Quantifying Intra- and Interlimb Coordination in Persons With Hemiparesis Post-stroke

Talk to any survivor and they won't give a shit about your quantification. Unless you have bamboozled them into accepting your tyranny of low expectations, where ANY recovery is a success.  They want to know your EXACT STROKE PROTOCOLS TO 100% RECOVERY!

Quantifying Intra- and Interlimb Coordination in Persons With Hemiparesis Post-stroke

Susan V Duff, Aaron J Miller, Lori Quinn, Gregory A Youdan Jr, Lauri Bishop, Heather Ruthrauff, Eric R Wade

DOI:

10.21203/rs.3.rs-832371/v1

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License:

This work is licensed under a CC BY 4.0 License. Read Full License

Background

Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb coordination, yet methods to quantify both are limited. Objective: To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb coordination.

Methods

Twenty adults post-stroke and 20 controls wore five inertial sensors (wrists, upper arms, sternum) during 12 seated UE tasks. Three sensor modalities (acceleration, angular rate of change, orientation) were examined for three metrics (peak to peak amplitude, time, and frequency). To allow for comparison between sensor data, the resultant values were combined into one motion parameter, per sensor pair, using a novel algorithm. This motion parameter was compared in a group-by-task analysis of variance as a similarity score (0-1) between key sensor pairs: sternum to wrist, wrist to wrist, and wrist to upper arm. A use ratio (paretic/non-paretic arm) was calculated in persons post-stroke from wrist sensor data for each modality and compared to scores from the Adult Assisting Hand Assessment (Ad-AHA Stroke) and UE Fugl-Meyer (UEFM).

Results

A significant group x task interaction in the similarity score was found for all key sensor pairs. Post-hoc tests between task type revealed significant differences in similarity for sensor pairs in 8/9 comparisons for controls and 3/9 comparisons for persons post stroke. The use ratio was significantly predictive of the Ad-AHA Stroke and UEFM scores for each modality.

Conclusions

Our algorithm and sensor data analyses distinguished task type within and between groups and were predictive of clinical scores. Future work will assess reliability and validity of this novel metric to allow development of an easy-to-use app for clinicians.