Deploying AI-based emotion recognition at the edge enables real-world applications but is constrained by data scarcity, heavy models, hardware limits, and privacy issues. To overcome these, we propose CHEER (Cloud-HEavy to Edge-Ready), a self-supervised, transfer-efficient framework where the cloud pre-trains lightweight graph-based encoders using unlabeled data, stores them as frozen models, and deploys only the needed encoder. New users are locally matched via centroids of clusters, and a small on-device classifier is trained with minimal labeled data, reducing computation, memory, and energy use while preserving privacy. Experimental results in the WEMAC and WESAD datasets show an accuracy of 78.19% and 80.08% at the edge on a NVIDIA Jetson Orin Nano. Moreover, CHEER achieves more than 60% reduction in model size, and lowers both peak RAM usage and energy consumption by more than 50% compared to the state-of-the-art.

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such applications at the edge, where limited computing resources constrain the design space. This paper introduces a novel approach to AI-driven personalized solutions in biosensing applications by combining deep learning with clustering-based separation techniques. The proposed Clustering and Learning for Emotion Adaptive Recognition (CLEAR) methodology strikes a balance between population-wide models and fully personalized systems by leveraging data-driven clustering. CLEAR demonstrates its effectiveness in emotion recognition tasks, and its integration with fine-tuning enables efficient deployment on edge devices, ensuring data privacy and real-time detection when new users are introduced to the system. We conducted experiments for model personalization on two edge computing platforms: the Coral Edge TPU Dev Board and the Raspberry Pi with an Intel Movidius Neural Compute Stick 2. The results show that initial cluster assignment for new users can be achieved without labeled data, directly addressing the cold-start problem. Compared to baseline validation without clustering, this proposal improves accuracy metric from 75% to 81.9%. Furthermore, fine-tuning with minimal labeled data significantly improves accuracy, achieving up to 86.34% for the fear detection task in the WEMAC dataset while remaining suitable for deployment on resource-constrained edge devices.

Negative emotions can lead to a variety of physiological and psychological problems. Identifying and interpreting negative emotions can help people and specialists to deal with their effects on the human body. This paper introduces a deep learning-based method for recognizing negative emotions utilizing physiological signals. It is based on extracting a set of 123 features from raw signals and organizing them into 2D feature maps. A hybrid CNN-LSTM model is then employed to classify these maps, simultaneously learning and integrating integral and sequential features to output emotional feedback. Feature fusion is incorporated during training to improve the method’s accuracy and reduce the data complexity. The approach has been validated using WEMAC and WESAD datasets, achieving F1-scores of 85.15% and 91.44% and accuracies of 85.03% and 90.98%, respectively. Furthermore, the feasibility of this method for real-world applications is demonstrated through deployment on the Coral Edge TPU, an embedded device, indicating its potential for run-time decision-making on the computing edge.

Applying affective computing techniques to recognize fear and combining them with portable signal monitors makes it possible to create real-time detection systems that could act as bodyguards when users are in danger. With this aim, this paper presents a fear recognition method based on physiological signals obtained from wearable devices. The procedure involves creating two-dimensional feature maps from the raw signals, using data augmentation and feature selection algorithms, followed by deep learning-based classification models, taking inspiration from those used in image processing. This proposal has been validated with two different datasets, achieving, in WEMAC, WESAD 3-classes, and WESAD 2-classes, F1-score results of 78.13%, 88.07%, and 99.60%, respectively, and 79.90%, 89.12%, and 99.60% in accuracy. Furthermore, the paper demonstrates the feasibility of implementing the proposed method on the Coral Edge TPU device, prepared to make inferences on the edge.