Exploring Wearable Emotion Recognition with Transformer-Based Continual Learning

The rapid advancement of wearable technology has enabled continuous, real-time health monitoring through devices such as smartwatches and fitness trackers. These devices generate vast amounts of biometric data, including heart rate, galvanic skin response (GSR), and activity levels, which can be used for personalized healthcare applications such as emotion recognition and stress monitoring. However, the use of medical data introduces privacy challenges due to regulations like HIPAA and GDPR, necessitating innovative learning techniques that do not rely on large, centralized datasets. Continual learning (CL) offers a solution by enabling models to incrementally acquire knowledge over time, adapting to new data without forgetting previously learned information. This paper evaluates the effectiveness of CL techniques in the context of emotion recognition using GSR data from the DEAP dataset. Each subject is treated as a separate task, and a custom transformer-based PatchTST model is trained sequentially on each patient’s data. Results show that the CL approach achieves performance levels comparable to traditional methods that train on all patients’ data simultaneously. This demonstrates the potential of CL to maintain high accuracy while preserving patient data privacy, thereby supporting the development of adaptive, real-time personalized healthcare solutions.