[4K2-IS-2e] Machine learning

[4K2-IS-2e-05] Enhancing Sound-Based Sleep Quality Assessment by Multimodal Knowledge Distillation

〇Haoyu Lu¹, Takafumi Kato², Ken-ichi Fukui^3,4 (1. Graduate School of Information Science and Technology, Osaka University, 2. Graduate School of Dentistry, Osaka University, 3. Faculty of Business Data Science, Kansai University, 4. SANKEN, Osaka University)

[[Online]]

Keywords:Sleep Quality Evaluation, Knowledge Distillation, Deep Learning

Sleep is vital for physical recovery, brain function, and emotional health. Polysomnography (PSG) is the gold standard for assessing sleep quality, but it is intrusive and impractical for widespread application. Sound data is a non-intrusive alternative, though its complexity makes extracting meaningful information difficult. This study enhances sound-based sleep quality assessment using a Knowledge Distillation (KD) framework. The teacher model integrates PSG features, physical factors, sleep stage data, sound data, and questionnaire factors, using a Gated Variable Selection Neural Network (GVSN) to identify key information from multimodal inputs. The student model uses physical factors and sound features extracted from one night’s sleep events and learns from the teacher via a SoftMax-based KD process. Results show the student model's accuracy improves significantly, demonstrating the potential of KD to improve sound-based sleep quality assessment.