Search Results (12)

This study introduces an innovative dog emotion classification system that integrates four non-invasive physiological indicators—skin potential (SP), muscle potential (MP), respiration frequency (RF), and voice pattern (VP)—with the extreme gradient boosting (XGBoost) algorithm. A four-breed dataset was meticulously constructed by recording and labeling physiological signals from dogs exposed to four fundamental emotional states: happiness, sadness, fear, and anger. Comprehensive feature extraction (time-domain, frequency-domain, nonlinearity) was conducted for each signal modality, and inter-emotional variance was analyzed to establish discriminative patterns. Four machine learning algorithms—Neural Networks (NN), Support Vector Machines (SVM), Gradient Boosting Decision Trees (GBDT), and XGBoost—were trained and evaluated, with XGBoost achieving the highest classification accuracy of 90.54%. Notably, this is the first study to integrate a fusion of two complementary electrophysiological indicators—skin and muscle potentials—into a multi-modal dataset for canine emotion recognition. Further interpretability analysis using Shapley Additive exPlanations (SHAP) revealed skin potential and voice pattern features as the most contributive to model performance. The proposed system demonstrates high accuracy, efficiency, and portability, laying a robust groundwork for future advancements in cross-species affective computing and intelligent animal welfare technologies. Full article

This paper proposes a multi–convolutional neural network (CNN)-based system for the detection, tracking, and recognition of the emotions of dogs in surveillance videos. This system detects dogs in each frame of a video, tracks the dogs in the video, and recognizes the dogs’ emotions. The system uses a YOLOv3 model for dog detection. The dogs are tracked in real time with a deep association metric model (DeepDogTrack), which uses a Kalman filter combined with a CNN for processing. Thereafter, the dogs’ emotional behaviors are categorized into three types—angry (or aggressive), happy (or excited), and neutral (or general) behaviors—on the basis of manual judgments made by veterinary experts and custom dog breeders. The system extracts sub-images from videos of dogs, determines whether the images are sufficient to recognize the dogs’ emotions, and uses the long short-term deep features of dog memory networks model (LDFDMN) to identify the dog’s emotions. The dog detection experiments were conducted using two image datasets to verify the model’s effectiveness, and the detection accuracy rates were 97.59% and 94.62%, respectively. Detection errors occurred when the dog’s facial features were obscured, when the dog was of a special breed, when the dog’s body was covered, or when the dog region was incomplete. The dog-tracking experiments were conducted using three video datasets, each containing one or more dogs. The highest tracking accuracy rate (93.02%) was achieved when only one dog was in the video, and the highest tracking rate achieved for a video containing multiple dogs was 86.45%. Tracking errors occurred when the region covered by a dog’s body increased as the dog entered or left the screen, resulting in tracking loss. The dog emotion recognition experiments were conducted using two video datasets. The emotion recognition accuracy rates were 81.73% and 76.02%, respectively. Recognition errors occurred when the background of the image was removed, resulting in the dog region being unclear and the incorrect emotion being recognized. Of the three emotions, anger was the most prominently represented; therefore, the recognition rates for angry emotions were higher than those for happy or neutral emotions. Emotion recognition errors occurred when the dog’s movements were too subtle or too fast, the image was blurred, the shooting angle was suboptimal, or the video resolution was too low. Nevertheless, the current experiments revealed that the proposed system can correctly recognize the emotions of dogs in videos. The accuracy of the proposed system can be dramatically increased by using more images and videos for training the detection, tracking, and emotional recognition models. The system can then be applied in real-world situations to assist in the early identification of dogs that may exhibit aggressive behavior. Full article

