Deans' stroke musings

Changing stroke rehab and research worldwide now.Time is Brain!Just think of all the trillions and trillions of neurons that DIE each day because there are NO effective hyperacute therapies besides tPA(only 12% effective). I have 493 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:

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's quite disgusting that this information is not available from every stroke association and doctors group.
My back ground story is here:

Saturday, January 28, 2017

Predicting muscle forces measurements from kinematics data using kinect in stroke rehabilitation

Your therapist should be able to use this to objectively measure progress in your rehab. And only 37 references your therapists already read. With this objective measurement you could match the protocols used to recovery.  And with that we could finally get scientifically reproducible results.
  • Mohamad Hoda
  • Yehya Hoda
  • Basim Hafidh
  • Abdulmotaleb El Saddik
  1. 1.Multimedia Communication Research LaboratoryUniversity of OttawaOttawaCanada
  2. 2.Perpuim Care PolyclinicDohaQatar
DOI: 10.1007/s11042-016-4274-5
Cite this article as:
Hoda, M., Hoda, Y., Hafidh, B. et al. Multimed Tools Appl (2017). doi:10.1007/s11042-016-4274-5


Muscle strength is mostly measured by wearable devices. However, wearing such devices is a tedious, unpleasant, and sometimes impossible task for stroke patients. In this paper, a mathematical model is proposed to estimate the strength of the upper limb muscles of a stroke patient by using Microsoft Kinect sensor. A prototype exergame is designed and developed to mimic real post-stroke rehabilitation exercises. Least-square regression matrix is used to find the relation between the kinematics of the upper limb and the strength of the corresponding muscles. Kinect sensor is used along with a force sensing resistors (FSR) glove and two straps to collect both, real-time upper limb joints data and the strength of muscles of the subjects while they are performing the exercises. The prototype of this system is tested on five stroke patients and eight healthy subjects. Results show that there is no statistically significant difference between the measured and the estimated values of the upper-limb muscles of the stroke patients. Thus, the proposed method is useful in estimating the strength of the muscles of stroke patient without the need to wear any devices.


Least-squares regression; Stroke rehabilitation; Kinect; Virtual reality

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