A Machine Learning Approach to Leverage Individual Keyboard and Mouse Interaction Behavior From Multiple Users in Real-World Learning Scenarios

Simple SummaryIt is widely recognized that the assessment of animal welfare should include measures of positive emotional (affective) state. Existing behavioral and physiological indicators of a positive affective state frequently lack sensitivity, objectivity or are unsuitable in a production environment. Therefore, there is a need to develop new approaches to accurately and objectively measure a positive emotional state in animals, including novel molecular markers such a miRNA. These biomarkers must be measurable in the peripheral circulation and provide an accurate account of the physiological and molecular activity in regions of the brain associated with emotional processing. Further, such markers require validation against established behavioral and physiological indices. Here we investigated the efficacy of circulating miRNA as biomarkers of emotional state in the pig.The ability to assess the welfare of animals is dependent on our ability to accurately determine their emotional (affective) state, with particular emphasis being placed on the identification of positive emotions. The challenge remains that current physiological and behavioral indices are either unable to distinguish between positive and negative emotional states, or they are simply not suitable for a production environment. Therefore, the development of novel measures of animal emotion is a necessity. Here we investigated the efficacy of microRNA (miRNA) in the brain and blood as biomarkers of emotional state in the pig. Female Large White × Landrace pigs (n = 24) were selected at weaning and trained to perform a judgment bias test (JBT), before being exposed for 5 weeks to either enriched (n = 12) or barren housing (n = 12) conditions. Pigs were tested on the JBT once prior to treatment, and immediately following treatment. MiRNA and neurotransmitters were analyzed in blood and brain tissue after euthanasia. Treatment had no effect on the outcomes of the JBT. There was also no effect of treatment on miRNA expression in blood or the brain (FDR p > 0.05). However, pigs exposed to enriched housing had elevated dopamine within the striatum compared to pigs in barren housing (p = 0.02). The results imply that either (a) miRNAs are not likely to be valid biomarkers of a positive affective state, at least under the type of conditions employed in this study, or (b) that the study design used to modify affective state was not able to create differential affective states, and therefore establish the validity of miRNA as biomarkers.

Simple SummaryEmotions or affective states recognition in farm animals is an underexplored research domain. Despite significant advances in animal welfare research, animal affective state computing through the development and application of devices and platforms that can not only recognize but interpret and process the emotions, are in a nascent stage. The analysis and measurement of unique behavioural, physical, and biological characteristics offered by biometric sensor technologies and the affiliated complex and large data sets, opens the pathway for novel and realistic identification of individual animals amongst a herd or a flock. By capitalizing on the immense potential of biometric sensors, artificial intelligence enabled big data methods offer substantial advancement of animal welfare standards and meet the urgent needs of caretakers to respond effectively to maintain the wellbeing of their animals.Farm animals, numbering over 70 billion worldwide, are increasingly managed in large-scale, intensive farms. With both public awareness and scientific evidence growing that farm animals experience suffering, as well as affective states such as fear, frustration and distress, there is an urgent need to develop efficient and accurate methods for monitoring their welfare. At present, there are not scientifically validated ‘benchmarks’ for quantifying transient emotional (affective) states in farm animals, and no established measures of good welfare, only indicators of poor welfare, such as injury, pain and fear. Conventional approaches to monitoring livestock welfare are time-consuming, interrupt farming processes and involve subjective judgments. Biometric sensor data enabled by artificial intelligence is an emerging smart solution to unobtrusively monitoring livestock, but its potential for quantifying affective states and ground-breaking solutions in their application are yet to be realized. This review provides innovative methods for collecting big data on farm animal emotions, which can be used to train artificial intelligence models to classify, quantify and predict affective states in individual pigs and cows. Extending this to the group level, social network analysis can be applied to model emotional dynamics and contagion among animals. Finally, ‘digital twins’ of animals capable of simulating and predicting their affective states and behaviour in real time are a near-term possibility.

