Deans' stroke musings: A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients

Changing stroke rehab and research worldwide now.Time is Brain! trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 523 posts on hyperacute therapy, enough for researchers to spend decades proving them out. These are my personal ideas and blog on stroke rehabilitation and stroke research. Do not attempt any of these without checking with your medical provider. Unless you join me in agitating, when you need these therapies they won't be there.

What this blog is for:

My blog is not to help survivors recover, it is to have the 10 million yearly stroke survivors light fires underneath their doctors, stroke hospitals and stroke researchers to get stroke solved. 100% recovery. The stroke medical world is completely failing at that goal, they don't even have it as a goal. Shortly after getting out of the hospital and getting NO information on the process or protocols of stroke rehabilitation and recovery I started searching on the internet and found that no other survivor received useful information. This is an attempt to cover all stroke rehabilitation information that should be readily available to survivors so they can talk with informed knowledge to their medical staff. It lays out what needs to be done to get stroke survivors closer to 100% recovery. It's quite disgusting that this information is not available from every stroke association and doctors group.

Wednesday, August 30, 2023

A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients

Useless since there are NO protocols provided that will recover the gait disabilities. Don't you understand there are two parts to stroke rehab? Objective damage diagnosis leading to EXACT rehab protocols. You barely accomplished the first part.

A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients

Nicoletta Balletti1,2 a, Gennaro Laudato1 b and Rocco Oliveto1,3 c
1STAKE Lab, University of Molise, Pesche (IS), Italy
2Defense Veterans Center, Ministry of Defense, Rome, Italy
3Datasound srl, Pesche (IS), Italy
Keywords: Gait Analysis, Motion Tracking, Post Stroke Rehabilitation, Machine Learning.

Abstract:

Stroke is a serious medical condition that can result in permanent brain damage and other pathological issues. Conditions suffered by survivors ranged in severity from full recovery to significant movement disability. Even though some may recover quickly, many stroke survivors require long-term support to help them achieve as much independence as they can. Thanks to a proper rehabilitation, patients who have experienced a stroke can work to regain skills that are suddenly lost when a section of their brain is injured. Due to the breakdown of neuronal networks in the motor cortex, abnormal gait patterns are a typical disability after a stroke. Therefore, gait analysis can be a powerful tool to support stroke patients during rehabilitation. In this work we propose GIULYO, a Machine Learning based tool that offers support in the assessment of video gait trials in stroke patients by providing an automatic analysis on the muscle activity of the assisted subject. GIULYO is a device-agnostic tool because it accepts motion tracking data in terms of 3d trajectories regardless of the type of instrumentation. GIULYO has been validated on the ARRA Stroke dataset and the results showed an overall accuracy of 0.74 while on a subset a patients—with common clinical assessment of mobility impairments—the accuracy increased to 0.92, therefore demonstrating the feasibility of involving a ML-based approach for the rehabilitation support of post stroke patients.