Projects - Smarthealthcenter

Enhancing Precision Digital Pathology with an AI-powered Platform Accelerated by Supercomputers

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Autonomous and Knowledge-guided Health Misinformation Defender Platform

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Ultrasensitive Lateral Flow Immunochromatography for Low-Cost Cancer Screening

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Accelerating Patient Rehabilitation via Wearable Filament Sensor Networks

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This project introduces a low-cost hydraulic soft filament sensor (SFS) integrated into a flexible brace, aimed at monitoring joint angles and guiding users through rehabilitation via a video interface. It incorporates an intelligent calibration technique leveraging a neural network (NN) model, wireless technology, onboard signal processing, a smartphone app, and assessment algorithms using machine-learning models tailored for knee and elbow injuries. This advancement improves the interaction between humans and machines during rehabilitation. Human-subject experiments at Vinmec Times City International Hospital will validate the system’s effectiveness, marking a pioneering effort in physical medicine and rehabilitation at Vinmec and Vinuniversity.

The Future of Rehabilitation Robotics: A Framework for Human-Robot Interaction for Complete Training and Assessment

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This research project will explore cutting-edge technologies in robotics, physical human-robot interaction, artificial intelligence (AI), and rehabilitation engineering to develop a novel exoskeleton robotic system to achieve complete training. This research will clarify the up-to-date design philosophy, redefine the definition of robots and intelligence means for multistage rehabilitation, and assess physical human-robot interaction to offer safe, robust, and reliable training with shared control (user preference and clinician prescription). If successful, the invented technologies will benefit the healthcare and robotics industries, transforming multistage rehabilitation from therapist-guided to robot-guided treatment.

From Causal Understanding of Healthy Longevity to Smart Health

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The project has several intertwined methodological research thrusts, including (1) Causal structure discovery to disentangle how various factors interact with one another in the complex system of human health evolution over time, (2) Counterfactual inference to estimate the treatment effect at the individual patient level, thereby allowing personalized decision (treatment), risk assessment, and prevention, (3) Causal evaluation framework to determine how to evaluate the performance or generalization of a causal model, and (4) Causal intervention optimization where adjustable properties of interventions, such as thresholds for action, can be optimized based on their causal impacts. These methodological thrusts will be brought together for cross-cutting healthy longevity applications. Bringing thrusts and cross-cuts together will provide significant insight and design principles into the causal structure of healthy longevity by advancing causality methodology and applying it to large-scale longitudinal health data.