Citation :

Thiyagarajan Arun Chettier, 2025. "Emotion-Aware Computing Using Multimodal Sensor Fusion" International Journal of Computer Science & Information Technology  Volume 1, Issue 3: 21-34.

Abstract :

As technology weaves its way even more into our daily lives, in the digital age where tech is no longer a separate part of society but deeply interwoven itself within society — the need for systems to not only understand what we say but how we feel has been exponentially increasing. No longer are our interactions with devices limited to utility — virtual assistants and customer service bots, intelligent healthcare monitors and tools for an interactive smart classroom all need to be capable of reacting functionally or even emotionally competent. Even though they might be great at calculating, machines are mostly just calculators — and we will not really be ready for an operate side-by-side- with-humans-assembly line enabled by artificial intelligence until they gain the ability to recognize and interpret human emotions in a way that feels organic. Therein lies the magic of emotion-aware computing, a blossoming discipline which seeks to connect man and machine through our shared emotional understanding. Fundamentally emotion-aware computing is about building systems that are able to perceive, analyse and respond / adapt to user's emotions with the aim of making interactions more human-like. At the center of this technological leap lies multimodal sensor fusion.Human emotions are extensive, complex, multidimensional, and often subtle; We communicate emotions not just with words or facial expressions, but also through tone of voice, body posture, eye movements, and can even unconsciously betray our emotional state through physiological variables such as heart rate or skin conductance. When it comes to identifying the data that meaningfully signals about emotions which requires more than one source of information and again a strong model. The first emotion-recognition systems used unimodal (a single modality, for example: just images of faces or the tone of voice) information since they could not solve with a high reliable the complexity behind solving when spontaneous emotions appear in real life conditions. To solve this problem, multimodal sensor fusion is to combine input from diverse types of sensors such as cameras, microphones, wearable biosensors and motion detectors in order to infer a richer profile of emotion. Machines can go beyond just “hearing” our voices or seeing our faces and start to listen at a different level… like, on the inside of your body.In this paper, we aim to investigate emotion-aware computing better, however, no less importantly automatically by leveraging multimodal sensor inputModerating features in the emotion and sensory data that describe all considering views forming an extended feature space using soft height weighted fusion. It focuses on the potential of combining audio-visual data (speech, facial expressions) with physiological signals (e.g. EEG, heart rate, skin response), and behavioral cues such as body movement or eye-tracking to enhance the ability of machines to read emotional states from users. This requires powerful machine learning and data fusion algorithms, which these systems are trained with: — to explore cross-modal correlations and capture rich patterns in multimodal data for recognizing users' emotions. For instance, a quiver in the voice box along with a brow to furrow and a heart rate all amok — could be ascribed to anxiety, despite whatever calm demeanor this person is trying to project. This level of emotional penetration is often invaluable in any number of everyday related situations.This is why multimodal emotion recognition is strong, not just in its accuracy but also in the resilience. In real world it may be that a single sensor will fail, get blocked or provide wrong data. On the other hand, multimodal systems are designed to handle missing and poor data very elegantly by using complementary streams. This replication makes them significantly more resilient in this type of dynamic environment. In addition to providing a more human-like approach, allowing people use all of their senses and contextual cues, these systems are now able to replicate the way humans interpret emotions which provide more intuitive interactions. Crucially, it is not only about adding data sources but rather about translating different aspects of feature into their correct weights and relevant in the context. This included a variety of sophisticated modeling techniques — such as attention-based neural networks, cross-modal transformers, and hierarchical fusion architectures — which are meant to capture the temporal dynamics of emotion.Applications of multimodal sensor fusion and emotion-aware computing are wide-ranging, as well as far-reaching in their positive effects. In education, these systems can detect when students are confused, bored, or frustrated in order to allow teaching methods to be adjusted. For example, they can offer preliminary signals for mental health or recognize stress in patients with the inability to speak. Emotion-aware virtual agents Responses are more empathetic in nature, which means improved customer satisfaction and increased trust. This type of system could also prove beneficial in regular scenarios, like when you are driving, gaming or operating household appliances; an emotion-aware system will alter its action based on the emotional state of the user, essentially turning technology into a supportive entity instead of just a blunt object.But not is all rainbows and unicorns when they start developing these… All of this is to say that human emotions are so idiosyncratic and culturally contingent, and it is enough reason for us to be a little wary about how we can generalize or what kind of fairness or bias we may find in emotion-recognition models. Privacy and consent are similar pressing ethical considerations — emotional data carries a lot of genuinely personal information with it. All too often the foundation is to make systems that are not intrusive, or manipulative and rather support, empathize and respect human dignity. This kind of system really demands a lot of thoughtful, interdisciplinary work across AI/ML (algorithms), human-centered design (psychology) and ethics.We introduce a multimodal sensor fusion based framework that focuses on accuracy, adaptability and ethical responsibility in a research direction towards emotion-aware computing. This article surveys the state of modalities and fusion techniques to date, suggests integrated real-time emotion recognition architecture, and provides synthetic experimental results using multiple datasets to prove its feasibility. The work is part of a larger effort to build technology with emotional intelligence that can do more than calculate data; it can perceive humans. We imagine a future where machines are not only intelligent but emotionally sensitive, and human-technology interaction feels less like an act and more like it understands what being human means.

References :

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Keywords :

Recognition Of Emotions, Multimodal Fusion, Affective Computing, Biosensors, Speech Analysis, Facial Expression, Deep Learning, Sensors Integration. Human-Computer Interaction.