Projects - Smarthealthcenter

Accelerating Patient Rehabilitation via Wearable Filament Sensor Networks

Principal Investigators & Key Members:

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

Principal Investigators & Key Members:

Nguyen Vu Linh | Tran Trung Dung | Ho Ngoc Minh | Yih-Kuen Jan | Manuel Enrique Hernandez

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

Principal Investigators & Key Members:

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.

Wastewater Epidemiological Surveillance in Vietnam

Principal Investigators & Key Members:

While the pandemic is global, transmission is local. Surveillance should be designed based on local context. Besides COVID-19, antimicrobial resistance (AMR) is a silent pandemic, and it only gets worse, as predicted by WHO. The identification of the sentinel sites and the frequency of sampling depends on the surveillance goals. Actionable public health goals in a crisis situation requires a different approach from those for endemic conditions.

Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System

Principal Investigators & Key Members:

Kok-Seng Wong | Bo Li | Khoa Doan

The project focuses on designing a trustworthy federated learning (FL) framework for the healthcare system with theoretical guarantees for its privacy, robustness, and agent-level data valuation and explainability, aiming to make the healthcare systems more efficient and trustworthy.

Developing a unified, low-cost, self-care mobile health application for common disease screening and early detection in low-and middle-income countries

Principal Investigators & Key Members:

This work aims to develop a low-cost, unified machine learning-based screening tool using multimodal signals collected from smartphones and wearable devices to evaluate the risk of presenting with common, high-demanding NCDs (stroke, chronic respiratory diseases, and neurodegenerative diseases) in low-and middle-income countries. This tool can be used at home or the point of care and opens up the opportunity to bring digital healthcare solutions to millions of people across countries.