Context-aware computing has become an essential component of modern wearable technology, enabling devices to sense, interpret, and respond to users’ environments and affective states. By integrating contextual intelligence, smart systems can adapt their own behavior to better align with user needs, enhancing efficiency, personalization, and user experience. A critical aspect of context awareness is the ability to recognize a user’s internal state, particularly emotions, which influence decision-making, communication, and overall well-being. While emotion recognition is traditionally approached through multimodal sensing—using cameras, microphones, or physiological sensors such as Electroencephalography (EEG), Electrocardiography (ECG), and Electro-dermal Activity (EDA)—these methods often require multiple sensors, leading to challenges related to power consumption, complexity, and privacy. The demand for a more efficient and unobtrusive approach to emotion and context recognition has driven research toward minimal-sensor solutions that can extract meaningful affective and contextual information with reduced hardware requirements. Restricting sensing sources of contextual information by using only limited number of sensors might come at the expense of constraining the ultimate goal of context-awareness. Such expense can be mitigated by “doing more with less” i.e. delving into the opportunities of using a single sensor for multiple purposes. What is desirable is a minimal-sensor solution capable of capturing both motion and physiological signals for both robust emotion recognition and additional context estimation. Among wearable sensors, chest-worn Inertial Measurement Units (IMUs) stand out as promising candidates for comprehensive context estimation, as they are well-positioned to capture motion patterns, and physiological vibrations, being the subtle cardio-respiratory signals. This raises the question of whether a single chest-worn IMU could serve as a minimal yet effective sensor for simultaneously estimating both emotional and contextual information, offering a practical alternative to traditional multi-sensor approaches. To address the challenge of using chest-worn IMU for multiple context sensing, I investigated the potential of using Seismocardiography (SCG) and Accelerometry-Derived Respiration (ADR) to estimate emotional states. First, I explored the existing capabilities of chest-worn IMUs in capturing cardio-respiratory vibrations and motion data, which I used as a knowledge base for embedding such IMUs in context sensing. Data plays a crucial role in this problem, therefore I developed a flexible software interface, ColEmo, to facilitate the scalable collection of emotion-related data and introduced a novel dataset, EmoWear, integrating physiological and motion data for emotion recognition and context awareness. Then, I examined the feasibility of using SCG for emotion recognition, comparing its performance with traditional physiological signals and evaluating its effectiveness as a single-sensor solution. This thesis presents a detailed analysis of the potential of chest-worn IMUs for emotion and context recognition. It reports on the design and validation of a SCG-based wearable system, the development and characteristics of the EmoWear dataset, and the implementation of the ColEmo platform for emotion data collection. The research also examines machine learning techniques applied to the SCG and ADR signals, assessing their effectiveness in estimating emotional states. My findings indicate that SCG-based emotion recognition achieves performance comparable to conventional physiological counterparts, demonstrating the feasibility of using a single IMU sensor for affective computing. The publicly-available EmoWear dataset provides a valuable resource for future research, highlighting the potential of combining motion and physiological data for enhanced context awareness. Additionally, the ColEmo software interface, which I published open-source, proves to be an effective tool for multi-modal emotion data collection, facilitating further advancements in wearable emotion recognition. The results pave the way for a minimal-sensor approach for context information estimation that can significantly reduce system complexity while maintaining high performance. These findings confirm that chest-worn IMUs present an opportunity to further improve emotion recognition in addition to the motion-based context information estimation. My research highlights the need for standardized methodologies in affective computing and demonstrates the viability of motion-based physiological sensing for emotion estimation. By using a single chest-worn IMU, this work presents an alternative to multi-sensor systems, reducing power consumption, and privacy concerns while maintaining robust performance. Future research should focus on refining IMU-based emotion recognition using deep learning techniques and larger datasets to enhance classification accuracy. Further validation in real-world settings is necessary to assess the practical applicability of the proposed system. The EmoWear dataset should be explored for additional context-awareness applications, such as activity recognition and stress monitoring, to maximize its utility. The ColEmo platform offers opportunities for expansion beyond emotion recognition, including cognitive load estimation, stress monitoring, and human-computer interaction. This research moves wearable technology forward by using fewer sensors, helping make affective computing more efficient, versatile, and respectful of privacy.

