| 1. |
- Ask, Katrina, et al.
(författare)
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Kinematic gait characteristics of straight line walk in clinically sound dairy cows
- 2021
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Ingår i: PLoS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this study is to describe the kinematic gait characteristics of straight line walk in clinically sound dairy cows using body mounted Inertial Measurement Units (IMUs) at multiple anatomical locations. The temporal parameters used are speed and non-speed normalized stance duration, bipedal and tripedal support durations, maximal protraction and retraction angles of the distal limbs and vertical displacement curves of the upper body. Gait analysis was performed by letting 17 dairy cows walk in a straight line at their own chosen pace while equipped with IMU sensors on tubera sacrale, left and right tuber coxae (LTC and RTC), back, withers, head, neck and all four lower limbs. Data intervals with stride by stride regularity were selected based on video data. For temporal parameters, the median was calculated and 95% confidence intervals (CI) were estimated based on linear mixed model (LMM) analysis, while for limb and vertical displacement curves, the median and most typical curves were calculated. The temporal parameters and distal limb angles showed consistent results with low variance and LMM analysis showed non-overlapping CI for all temporal parameters. The distal limb angle curves showed a larger and steeper retraction angle range for the distal front limbs compared with the hind limbs. The vertical displacement curves of the sacrum, withers, LTC and RTC showed a consistent sinusoidal pattern while the head, back and collar curves were less consistent and showed more variation between and within cows. This kinematic description might allow to objectively differentiate between normal and lame gait in the future and determine the best anatomical location for sensor attachment for lameness detection purposes.
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| 2. |
- Ask, Katrina, et al.
(författare)
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Performance of four equine pain scales and their association to movement asymmetry in horses with induced orthopedic pain
- 2022
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Ingår i: Frontiers in Veterinary Science. - : Frontiers Media SA. - 2297-1769. ; 9
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Tidskriftsartikel (refereegranskat)abstract
- Objective: This study investigated the relationship between orthopedic pain experienced at rest, and degree of movement asymmetry during trot in horses with induced reversible acute arthritis. Orthopedic pain was assessed with the Horse Grimace Scale (HGS), the Equine Utrecht University Scale of Facial Assessment of Pain (EQUUS-FAP), the Equine Pain Scale (EPS), and the Composite Orthopedic Pain Scale (CPS). Reliability and diagnostic accuracy were evaluated with intraclass correlation coefficients (ICC) and area under the curve (AUC).Study design and animals: Eight healthy horses were included in this experimental study, with each horse acting as its own control.Methods: Orthopedic pain was induced by intra-articular lipopolysaccharide (LPS) administration. Serial pain assessments were performed before induction and during pain progression and regression, where three observers independently and simultaneously assessed pain at rest with the four scales. Movement asymmetry was measured once before induction and a minimum of four times after induction, using objective gait analysis.Results: On average 6.6 (standard deviation 1.2) objective gait analyses and 12.1 (2.4) pain assessments were performed per horse. The ICC for each scale was 0.75 (CPS), 0.65 (EPS), 0.52 (HGS), and 0.43 (EQUUS-FAP). Total pain scores of all scales were significantly associated with an increase in movement asymmetry (R2 values ranging from −0.0649 to 0.493); with CPS pain scores being most closely associated with movement asymmetry. AUC varied between scales and observers, and CPS was the only scale where all observers had a good diagnostic accuracy (AUC > 0.72).Conclusions and clinical relevance: This study identified significant associations between pain experienced at rest and degree of movement asymmetry for all scales. Pain scores obtained using CPS were most closely associated with movement asymmetry. CPS was also the most accurate and reliable pain scale. All scales had varying linear and non-linear relations between total pain scores and movement asymmetry, illustrating challenges with orthopedic pain assessment during rest in subtly lame horses since movement asymmetry needs to be rather high before total pain score increase.
