Healthcare AI

Self-supervised representations combine CCTV, depth, audio, and biomedical sensors to detect rare events while preserving privacy.

Designing interpretable CNN-Transformer pipelines that estimate sleep stages from cross-modal biosignals collected in clinical studies and remote assignments.

Detecting irregular heartbeats through optimized deep neural architectures for wearable and clinical ECG.

Applying CNNs and RNNs to ECG, PPG, and multi-sensor streams for real-time healthcare monitoring.