Monitoring Affective Trajectories during Complex Learning Sidney D’Mello (sdmello@memphis.edu) Department of Computer Science, University of Memphis Memphis, TN 38152 USA Roger S. Taylor (rstaylor@memphis.edu) Institute for Intelligent Systems, University of Memphis Memphis, TN 38152 USA Art Graesser (a-graesser@memphis.edu) Department of Psychology, University of Memphis Memphis, TN 38152 USA A series of studies have recently explored the affective states that occur during complex learning. Studies by Graesser and his colleagues have collected online measures of affect, such as observations by trained judges and emote- aloud protocols, as well as offline judgments of emotions by multiple judges (Craig, et al., 2004; D’Mello et al., 2006; Graesser et al., 2006). These studies have revealed that the basic emotions identified by Ekman and Friesen (1978), namely anger, fear, sadness, joy, disgust, and surprise, typically do not play a significant role in learning (see also Kort Reilly, & Picard, 2001). Instead they documented a set of affective states that typically do play a significant role in learning, at least in the case of college students learning about computer literacy with an intelligent tutoring system. These affective states were boredom, flow (engagement, Csikszentmihalyi, 1990), confusion, and frustration. They also monitored the affective states of delight and surprise, which occurred less frequently. While some of these affective states might be viewed as purely cognitive in nature, our position is that they should be classified as affective states (or emotions) because these states are accompanied by significant changes in physiological arousal compared with a “neutral” state of no apparent emotion or feeling (Barrett, 2006; Meyer & Turner, in press; Stein & Hernandez, in press). Furthermore, affective-cognitive composites are particularly relevant to higher-order learning. The aforementioned set of affective states can be situated within a broader perspective of emotion, in particular Russell’s (2003) Core Affect framework. This perspective holds that an affective state is composed of two integrated components: valence (pleasure to displeasure) and arousal (activation to deactivation). These components can be depicted graphically with valence represented on the X-axis and arousal on the Y-axis. Moving from left to right along the X-axis (valence) would correspond to increasing feelings of pleasure. Moving upward along the Y-axis (arousal) would correspond to increasing feelings of activation and energy (see Figure 1). The affective states of boredom, flow, confusion, and frustration will be the primary focus of this paper. These affective states have been previously correlated with learning Abstract This study investigated the transitions between affective states (i.e., boredom, flow, confusion, frustration, delight, and surprise) during learning while college students were tutored in computer literacy by AutoTutor, an automated tutoring system with natural language dialogue. Videos of participants’ faces and the interaction histories were recorded and then played back for the participants to judge their own affective states. We developed a metric to measure the relative likelihood of transitioning from an affective state at time t i to a subsequent affective state at time t i+1 . Several significant trajectories between affective states were identified. Instructional implications are discussed in the context of an expanded version of a cognitive disequilibrium model. Keywords: Affective states; emotions; affect trajectories, affect sequencing; emotion dynamics; AutoTutor; learning; instruction. Introduction There is ample empirical evidence in the psychological literature that emotions (or affective states) are systematically influenced by the knowledge and goals of the learner, and vice versa (Mandler, 1984; Ortony, Clore, & Collins, 1988; Russell, 2003; Stein & Levine, 1991). As individuals interact in the social and physical world, they attempt to assimilate new information with existing knowledge schemas. When new or discrepant information is detected, a mismatch between input and knowledge occurs. Attention shifts to discrepant information, the autonomic nervous system increases in arousal, and the new information may modify the individual’s goals and knowledge. The learner experiences a variety of possible emotions, depending on the context, the amount of change, and whether important goals are blocked. However, this type of affective arousal that accompanies learning is still not well understood. For example, researchers have yet to narrow down the emotions that accompany deep level learning of conceptual material. The consequential impact of the emotions on knowledge acquisition and transfer is still not well understood.

