Medical Imaging, IoT Healthcare & Affective Computing

An IoT and cloud-based telerehabilitation framework using RGB-Depth sensors for remote patient monitoring and rehabilitation assessment. The system captures 3D skeletal data and RGB video to analyze exercise quality, range of motion, and treatment adherence, providing real-time feedback to patients and progress reports to therapists via secure cloud infrastructure for at-home rehabilitation programs.

A multimodal IoT-enabled framework for human action recognition in UAV/drone-based crowd and multi-person scenarios. The system integrates aerial RGB video with IoT sensor data through deep learning pipelines to recognize human actions from elevated viewpoints, addressing challenges of scale variation, occlusion, and dynamic camera motion in real-time surveillance and search-and-rescue operations.

An RGB-D robotic perception pipeline for hand-object interaction analysis and scene understanding in human-centric environments for natural human-robot collaboration. The system fuses color and depth data to track hand poses, recognize grasped objects, and understand manipulation intents, enabling robots to assist humans in shared tasks with contextual awareness and safe physical interaction.

An attention-driven multimodal framework for sleep staging and obstructive apnea screening using EEG and physiological signals for accurate sleep assessment. The system employs self-attention mechanisms to identify key temporal patterns across EEG channels, airflow, and oxygen saturation, enabling automated classification of sleep stages and detection of apneic events for clinical sleep disorder diagnosis.

A multi-modal feature fusion pipeline for myocardial infarction detection from echocardiogram and MRI sequences using deep learning. The system extracts complementary features from both imaging modalities, fuses them through attention-guided mechanisms, and classifies cardiac abnormalities with high sensitivity and specificity, enabling early detection of heart attacks and improved patient outcomes in clinical cardiology.

An intelligent multi-region MRI analytics framework for neurological disorder diagnosis using anatomical biomarkers and quantum-optimized feature fusion. The system analyzes multiple brain regions simultaneously, extracts volumetric and textural biomarkers, and employs quantum-inspired optimization for feature selection, enabling accurate classification of neurological conditions including Alzheimer disease, brain tumors, and multiple sclerosis from structural MRI scans.

A multimodal EEG-ECG pipeline for emotion recognition, integrating brain and cardiac signal analysis for affective computing and mental health monitoring. The system synchronizes electroencephalogram and electrocardiogram signals through temporal alignment, extracts spectral and HRV features, and employs deep fusion networks to classify emotional states such as happiness, sadness, stress, and relaxation for human-computer interaction and mental wellness applications.

An integrated AI framework for anatomically guided lung cancer segmentation and subtype classification using thoracic CT with multi-view deep learning. The system performs precise lung nodule segmentation guided by anatomical priors, extracts radiomic features from multiple viewing planes, and classifies cancer subtypes including adenocarcinoma, squamous cell carcinoma, and small cell carcinoma for computer-aided diagnosis in pulmonary oncology.