Sonomyography accurately captures joint kinematics during volitional and electrically stimulated motion in healthy adults and an individual with cerebral palsy

 If your incompetent? doctor isn't getting an objective damage diagnosis like this; THERE IS NO FUCKING WAY TO CREATE REPEATABLE PROTOCOLS THAT DELIVER RECOVERY EVERY TIME!

Or maybe like this:

Wearable Devices for Parkinson’s Disease: The Future Is Here December 2024

I expect your stroke doctor to OBJECTIVELY know the damage. Like the number of dead neurons, dead myelinated fibers, dead synapses! Without that knowledge your doctor can never correlate what protocols fix them.

I would just flat out ask your doctor why they are so fucking incompetent that they know nothing objective about your damage! Quoting NIHSS or Barthel is the height of stupidity!

NIHSS and the Berthel Index ARE NOT DAMAGE DIAGNOSES, they do not give you the 3d location of your dead and damaged neurons. In my opinion, they are FUCKING WORTHLESS to getting you recovered!)

The latest here:

Sonomyography accurately captures joint kinematics during volitional and electrically stimulated motion in healthy adults and an individual with cerebral palsy

• Shriniwas Patwardhan,
• Noah Rubin,
• Katharine E. Alter,
• Diane L. Damiano &
• Thomas C. Bulea 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Despite significant advances in biosignal extraction techniques for studying neuromotor disorders, there remains an unmet need for a method that effectively links muscle structure and dynamics to muscle activation. Addressing this gap could improve the quantification of neuromuscular impairments and pave the way for precision rehabilitation. In this study, we demonstrate the proof of concept of recording multimodal signals from the brain, muscles, and resulting limb kinematics. We also explore the use of ultrasound imaging to extract limb kinematics.

Methods

We collected data from three healthy volunteers and one individual with cerebral palsy during single degree-of-freedom ankle and wrist movements. Participants performed range of motion (ROM) tasks at approximately 1-second intervals, either volitionally or through functional electrical stimulation. We simultaneously recorded electroencephalography, surface electromyography (EMG), continuous ultrasound imaging, and motion capture data. Joint kinematics were computed from ultrasound imaging using a technique called sonomyography (SMG), and we evaluated the technical feasibility of estimating joint kinematics from both sonomyography and surface EMG signals.

Results

The technical feasibility study evaluated joint angle prediction using EMG and SMG under volitional (FES-OFF) and electrically stimulated (FES-ON) conditions. Root mean squared error (RMSE) between predicted and measured joint angles was computed for multiple methods of extracting kinematics from EMG and SMG. EMG-based RMSE ranged from 0.34 to 0.57 (FES-OFF) and 0.43–0.51 (FES-ON). SMG-based RMSE ranged from 0.10 to 0.25 across all conditions and methods. Linear regression analysis produced

values between 0.31 and 0.81 depending on joint, condition, and method. No significant RMSE difference was found between FES-ON and FES-OFF conditions within SMG. SMG RMSE values were also comparable to previously reported values (10-25%) in prior literature.

Conclusion

Our findings suggest that sonomyography can be used as a noninvasive method for estimating joint kinematics when the joint movement is driven either by volition or by functional electrical stimulation. This technique can potentially be be useful in evaluating altered muscle dynamics and driving assistive and rehabilitation devices in individuals with neuromotor disorders such as cerebral palsy.