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| 3. |
- Ask, Katrina
(författare)
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The look of lameness - Behaviors and facial expressions associated with orthopedic pain in horses
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are increasing concerns about equine welfare in equestrian sports, where early detection of orthopedic pain remains a major challenge since reliable and valid pain assessment tools are lacking. Movement asymmetry may be present in horses perceived as free from lameness by their owners, as well as in horses with confirmed orthopedic pain. It is therefore important to differentiate movement asymmetry due to pain from that due to other reasons, which may be achievable by improving orthopedic pain assessment. The aim of this thesis was thus to identify body behaviors and changes in facial activity related to orthopedic pain and movement asymmetry in horses. Progression and regression of movement asymmetry after induced orthopedic pain was monitored and measured with gait analysis in eight horses. A number of behaviors including altered posture, head position, location in the box stall, focus and human interaction were found to be associated with orthopedic pain, as were facial expressions. Only one of four equine pain scales tested detected orthopedic pain reliably and accurately. Dynamic and diverse facial displays were identified in resting and moving horses during pain, illustrating that the concept of one prototypical pain face may be a simplification of the full pain-related facial repertoire. Horses trotted by hand showed a great inter-individual variation in facial expressiveness, highlighting the need for further analysis of facial activity during motion before its use for pain detection. The new knowledge on the relationship between pain and movement asymmetry provided in this thesis, can lead to improved pain assessments, pain management and equine welfare.
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| 4. |
- Broomé, Sofia, et al.
(författare)
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Sharing pain : Using pain domain transfer for video recognition of low grade orthopedic pain in horses
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:3, s. e0263854-
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Tidskriftsartikel (refereegranskat)abstract
- Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
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| 5. |
- Haubro Andersen, Pia, et al.
(författare)
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Towards Machine Recognition of Facial Expressions of Pain in Horses
- 2021
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Ingår i: Animals. - : MDPI. - 2076-2615. ; 11:6
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Forskningsöversikt (refereegranskat)abstract
- Simple Summary Facial activity can convey valid information about the experience of pain in a horse. However, scoring of pain in horses based on facial activity is still in its infancy and accurate scoring can only be performed by trained assessors. Pain in humans can now be recognized reliably from video footage of faces, using computer vision and machine learning. We examine the hurdles in applying these technologies to horses and suggest two general approaches to automatic horse pain recognition. The first approach involves automatically detecting objectively defined facial expression aspects that do not involve any human judgment of what the expression "means". Automated classification of pain expressions can then be done according to a rule-based system since the facial expression aspects are defined with this information in mind. The other involves training very flexible machine learning methods with raw videos of horses with known true pain status. The upside of this approach is that the system has access to all the information in the video without engineered intermediate methods that have filtered out most of the variation. However, a large challenge is that large datasets with reliable pain annotation are required. We have obtained promising results from both approaches. Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.
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| 6. |
- Leclercq, Anna, et al.
(författare)
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Perceived sidedness and correlation to vertical movement asymmetries in young warmblood horses
- 2023
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Ingår i: PLoS ONE. - 1932-6203. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- The prevalence of vertical asymmetries is high in "owner-sound" warmblood riding horses, however the origin of these asymmetries is unknown. This study investigated correlations between vertical asymmetries and motor laterality. Young warmblood riding horses (N = 65), perceived as free from lameness were evaluated on three visits, each comprising objective gait analysis (inertial measurement units system) and a rider questionnaire on perceived sidedness of the horse. A subgroup (N = 40) of horses were also subjected to a forelimb protraction preference test intended as an assessment of motor laterality. We hypothesized associations between vertical asymmetry and motor laterality as well as rider-perceived sidedness. Vertical asymmetry was quantified as trial means of the stride-by-stride difference between the vertical displacement minima and maxima of the head (HDmin, HDmax) and pelvis (PDmin, PDmax). Laterality indexes, based on counts of which limb was protracted, and binomial tests were used to draw conclusions from the preference tests. In the three visits, 60-70% of horses exhibited vertical asymmetries exceeding clinically used thresholds for & GE;1 parameter, and 22% of horses exhibited a side preference in the preference test as judged by binomial tests. Linear mixed models identified a weak but statistically significant correlation between perceived hindlimb weakness and higher PDmin values attributable to either of the hindlimbs (p = 0.023). No other statistically significant correlations to vertical asymmetry were seen for any of the questionnaire answers tested. Tests of correlation between the absolute values of laterality index and asymmetry parameters (HDmin, HDmax, PDmin, PDmax) identified a weak correlation (p = 0.049) with PDmax, but when accounting for the direction of asymmetry and motor laterality, no correlations were seen for either of the asymmetry parameters. No convincing evidence of associations between vertical asymmetries and motor laterality were seen and further studies investigating motor laterality and the origin of vertical asymmetries are needed.