Relevance. Nowadays, technological systems, artificial intelligence, the general availability of the Internet and penetration into the systems of banks, institutions and social networks have become a studied science and are accessible to all groups and ages. One of the main tasks was to provide a system for protecting confidential information from hackers, as well as easy access to authentication and identification of users. Biometric systems came to the fore, including mouse movement dynamics and keystroke dynamics, which reveal the typing style and mouse movement of each person. Soft biometrics is an interesting and inexpensive biometric method that does not require additional equipment. The system identifies a person based on the input information they enter in a special column. Hand identification dynamics falls into the category of behavioral soft biometrics, that is, the user's patterns reflect the individual program of actions that he follows when using the site.The goal of this article the purpose of this work is to improve the security level by creating a function that will strengthen the authentication system and improve the iron gate Методы исследования. In carrying out the work, methods of analysis and synthesis, theories of algorithms, laws of kinematics, neural networks, keystroke dynamics and soft biometrics were used.Results. A method for extracting dynamic characteristics of keystrokes is described. A neural network is created and a threshold value is determined for identifying the type of typing hand.Scientific novelty. Unlike known authentication methods, the proposed method is used to determine the typing hand on the keyboard through a neural network using the laws of kinematics, soft biometrics and extracting the dynamics of keystrokes in order to determine the value and accuracy of determining the type of typing hand.Significance. The proposed solution allows to increase the security of user authentication, increase the speed of implementation and reduce the cost. The results obtained in the work are positive and can be used in the near future. In turn, soft biometric measurements depend on human behavioral patterns, which complicates user falsification. It is difficult to imitate typing behavior, since it is ballistic (semi-autonomous), which makes behavioral information valuable as a soft and sensitive biometric method.

A modern Intelligent Tutoring System (ITS) should be sentient of learner's cognitive and affective states, as learner's performance could be affected by motivational and emotional factors. It is important to design a low-cost and unobtrusive computational method for ITS to determine learner's states automatically. The automated learner's data sensing is useful in the development of personalized e-learning that can adapt learning content according to individual's emotion. Although many past findings relate stressor such as task demand to learner's emotion, however there is a lack of existing studies that examine the impact of stressor on learners' cognitive states and their mouse and keystroke dynamics. This exploratory research, conducted on 160 undergraduate students, found that the impacts of direct learning instruction and external stress stimuli, such as timer display, on learner's cognitive and affective states are significant. The correlations between direct instruction, external stimuli, learners' cognitive and affective states, as well as their mouse and keystroke behaviours are also significant.

Recent research on forward masking and backward masking has led us to the hypothesis that combined forward and backward masking may involve at least two different types of interaction patterns. The previously documented interaction results in masking are greater than predicted by the simple summation of masking effects obtained with each masker alone (Lynn & Small, 1977; Patterson, 1971; Wilson & Carhart, 1971). Another possible type of interaction is based upon the finding that backward masking, but not forward masking, can be reduced by cues providing timing information (Puleo & Pastore, 1980). A forward masker appears to involve minimum temporal uncertainty (Pastore & Freda, 1980) and, therefore, should be able to act as such a source of timing information to reduce the contribution of a later occurring backward masker. Both types of interaction patterns were found, with the specific forward and backward masker parameters determining which pattern is observed.

