AFairDNet: Actively Empowering Fair Multisensor Emotion Recognition with Chain-of-Thought on Diffused Biosignals

The scarcity of reliable, extensive datasets hampers the training of effective models for wearable healthcare technology. This data gap frequently introduces biases into training sets, which then carry over into the models themselves. Such inherent biases pose substantial fairness challenges, particularly in sensitive healthcare scenarios. To this end, we propose AFairDNet, an effective active learning framework that utilizes a small collection of annotated data to create an initial classifier, and then continually refines it by incorporating synthesized 'hard' signals, representing areas where the model's training is currently insufficient. To ensure both creativity and ethical responsibility in these generated signals, we enhance the signal generation process using Chain of Thought (CoT) reasoning. The model employs real-time iterative CoT refinement of the model's text prompts to condition the multisensor signal diffuser, ensuring that the synthesized multisensor biosignals are not only of high quality but also semantically faithful. Extensive evaluations using two large publicly available multisensor emotion recognition datasets demonstrate that by leveraging a small yet comprehensive collection of synthesized samples (i.e., around 1.4% of the total training set), AFairDNet may boost a baseline classifier's performance, outperforming the state-of-the-art methods. More precisely, in addition to achieving 1.5-3% higher accuracy than current supervised and self-supervised baselines, AFairDNet also boasts an impressive Total Fairness Score, signaling its potential for more responsible and transparent AI-driven synthesized signal generation.