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| 7. |
- Rashid, Maheen, et al.
(författare)
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Equine Pain Behavior Classification via Self-Supervised Disentangled Pose Representation
- 2022
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Ingår i: 2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 152-162
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Konferensbidrag (refereegranskat)abstract
- Timely detection of horse pain is important for equine welfare. Horses express pain through their facial and body behavior, but may hide signs of pain from unfamiliar human observers. In addition, collecting visual data with detailed annotation of horse behavior and pain state is both cumbersome and not scalable. Consequently, a pragmatic equine pain classification system would use video of the un-observed horse and weak labels. This paper proposes such a method for equine pain classification by using multi-view surveillance video footage of unobserved horses with induced orthopaedic pain, with temporally sparse video level pain labels. To ensure that pain is learned from horse body language alone, we first train a self-supervised generative model to disentangle horse pose from its appearance and background before using the disentangled horse pose latent representation for pain classification. To make best use of the pain labels, we develop a novel loss that formulates pain classification as a multi-instance learning problem. Our method achieves pain classification accuracy better than human expert performance with 60% accuracy. The learned latent horse pose representation is shown to be viewpoint covariant, and disentangled from horse appearance. Qualitative analysis of pain classified segments shows correspondence between the pain symptoms identified by our model, and equine pain scales used in veterinary practice.
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| 8. |
- Rhodin, Marie, et al.
(författare)
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Identification of Body Behaviors and Facial Expressions Associated with Induced Orthopedic Pain in Four Equine Pain Scales
- 2020
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Ingår i: Animals. - : MDPI AG. - 2076-2615. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- Simple SummaryPain scales are tools developed to improve pain assessment in horses. They are based on behaviors and/or facial expressions, and the observer allocates a score based on the character of the behavior or facial expression. Little is known about behaviors and facial expressions at rest in horses with orthopedic pain since pain is mainly assessed by lameness evaluation during movement. The aim of this study was to describe how closely equine behaviors and facial expressions are associated with movement asymmetry and to identify combinations of behavior and expressions present in horses with induced orthopedic pain. Orthopedic pain was induced in eight horses and assessed in two ways; using four existing equine pain scales at rest, and by measuring movement asymmetry during movement. The association of behavior and facial expression items in the pain scales with actual lameness was analyzed. Posture-related behavior showed the strongest association, while facial expressions varied between horses. These results show that pain scales for orthopedic pain assessment would benefit from including posture, head position, location in the box stall, focus, interactive behavior, and facial expressions. This could improve orthopedic pain detection in horses during rest with mild lameness.Equine orthopedic pain scales are targeted towards horses with moderate to severe orthopedic pain. Improved assessment of pain behavior and pain-related facial expressions at rest may refine orthopedic pain detection for mild lameness grades. Therefore, this study explored pain-related behaviors and facial expressions and sought to identify frequently occurring combinations. Orthopedic pain was induced by intra-articular LPS in eight horses, and objective movement asymmetry analyses were performed before and after induction together with pain assessments at rest. Three observers independently assessed horses in their box stalls, using four equine pain scales simultaneously. Increase in movement asymmetry after induction was used as a proxy for pain. Behaviors and facial expressions commonly co-occurred and were strongly associated with movement asymmetry. Posture-related scale items were the strongest predictors of movement asymmetry. Display of facial expressions at rest varied between horses but, when present, were strongly associated with movement asymmetry. Reliability of facial expression items was lower than reliability of behavioral items. These findings suggest that five body behaviors (posture, head position, location in the box stall, focus, and interactive behavior) should be included in a scale for live assessment of mild orthopedic pain. We also recommend inclusion of facial expressions in pain assessment.
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| 9. |
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