By lowering the risk of neurocognitive and chronic illnesses and facilitating informed decisions prior to traditional clinical diagnosis, this technology sets a new benchmark in preventive healthcare. One of the biggest obstacles to preventive healthcare is the early identification of pre-disease behaviours. Conventional diagnostic techniques frequently rely on overt symptoms, which manifest later in the illness. In this paper, a unique predictive AI framework is presented that analyses common interaction patterns in both digital and physical domains to detect modest pre-disease behavioural signals. Lifestyle/contextual data, typing and smartphone usage patterns, sleep metrics, physical activity, and voice prosody are just a few of the multi-modal data streams that the system integrates.By using attention-based deep neural networks with multi-scale feature fusion to minimize overfitting and dynamically prioritize informative signals, the model enables real-time monitoring of pre-disease states. Unlike previous disease-specific approaches, this framework identifies micro-deviations across multiple conditions, providing actionable insights for early intervention. Experimental validation on both synthetic and real-world datasets shows up to 99% predictive accuracy, outperforming single-modality models. An explainable AI layer enables clinicians to interpret feature contributions and personalize preventive measures.

Simple SummaryIn the past, there was a strong focus on avoiding or reducing negative animal welfare in animal experimentation. Recently, the importance of promoting positive animal welfare in laboratory animals has been highlighted. To ensure and promote positive animal welfare, reliable methods to evaluate the animal’s emotional state are important. Important achievements have been made to assess pain and other negative states in animals in the last decades, and only recently have positive emotions been gaining more interest. Therefore, more methods allowing the assessment of emotional states in animals have been introduced. In this overview article, we present common and emerging methods to assess emotions in laboratory rodents. We focus on the use of these methods in applied refinement research to identify achieved progress as well as the potential of these tools to improve animal welfare in animal-based research and animal experimentation.An animal’s capacity to suffer is a prerequisite for any animal welfare concern, and the minimization of suffering is a key aim of refinement research. In contrast to the traditional focus on avoiding or reducing negative welfare states, modern animal welfare concepts highlight the importance of promoting positive welfare states in laboratory animals. Reliable assessments of affective states, as well as the knowledge of how to elicit positive affective states, are central to this concept. Important achievements have been made to assess pain and other negative affective states in animals in the last decades, but it is only recently that the neurobiology of positive emotions in humans and animals has been gaining more interest. Thereby, the need for promotion of positive affective states for laboratory animals is gaining more acceptance, and methods allowing the assessment of affective states in animals have been increasingly introduced. In this overview article, we present common and emerging methods to assess affective states in laboratory rodents. We focus on the implementation of these methods into applied refinement research to identify achieved progress as well as the future potential of these tools to improve animal welfare in animal-based research.

Emotional computing is a field of human computer interaction where a system has the ability to recognize emotions and react accordingly. Recognizing Emotional states is becoming a major part of a user’s context for wearable computing applications. The system should be able to acquire the user’s emotional states by using physiological sensors. We want to develop a personal emotional states recognition system that is practical, reliable, and can be used for health-care related applications. We propose to use the eHealth platform which is a ready-made, light weight, small and easy to use device for recognizing few Emotional states like Sad, Dislike, Joy, Stress, Normal, NoIdea, Positive and Negative using decision tree classifier. In this paper, we present an approach to build a system that exhibits this property and provides evidence based on data for 8 different emotional states collected from 24 different subjects. Our results indicate that the system has an accuracy rate of approximately 91 %. In our work, we used three physiological sensors (i.e. BVP, GSR and EMG) in order recognize Emotional states (i.e. Stress, joy/Happy, sad, normal/Neutral, dislike and no idea).

The increasing sophistication of cyberattacks necessitates innovative approaches to cybersecurity. Behavioral biometrics, which leverage unique patterns in human behavior such as keystroke dynamics, mouse movements, and touchscreen interactions, have emerged as a promising solution for enhancing security frameworks. This study explores the efficacy of behavioral biometrics in detecting and mitigating cyber threats. By analyzing real-world applications and experimental data, the research highlights the strengths of behavioral biometrics in providing continuous authentication, reducing reliance on traditional static credentials, and improving threat detection accuracy. Challenges, including data privacy concerns, system integration, and adaptability to user behavior changes, are also discussed. The findings underscore the potential of behavioral biometrics as a vital component of next-generation cybersecurity systems, paving the way for more robust and user-friendly digital security solutions.