While the financial and social support afforded to United Kingdon (UK) ‘animals other than human animals’ (AOTHAs) welfare charities, such as the RSPCA and Dogs Trust, could suggest that they are valued intrinsically within Western society criminological texts largely omit them from recognition, with some important exceptions, see non-speciesist criminology. Most human animals likely do not want to directly or indirectly harm AOTHAs and even value relationships with “companion” AOTHAs. Regardless, AOTHAs have been victimized throughout history and continue to be. This article examines harm to AOTHAs in the context of the global COVID-19 pandemic to argue that abuse proliferates where harmful subjectivities are generated by society’s acceptance of (1) the anthropocentric culture, and (2) when humanity values their individual advancement within the competitive consumer culture. Companion dogs were specifically focused upon within this article due to their so called close emotional and physical proximity to human animals, with proximity meaning that they were directly impacted by the lockdown measures implemented. The coronavirus pandemic was addressed by governments throughout the world by initiating an array of social restrictions. Because of these social restrictions, millions of individuals within England, and in countries such as the United States of America (USA), decided to adopt or purchase dogs for a variety of reasons, including to help them mitigate feelings of isolation and loneliness and to provide them with an excuse to participate in outdoor exercise. In order to determine the impact that the coronavirus pandemic has had upon the plight of domesticated companion dogs within England, semi-structured interviews, document analyses, and observation research were undertaken. The initial analysis of data presented here suggests that the coronavirus pandemic threatened the wellbeing of dogs within England, with their reproductive, physical, medical, and psychological wellbeing being put at risk. Full article

In this new era, it is no longer impossible to create a smart home environment around the household. Moreover, users are not limited to humans but also include pets such as dogs. Dogs need long-term close companionship with their owners; however, owners may occasionally need to be away from home for extended periods of time and can only monitor their dogs’ behaviors through home security cameras. Some dogs are sensitive and may develop separation anxiety, which can lead to disruptive behavior. Therefore, a novel smart home solution with an affective recommendation module is proposed by developing: (1) an application to predict the behavior of dogs and, (2) a communication platform using smartphones to connect with dog friends from different households. To predict the dogs’ behaviors, the dog emotion recognition and dog barking recognition methods are performed. The ResNet model and the sequential model are implemented to recognize dog emotions and dog barks. The weighted average is proposed to combine the prediction value of dog emotion and dog bark to improve the prediction output. Subsequently, the prediction output is forwarded to a recommendation module to respond to the dogs’ conditions. On the other hand, the Real-Time Messaging Protocol (RTMP) server is implemented as a platform to contact a dog’s friends on a list to interact with each other. Various tests were carried out and the proposed weighted average led to an improvement in the prediction accuracy. Additionally, the proposed communication platform using basic smartphones has successfully established the connection between dog friends. Full article

This paper describes an emotion recognition system for dogs automatically identifying the emotions anger, fear, happiness, and relaxation. It is based on a previously trained machine learning model, which uses automatic pose estimation to differentiate emotional states of canines. Towards that goal, we have compiled a picture library with full body dog pictures featuring 400 images with 100 samples each for the states “Anger”, “Fear”, “Happiness” and “Relaxation”. A new dog keypoint detection model was built using the framework DeepLabCut for animal keypoint detector training. The newly trained detector learned from a total of 13,809 annotated dog images and possesses the capability to estimate the coordinates of 24 different dog body part keypoints. Our application is able to determine a dog’s emotional state visually with an accuracy between 60% and 70%, exceeding human capability to recognize dog emotions. Full article

Animals’ facial expressions are involuntary responses that serve to communicate the emotions that individuals feel. Due to their close co-existence with humans, broad attention has been given to identifying these expressions in certain species, especially dogs. This review aims to analyze and discuss the advances in identifying the facial expressions of domestic dogs and their clinical utility in recognizing pain as a method to improve daily practice and, in an accessible and effective way, assess the health outcome of dogs. This study focuses on aspects related to the anatomy and physiology of facial expressions in dogs, their emotions, and evaluations of their eyebrows, eyes, lips, and ear positions as changes that reflect pain or nociception. In this regard, research has found that dogs have anatomical configurations that allow them to generate changes in their expressions that similar canids—wolves, for example—cannot produce. Additionally, dogs can perceive emotions similar to those of their human tutors due to close human-animal interaction. This phenomenon—called “emotional contagion”—is triggered precisely by the dog’s capacity to identify their owners’ gestures and then react by emitting responses with either similar or opposed expressions that correspond to positive or negative stimuli, respectively. In conclusion, facial expressions are essential to maintaining social interaction between dogs and other species, as in their bond with humans. Moreover, this provides valuable information on emotions and the perception of pain, so in dogs, they can serve as valuable elements for recognizing and evaluating pain in clinical settings. Full article

