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.

Friday, June 2, 2023

Development of a Gait Feature–Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor

I've had a gait disorder for 17 years because my doctors and therapists completely failed at getting me recovered. I have no cognitive disorder and won't have one for as long as I live. No thanks to any of my medical specialists. 

Development of a Gait Feature–Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor

Jeongbin Park, Hyang Jun Lee, Ji Sun Park, Chae Hyun Kim, Woo Jin Jung, Seunghyun Won, Jong Bin Bae, Ji Won Han, Ki Woong Kim

Abstract

Background and Objectives: Gait changes are potential markers of cognitive disorders (CD). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertia sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE).

Methods: We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertia sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristics analysis.

Results: In total, 595 participants were enrolled, 101 of them had CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% confidence interval [CI] = 0.748–0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI = 0.729–0.877, p < 0.001). Our model showed comparable diagnostic performance for CD to that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was > -1.56.

Discussion: Our gait-based model using a wearable inertia sensor may be a promising diagnostic marker of CD in older adults.

Classification of Evidence: This study provides Class III evidence that gait analysis can accurately distinguish cognitive disorders from healthy controls in older adults.

  • Received August 25, 2022.
  • Accepted in final form March 17, 2023.

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