The increasing sophistication of cyberattacks necessitates innovative approaches to cybersecurity. Behavioral biometrics, which leverage unique patterns in human behavior such as keystroke dynamics, mouse movements, and touchscreen interactions, have emerged as a promising solution for enhancing security frameworks. This study explores the efficacy of behavioral biometrics in detecting and mitigating cyber threats. By analyzing real-world applications and experimental data, the research highlights the strengths of behavioral biometrics in providing continuous authentication, reducing reliance on traditional static credentials, and improving threat detection accuracy. Challenges, including data privacy concerns, system integration, and adaptability to user behavior changes, are also discussed. The findings underscore the potential of behavioral biometrics as a vital component of next-generation cybersecurity systems, paving the way for more robust and user-friendly digital security solutions.

Emotions influence the way drivers process and react to internal or environmental factors, but relatively little research has focused on drivers’ emotions. Of many emotional states, anger is considered the most serious threat on the road. Therefore, having an affective intelligent system in the car that can estimate drivers’ anger and respond to it appropriately can help drivers adapt to moment-to-moment changes in driving situations. To this end, we integrated behavioral, physiological, and subjective data to monitor drivers’ affective states in various driving contexts to address the question: “can self-selected music mitigate the effects of anger on driving performance?” In our experiment, three groups of participants (in total 52) drove using a driving simulator: anger without music, anger with music, and neutral without music. Results showed that angry drivers who did not listen to music had riskier driving behavior than emotion-neutral drivers. Results from heart rate, oxygenation level in prefrontal cortex, and self-report questionnaires showed that music could help angry drivers react at the similar level to emotion-neutral drivers. Regarding personality characteristics, drivers who had anger-expression out style had riskier driving behavior. Divers’ workload data showed lower performance and higher effort for angry drivers without music. In conclusion, this study shows that multimodal sensing can be effectively used to holistically assess drivers’ emotional states and that music can be used as a possible multimodal strategy to mitigate the anger effects on driving performance as well as drivers’ subjective experiences.

Emotional, affective and biometrical states analytics of a built environment

It has been found that older adults who are confined to nursing homes have permanent stay sedentary, for that reason has greater stiffness in their joints which implies deterioration in physical ability and emotional state. PURPOSE: To determine the effect of physical activation program on functional ability, fall risk and affective state among older adults asylees. METHODS: Fifty institutionalized persons were randomly divided into two groups, experimental group (EG) and control group (CG), 15 women and 10 men in each group were selected. All were over 60 years old, did not have a terminal illness and did not use a wheelchair. The intervention consisted of implementing an exercise program with emphasis on joint mobility, consisting of 60 sessions conducted over 12 weeks. The sessions had a 10 minutes warmup, 20 for the central part and 10 minutes to cool down. The control group continued with the activities that the institution provides them. Affective state, with the geriatric scale of Yesavage, functional capacity with the Katz index and the risk of falling with the Tinetti scale were evaluated. Descriptive statistics and t-test was performed to determine differences between the initial and final evaluation. RESULTS: The retention was 100 per cent; the average age of participants was 74 with a range of 61-82 years for the EG, while the CG was 73 with a range of 62-81 years. In EG increased in both gait and balance, and functional ability between the pretest and postest (p=0.001) was observed, while depression state a decreased (p=0.001) was observed. Among CG a decrease was observed in both gait and balance, as in the functional capacity between pretest and postest (p=0.001), while depression state an increase (p=0.01) was observed. CONCLUSIONS: The implementation of an exercise program with emphasis on joint mobility improves gait and balance in elderly asylees, representing a decreased risk of falling. It also increases the functional capacity representing greater independence in performing basic activities of daily living. It also lowers the level of depression improving affective state. These shows that design and implement exercise plans for the elderly, providing autonomy, improve their physical ability and directly and positively impact their emotional state and their relationships manner.