That’s a very interesting question! In fact, we can say that the era of “machine learning automatically assessing a patient’s recovery stage” is already slowly dawning.
Of course, it’s not yet fully automated, but many efforts are already being made in this direction in research labs and clinical settings.

[Neurorehabilitation: Understanding Recovery with Data]

The recovery process of a stroke patient is too complex to simply judge by “improvement/deterioration of movement.”

Each person has different areas of the brain damaged, different rates of nerve reconnection, and different exercise styles.

Therefore, machine learning models are currently developing to analyze a patient’s recovery stage based on the diverse data collected by robotic rehabilitation equipment.

For example, the robot measures the patient’s arm or leg movements,

joint angle changes, muscle electrical signals (EMG), velocity, force, and acceleration patterns, and visual feedback responses.

By integrating all this information, the model can calculate “what stage of recovery the patient is currently in.”

[Practical Applications]

Rehabilitation robots like the InMotion ARM analyze a patient’s arm movement data, calculate how the motors should move and how much force to apply, and automatically visualize the recovery rate.

In EMG-based exoskeleton research, deep learning analyzes muscle signal patterns to automatically classify the progress of neuromuscular rehabilitation.

Some hospitals even use AI-based systems to instantly score a patient’s motor performance and display it. This immediate feedback greatly contributes to patient motivation and consistent training.

Simply put, the robot and AI continuously monitor the patient’s movement data,
and determine, “This person is at this level, so let’s provide some additional assistance or adjustments.”

While this doesn’t completely eliminate the need for human intervention, it’s increasingly becoming a powerful tool that supports both therapists and patients.

David Mun, your post is really fascinating! It’s about machine learning automatically assessing a patient’s recovery stage. Honestly, I’m quite surprised and intrigued. Will this really advance to the point where we can accurately assess a patient’s condition without a doctor or nurse? Especially since “recovery stage” is so subjective and complex.

Oh, Steve Ryu! That’s a good question. Indeed, “recovery” is a difficult concept to define with just a few numbers. However, recent research is actively attempting to objectively assess pain levels and the extent of rehabilitation by analyzing biometric data like heart rate and activity levels obtained from wearable sensors, or even facial expressions and movements using computer vision technology. While this isn’t a complete replacement, it will complement the subjective judgment of medical professionals and be incredibly useful in quickly identifying early warning signs, especially in situations where 24-hour monitoring is difficult.

Wow, utilizing wearable data and facial expression analysis really sounds like future technology. So, if this is going to be implemented in the medical field, issues like data reliability and privacy will be extremely important. What discussions are currently underway on this? I’m concerned, as it’s sensitive information.

Of course it’s important! Medical data is particularly sensitive, so we’re putting a lot of effort into data anonymization and establishing security protocols from the research stage onward. Technically, we’re also discussing methods like encryption and federated learning to keep the data inside the hospital while only training the model. Ultimately, I think establishing legal and ethical guidelines that match the pace of technological advancement is crucial. Only when these issues are addressed can it truly be implemented in practice!