adult horse. A scoping review was performed to identify and categorize evidence on all risk factors for colic. A systematic review of management-related risk factors was then performed following PRISMA guidelines. Both searches were conducted in Medline, CAB Abstracts and Web of Science databases, and publications were assessed against inclusion and exclusion criteria. For the scoping review, study and participant characteristics of included publications and key results were extracted and tabulated. For the systematic review, cohort, case-control or cross-sectional studies investigating acute abdominal pain in horses within two weeks of management changes were assessed. Study characteristics, participant characteristics and study results of included publications for the systematic review were extracted and tabulated. Included publications were appraised using the Joanna Briggs Institute Critical Appraisal Tools for cohort, case-control and cross-sectional studies. The scoping review search identified 3,756 publications. Fifty eight studies met final inclusion criteria, and 22 categories of risk factors were identified. These were grouped into three broad areas: horse-related factors, management-related factors and environment-related factors. The largest body of evidence related to management change. The systematic review of management change identified 410 publications: 14 met inclusion criteria for analysis. These consisted of one cohort, eight case-control and five cross-sectional studies. The studies were conducted between 1990–2008, and the majority of studies were located in the USA (8/14) or UK (3/14). The risk factors related to management change that were assessed were feed, carer, exercise, pasture, water and housing. The largest bodies of evidence for increased risk of colic associated with management change were changes in feed (5/14 publications) and recent change in housing (3/14). Most studies (8/14) did not meet the JBI criterion on confounding factors. There was marked heterogeneity of study methodologies and measures. This is the first study to use a combined scoping and systematic review to analyse evidence for modifiable risk factors for a common condition in the horse. It provides a comprehensive review that will be a key resource for researchers, veterinary practitioners and horse owners. It identified modifiable risk factors associated with an increased risk of colic which should be a key target for preventative health programmes. The findings from the critical appraisal were used to develop rec...

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“…These events can be examined visually but due to the limitations of the temporal resolution of the human eye [2] there are limitations to how well these events can be distinguished. Instead, objective measurement tools such as force plates, optical motion capture (OMC) systems and inertial measurement units (IMUs) are used [3].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, data collection is time consuming [4] and this method can only be used in a laboratory settings. In addition, multiple consecutive strides can only be measured with a force measuring treadmill and by force measuring shoes [3], which can alter the kinematics [5,6]. OMC systems and IMUs can also be used to measure consecutive strides and OMC systems are considered the gold standard for kinematic gait analysis.…”

Section: Introductionmentioning

confidence: 99%

“…However, analysis of the data and extraction of hoof-events was performed manually or semi-manually which is time consuming and subjective. Time reduction and objectivity can be gained by developing an algorithm for automatic detection of hoof-on and hoof-off events from the output of the IMUs [3]. The output of the IMUs consists of tri-axial acceleration and angular velocity signals.…”

Section: Introductionmentioning

confidence: 99%

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Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors

et al. 2020

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For gait classification, hoof-on and hoof-off events are fundamental locomotion characteristics of interest. These events can be measured with inertial measurement units (IMUs) which measure the acceleration and angular velocity in three directions. The aim of this study was to present two algorithms for automatic detection of hoof-events from the acceleration and angular velocity signals measured by hoof-mounted IMUs in walk and trot on a hard surface. Seven Warmblood horses were equipped with two wireless IMUs, which were attached to the lateral wall of the right front (RF) and hind (RH) hooves. Horses were walked and trotted on a lead over a force plate for internal validation. The agreement between the algorithms for the acceleration and angular velocity signals with the force plate was evaluated by Bland Altman analysis and linear mixed model analysis. These analyses were performed for both hoof-on and hoof-off detection and for both algorithms separately. For the hoof-on detection, the angular velocity algorithm was the most accurate with an accuracy between 2.39 and 12.22 ms and a precision of around 13.80 ms, depending on gait and hoof. For hoof-off detection, the acceleration algorithm was the most accurate with an accuracy of 3.20 ms and precision of 6.39 ms, independent of gait and hoof. These algorithms look highly promising for gait classification purposes although the applicability of these algorithms should be investigated under different circumstances, such as different surfaces and different hoof trimming conditions.

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Research trends in equine movement analysis, future opportunities and potential barriers in the digital age: A scoping review from 1978 to 2018

Cited by 31 publications

References 76 publications

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning

Risk factors for acute abdominal pain (colic) in the adult horse: A scoping review of risk factors, and a systematic review of the effect of management-related changes

Automatic hoof-on and -off detection in horses using hoof-mounted inertial measurement unit sensors

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