AI-Powered Stroke Rehabilitation Technology Enhances Patient Safety

Researchers at Simon Fraser University are developing a large-scale project that integrates artificial intelligence (AI), machine learning, and wearable technology to support patient safety during recovery from stroke or spinal cord injury. The new technique is outlined in a recent study published in Clinical Rehabilitation, and it is already generating interest with its practical uses.

The new stroke rehabilitation technology presents a distinctive blend of advanced analysis and easy-to-use software, allowing patients to become stronger and not get hurt.

A Movement and Safety Focus

The project is led by Gustavo Balbinot. He is an assistant professor of neurorehabilitation and director of SFU's Movement Neurorehabilitation and Neurorepair Laboratory. His research team helps patients regain movement following severe brain injury. They also try to reduce the risk of falls that can be dangerous in the recovery process.

"Rehab is all about mobility," Balbinot says. "We want patients to move to regain what they've lost, but we want them to move safely. Our research teaches us how patients move and where the risks are."

Real-time Data from Wearable Devices

More than 50 stroke survivors took part in the research. They were equipped with small sensors on their bodies during the process of rehabilitation. These sensors tracked their activity patterns as they undertook everyday activities such as rising from a sitting position or walking around objects. The true innovation lies in software developed by the researchers—it employs machine learning to scan the vast amount of movement data and identify patterns that may indicate a greater risk of falls.

Personalized Warnings for Safer Travelling

If the system senses a movement that could be dangerous, it can alert the patient in a split second. For instance, it may tell the patient: "This is a very difficult movement. Move carefully and safely." Such a warning system would indeed prove beneficial for stroke patients, who will have mobility issues all their lives and are highly likely to get injured while undergoing rehabilitation.

This sensor can learn from the patient's movement patterns," says Balbinot. "It knows when someone is about to fall and can provide them with a timely warning. It's a smart, personalized support system.

This is a stroke rehabilitation technology that is customized to every patient's unique healing process, making this one of the most adaptable devices in brain rehabilitation today.

SFU Pioneering in AI Innovation

Simon Fraser University is the most prominent British Columbia university dedicated to AI research. It leverages over 100 specialists in eight faculties to create the most sophisticated technology. The rehab project is an example of how machine learning and AI can actually make a difference—not just in concepts but in practice.

The Future of Wearable Rehabilitation Technology

The wearable technology used is discreet and weightless. Balbinot hopes that one day, such sensors will be incorporated into normal clothing, providing patients with continuous monitoring and tailored assistance without invading their lifestyle. "Wearable devices matter," he emphasizes. "They bring the laboratory into daily life, keeping the patient safe while they carry out their day-to-day activities."

With such technologies, stroke rehabilitation technology is poised at the dawn of a new age—where intelligent devices and AI collaborate to enable patients with greater independence and confidence.