Animal-assisted Interventions (AAI) proliferated rapidly since clinicians and researchers first noted the positive effects animals have on people struggling with physical and mental health concerns. The intersection of AAI with the field of animal welfare evolved from considering animals’ basic needs, such as freedom from pain, to recognition that animals experience nuanced emotions. Current conceptualizations of the various roles of companion animals as an adjunct to treatments for humans emphasize not only the animals’ physical comfort and autonomy, but also their mental well-being and enjoyment of AAI activities. However, numerous challenges to effective monitoring of animals involved in AAI exist. This article focuses specifically on dogs, highlighting factors that may lead handlers and therapists to miss or ignore canine stress signals during human-animal interactions and offers strategies to recognize and ameliorate dogs’ distress more consistently. The primary goals of this discussion are to summarize the current thinking on canine well-being and to highlight practical applications of animal welfare principles in real-world AAI settings. The paper highlights contextual factors (e.g., physical setting, patient demand), human influences (e.g., desire to help), and intervention characteristics (e.g., presence or absence of a dog-specific advocate) that may promote or inhibit humans’ ability to advocate for therapy dogs during AAI activities. Deidentified examples of each of these factors are discussed, and recommendations are provided to mitigate factors that interfere with timely recognition and amelioration of canine distress. Full article

Olfaction in dogs is crucial for gathering important information about the environment, recognizing individuals, making decisions, and learning. It is far more specialized and sensitive than humans’ sense of smell. Using the strength of dogs’ sense of smell, humans work with dogs for the recognition of different odors, with a precision far exceeding the analytical capabilities of most modern instruments. Due to their extremely sensitive sense of smell, dogs could be used as modern, super-sensitive mobile area scanners, detecting specific chemical signals in real time in various environments outside the laboratory, and then tracking the odor of dynamic targets to their source, also in crowded places. Recent studies show that dogs can detect not only specific scents of drugs or explosives, but also changes in emotions as well as in human cell metabolism during various illnesses, including COVID-19 infection. Here, we provide an overview of canine olfaction, discussing aspects connected with anatomy, physiology, behavioral aspects of sniffing, and factors influencing the olfactory abilities of the domestic dog (Canis familiaris). Full article

The detection of a congenital heart defect at purchase is an important step in early detection from a clinical and legal standpoint. Indeed, some cardiac abnormalities may be corrected with surgery, and very often, treatment needs to be performed early before congestive heart failure or irreversible heart damage can occur. From a legal viewpoint, if the defect is revealed in a newly purchased puppy, the buyer may be required to return it and receive compensation. Puppies affected with congenital heart defects are likely to die prematurely, causing emotional suffering to the owner. Furthermore, by considering breed predisposition, early recognition allows breeders to avoid breeding from particular dogs with genetic defects and prevent the continuation of genetic defects in breeding lines. Given gaps in the literature about the recognition of murmurs in the puppy trade, the present article describes how to identify a heart murmur in a puppy during a pre-purchase examination and its significance from a clinical and legal viewpoint. In the canine population, the prevalence of cardiac defects ranges between 0.13 and 1.6%. Pulmonic stenosis is the most common defect found in puppies, followed by patent ductus arteriosus, subaortic stenosis, and ventricular septal defect. On the basis of the above considerations, the veterinarian should recognize and identify the murmur following a protocol for routine examination of puppies involved in trade. Full article

