Yixuan Zhang

Video-based remote physiological measurement, enabling non-contact monitoring of physiological signals through facial videos, demonstrates promising prospects in remote medicine and health monitoring. To take advantage of long-term dependency information from cardiac activity, previous methods commonly rely on long video sequences for measurement. These approaches are highly susceptible to various internal and external factors and struggle to capture fluctuations in physiological signals over extended measurement periods reliably. Unlike previous long-form video-based methods, we propose a novel adaptive knowledge distillation framework that utilizes short-form videos. This approach aims to mitigate the impact of internal and external factors while accurately capturing physiological signal fluctuations by reducing the required video length for precise measurement. Specifically, we first propose a long-short-term distillation framework that uses a teacher model to capture long-term dependencies from long-form videos and leverages this capability to guide the training of the student model on short-form videos. Subsequently, we propose a frame-level dynamic prior mechanism (FDPM) that replaces student features similar to teacher features with the corresponding teacher features and dynamically adjusts their proportions. Furthermore, we design an instance adaptive distillation mechanism (IADM) module that dynamically regulates the distillation temperature and the loss weight to develop an optimal distillation strategy for each sample. Finally, we conduct systematic experiments across three benchmark datasets and various video lengths. Experimental results demonstrate the effectiveness of our method, especially in short-form video scenarios.

Lung diseases are serious threats to human health and life, therefore, an accurate diagnosis of lung diseases is significant. The use of artificial intelligence to analyze lung sounds can aid in diagnosing lung diseases. Most of the existing lung sound recognition methods ignore the correlation between the time-domain and frequency-domain information of the lung sounds. Additionally, the spectrograms used in these models do not adequately capture the detailed features of the lung sounds. This paper proposes a model based on wavelet feature enhancement and time-frequency synchronous modeling, comprising a dual wavelet analysis module (DWAM), a cubic network, and an attention module. DWAM in the model performed a dual wavelet transformation on the spectrograms to extract the detailed features of the lung sounds. The cubic network comprised multiple cubic gated recursive units to capture the correlation of the time-frequency of the lung sounds using the time-frequency synchronous modeling. The attention module, which includes temporal and channel attention, was used to enhance the time-domain and channel dimension features. In the combined dataset and the International Conference on Biomedical and Health Informatics 2017 dataset, the suggested framework outperforms existing models by more than 1.36% and 4.28%, respectively.