Analysis of Motion Patterns for Pain Estimation of Horses

Reulke, Ralf; Rues, Dominik; Deckers, Niklas; Barnewitz, Dirk; Wieckert, Anne; Kienapfel, Kathrin

doi:10.1109/avss.2018.8639330

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…In this section, the meta-analysis of the works in Table 1 is organized according to the different stages of a typical workflow in studies within this domain: data collection and annotation, followed by data analysis (typically, model training and inference) and last, performance evaluation. For each of these stages, we classify the methods and techniques applied in these [76] unknown or naturally occurring face + Lencioni et al [77] Horses pain surgical castration face + Hummel et al [38] unknown or induced pain face + Broomé et al [78] induced pain body and face + Broomé et al [79] induced pain body and face + Rashid et al [80] induced pain body + Reulke et al [81] vet. procedure body -Corujo et al [82] emotion unknown body and face + Li et al [83] --face ---Feightelstein et al [84] Cats pain vet.…”

Section: Meta-analysis Of Computer Vision-based Approaches For Classi...mentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

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Advances in animal motion tracking and pose recognition have been a game changer in the study of animal behavior. Recently, an increasing number of works go 'deeper' than tracking, and address automated recognition of animals' internal states such as emotions and pain with the aim of improving animal welfare, making this a timely moment for a systematization of the field. This paper provides a comprehensive survey of computer vision-based research on recognition of affective states and pain in animals, addressing both facial and bodily behavior analysis. We summarize the efforts that have been presented so far within this topic -classifying them across different dimensions, highlight challenges and research gaps, and provide best practice recommendations for advancing the field, and some future directions for research.

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Section: Meta-analysis Of Computer Vision-based Approaches For Classi...mentioning

confidence: 99%

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Broomé¹,

Feighelstein²,

Zamansky³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, our proposed system will identify the mood and state of the pet. Related literature [20] observes the difference in horse behavior before and after surgery, inferring different pain levels based on the condition of the surgical wound and observes the horse's behavioral performance at different levels of pain. This paper only observes the horse's displacement in space.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation

et al. 2020

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Image identification, machine learning and deep learning technologies have been applied in various fields. However, the application of image identification currently focuses on object detection and identification in order to determine a single momentary picture. This paper not only proposes multiple feature dependency detection to identify key parts of pets (mouth and tail) but also combines the meaning of the pet’s bark (growl and cry) to identify the pet’s mood and state. Therefore, it is necessary to consider changes of pet hair and ages. To this end, we add an automatic optimization identification module subsystem to respond to changes of pet hair and ages in real time. After successfully identifying images of featured parts each time, our system captures images of the identified featured parts and stores them as effective samples for subsequent training and improving the identification ability of the system. When the identification result is transmitted to the owner each time, the owner can get the current mood and state of the pet in real time. According to the experimental results, our system can use a faster R-CNN model to improve 27.47%, 68.17% and 26.23% accuracy of traditional image identification in the mood of happy, angry and sad respectively.

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“…With the introduction of data processing [22][23][24][25][26], the development of technology for biometric measurements [24][25][26][27][28][29][30][31][32][33] and its introduction to equine medicine and care, there is a growing need to develop innovations that support the diagnosis of the rider:horse bodyweight ratio. According to the World Horse Welfare (WHW) and the British Equestrian Federation (BEF), riders should have the possibility to assess if they represent an appropriate bodyweight concerning their horse's size; thus, every effort should be made to develop innovative assessment methods for the rider-horse fit [17].…”

Section: Introductionmentioning

confidence: 99%

“…According to the World Horse Welfare (WHW) and the British Equestrian Federation (BEF), riders should have the possibility to assess if they represent an appropriate bodyweight concerning their horse's size; thus, every effort should be made to develop innovative assessment methods for the rider-horse fit [17]. The use of mathematical classification [22,23] and machine learning [24][25][26][27] methods constitute a new direction in the development of monitoring equine physiology [22,24] and the reaction of the horse's organism to exercise loads [23,25] and diseases [26,27]. In the field of non-invasive horse monitoring, there is progress in the use of biometric devices, including inertial measurement units (IMU) to monitor motion speed [24], motion caption [28], and breath-jockey to assess the respiratory rate and kinematic parameters of performance horses [29], single inertial sensor (SIS) for the evaluation of the neuromusculoskeletal system of performance horses [30,31], and infrared thermography (IRT) for quantification of the radiant energy emitted by the body surface, which is proportional to the horse's effort intensity [32,33].…”

Section: Introductionmentioning

confidence: 99%

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

Domino

Borowska

Zdrojkowski

et al. 2022

Sensors

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As obesity is a serious problem in the human population, overloading of the horse’s thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse’s back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.

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Analysis of Motion Patterns for Pain Estimation of Horses

Cited by 6 publications

References 16 publications

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States

Multiple Feature Dependency Detection for Deep Learning Technology—Smart Pet Surveillance System Implementation

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding

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