Although the domestic cat (Felis catus) is probably the most widespread companion animal in the world and interacts in a complex and multifaceted way with humans, the human–cat relationship and reciprocal communication have received far less attention compared, for example, to the human–dog relationship. Only a limited number of studies have considered what people understand of cats’ human-directed vocal signals during daily cat–owner interactions. The aim of the current study was to investigate to what extent adult humans recognize cat vocalizations, namely meows, emitted in three different contexts: waiting for food, isolation, and brushing. A second aim was to evaluate whether the level of human empathy toward animals and cats and the participant’s gender would positively influence the recognition of cat vocalizations. Finally, some insights on which acoustic features are relevant for the main investigation are provided as a serendipitous result. Two hundred twenty-five adult participants were asked to complete an online questionnaire designed to assess their knowledge of cats and to evaluate their empathy toward animals (Animal Empathy Scale). In addition, participants had to listen to six cat meows recorded in three different contexts and specify the context in which they were emitted and their emotional valence. Less than half of the participants were able to associate cats’ vocalizations with the correct context in which they were emitted; the best recognized meow was that emitted while waiting for food. Female participants and cat owners showed a higher ability to correctly classify the vocalizations emitted by cats during brushing and isolation. A high level of empathy toward cats was significantly associated with a better recognition of meows emitted during isolation. Regarding the emotional valence of meows, it emerged that cat vocalizations emitted during isolation are perceived by people as the most negative, whereas those emitted during brushing are perceived as most positive. Overall, it emerged that, although meowing is mainly a human-directed vocalization and in principle represents a useful tool for cats to communicate emotional states to their owners, humans are not particularly able to extract precise information from cats’ vocalizations and show a limited capacity of discrimination based mainly on their experience with cats and influenced by empathy toward them. Full article

The presented study examines the ability of 265 children aged 4–12 years to correctly assign contextual cues and inner state values to a set of audio and audio-visual recordings of dog vocalizations and behaviors in different situations. Participants were asked to mark which situation each recording captured, what inner state of the dog it showed, and what inner state a human would feel in the same situation. Recognition of the inner state of dogs was affected by the age of the child when evaluating the audio recordings (p < 0.001), and such a tendency was revealed in evaluating the audiovisual materials (p = 0.08). The inner state of dog evaluation was associated with both the situation assessment (p < 0.01) and human inner state (p < 0.001) in the case of audio recordings, but it was only correlated with situation assessment in audio-visual recordings (p < 0.01). The contextual situations were recognized by the participants only in the audio materials, with “stranger” being the best recognized situation, while “play” was the least recognized. Overall, children aged 4–5 years showed a limited ability to understand dog signals compared to children aged 6–12 years, who were successful in recognizing the dogs’ stimuli more than 80% of the time. Therefore, children younger than 6 years of age require increased supervision when interacting with dogs. Full article

Here we describe a pilot Dog-Assisted Activity program that was designed to improve wellbeing and social integration in a multi-cultural elementary classroom in which some episodes of bullying had been reported. We developed a 5-encounters protocol with the aim of introducing pet dogs into the class to stimulate understanding of different types of communication and behavior, ultimately facilitating positive relationships among peers. A preliminary evaluation was carried out in order to assess the effect of the program on teachers’ perception of children’s difficulties (e.g., peer relationship problems) and strengths (prosocial behaviors) by means of a brief behavioral screening tool, the Strengths and Difficulties Questionnaire (SDQ—Teacher version). Overall results indicate that, by means of the recognition of the dogs’ behavior and non-verbal communication, children were able to express their emotions and to show behaviors that had not been recognized by the teachers prior to the intervention. In particular, the SDQ Total Difficulties scores suggest that the teacher had increased awareness of the students’ difficulties as a result of the dog-assisted program. Overall, the presence of animals in the educational environment may provide enjoyment and hands-on educational experiences, enhanced psychological wellbeing, and increased empathy and socio-emotional development. Full article