Smartphone-Based Pelvic Movement Asymmetry Measures for Clinical Decision Making in Equine Lameness Assessment

Marunova, Eva; Dod, Leea; Witte, Stefan; Pfau, Thilo

doi:10.3390/ani11061665

Cited by 9 publications

(8 citation statements)

References 34 publications

(74 reference statements)

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“…Associated with the highest sensitivity and specificity, AI-up_P discriminates LH lame horses under −7% of asymmetry and RH lame horses over +18% of asymmetry from sound horses. These observations confirm that the upward movement of the pelvis has the highest power to discriminate hindlimb lameness [ 30 ].…”

Section: Discussionsupporting

confidence: 80%

Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line

Macaire

Hanne-Poujade²,

Azevedo

et al. 2022

Animals

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Defining whether a gait asymmetry should be considered as lameness is challenging. Gait analysis systems now provide relatively accurate objective data, but their interpretation remains complex. Thresholds for discriminating between horses that are visually assessed as being lame or sound, as well as thresholds for locating the lame limb with precise sensitivity and specificity are essential for accurate interpretation of asymmetry measures. The goal of this study was to establish the thresholds of asymmetry indices having the best sensitivity and specificity to represent the visual single-limb lameness assessment made by expert veterinarians as part of their routine practice. Horses included in this study were evaluated for locomotor disorders at a clinic and equipped with the EQUISYM® system using inertial measurement unit (IMU) sensors. Visual evaluation by expert clinicians allocated horses into five groups: 49 sound, 62 left forelimb lame, 67 right forelimb lame, 23 left hindlimb lame, and 23 right hindlimb lame horses. 1/10 grade lame horses were excluded. Sensors placed on the head (_H), the withers (_W), and the pelvis (_P) provided vertical displacement. Relative difference of minimal (AI-min) and maximal (AI-max) altitudes, and of upward (AI-up) and downward (AI-down) amplitudes between right and left stance phases were calculated. Receiver operating characteristic (ROC) curves discriminating the sound horses from each lame limb group revealed the threshold of asymmetry indice associated with the best sensitivity and specificity. AI-up_W had the best ability to discriminate forelimb lame horses from sound horses with thresholds (left: −7%; right: +10%) whose sensitivity was greater than 84% and specificity greater than 88%. AI-up_P and AI-max_P discriminated hindlimb lame horses from sound horses with thresholds (left: −7%; right: +18% and left: −10%; right: +6%) whose sensitivity was greater than 78%, and specificity greater than 82%. Identified thresholds will enable the interpretation of quantitative data from lameness quantification systems. This study is mainly limited by the number of included horses and deserves further investigation with additional data, and similar studies on circles are warranted.

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Section: Discussionsupporting

confidence: 80%

Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line

Macaire

Hanne-Poujade²,

Azevedo

et al. 2022

Animals

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“…Such data can be used to classify horses, define phenotypes and possibly relate their behaviour to genomic data. Furthermore, data obtained from mobile phone sensors has been used for lameness detection Pfau and Weller (2017); Marunova et al (2021) but such studies could benefit from the labelling approach we used here. Sensors attached to a horse have been used to identify lameness (Keegan et al, 2011;McCracken et al, 2012;Yigit et al, 2020) with commercial products available 9 .…”

Section: Discussionmentioning

confidence: 99%

Efficient development of gait classification models for five-gaited horses

Davíðsson

Rees²,

Ólafsdóttir

et al. 2022

Preprint

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In this study, we perform horse gait classification using mobile phone sensors located in the rider’s pocket. We train a classifier using data obtained simultaneously from a mobile phone and a gait label acquired from a device based on four wearable sensors attached to a horse’s limbs. With this efficient approach to acquire labelled data, we train a gait classification model and demonstrate that data from mobile phones can be used to classify the five gaits of the Icelandic horse with up to 98% accuracy. The result suggests that horseriding activities can be studied at a large scale using mobile phones to gather data on gaits.

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“…With an increasing diversity of camera-and IMU-based systems available [5,[9][10][11][12], the question of "comparability" is of growing importance. In particular, systems with minimal infrastructure requirements are opening the door for larger-scale and/or longitudinal followup studies.…”

Section: Introductionmentioning

confidence: 99%

Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses

Pfau,

Landsbergen,

Davis

et al. 2023

Sensors

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Background: With an increasing number of systems for quantifying lameness-related movement asymmetry, between-system comparisons under non-laboratory conditions are important for multi-centre or referral-level studies. This study compares an artificial intelligence video app to a validated inertial measurement unit (IMU) gait analysis system in a specific group of horses. Methods: Twenty-two reining Quarter horses were equipped with nine body-mounted IMUs while being videoed with a smartphone app. Both systems quantified head and pelvic movement symmetry during in-hand trot (hard/soft ground) and on the lunge (left/right rein, soft ground). Proportional limits of agreement (pLoA) were established. Results: Widths of pLoA were larger for head movement (29% to 50% in-hand; 22% to 38% on lunge) than for pelvic movement (13% to 24% in-hand; 14% to 24% on lunge). Conclusion: The between-system pLoAs exceed current “lameness thresholds” aimed at identifying the affected limb(s) in lame horses. They also exceed published limits of agreement for stride-matched data but are similar to repeatability values and “lameness thresholds” from “non-lame” horses. This is encouraging for multi-centre studies and referral-level veterinary practice. The narrower pLoA values for pelvic movement asymmetry are particularly encouraging, given the difficulty of grading hind limb lameness “by eye”.

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Smartphone-Based Pelvic Movement Asymmetry Measures for Clinical Decision Making in Equine Lameness Assessment

Cited by 9 publications

References 34 publications

Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line

Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line

Efficient development of gait classification models for five-gaited horses

Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses

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