Applying LDA-based pattern recognition to predict isometric shoulder and elbow torque generation in individuals with chronic stroke with moderate to severe motor impairment

Fuck, we don't care about prediction you blithering idiots. What is the protocol to resolve the shoulder impairment?  Have a stroke and try to recover with your stupidity of research.  I take no prisoners.

Applying LDA-based pattern recognition to predict isometric shoulder and elbow torque generation in individuals with chronic stroke with moderate to severe motor impairment 

• Joseph V. Kopke,
• Levi J. Hargrove and
• Michael D. EllisEmail author

Journal of NeuroEngineering and Rehabilitation201916:35

https://doi.org/10.1186/s12984-019-0504-1

©  The Author(s). 2019

• Received: 11 September 2018
• Accepted: 22 February 2019
• Published: 5 March 2019

Abstract

Background

Abnormal synergy is a major stroke-related movement impairment that presents as an unintentional contraction of muscles throughout a limb. The flexion synergy, consisting of involuntary flexion coupling of the paretic elbow, wrist, and fingers, is caused by and proportional to the amount of shoulder abduction effort and limits reaching function. A wearable exoskeleton capable of predicting movement intent could augment abduction effort and therefore reduce the negative effects of distal joint flexion synergy. However, predicting movement intent from abnormally-coupled torques or EMG signals and subsequent use as a control signal remains elusive. One control strategy that has proven viable, effective, and computationally efficient in myoelectric prostheses for use in individuals with amputation is linear discriminant analysis (LDA)-based pattern recognition. However, following stroke, shoulder effort has been shown to have a negative effect on classification accuracy of hand tasks due to the multi-joint torque coupling of abnormal synergy. This study focuses on the evaluation of an LDA-based classifier to predict individual degrees-of-freedom of the shoulder and elbow joints.

Methods

Six degree-of-freedom load cell data along with eight channels of EMG data were recorded during eight tasks (shoulder abduction and adduction, horizontal abduction and adduction, internal rotation and external rotation, and elbow flexion and extension) and used to create feature sets for LDA-based classifiers to distinguish between these eight classes.

Results

Cross-validation yielded functional offline classification accuracies (> 90%) for two of the eight classes using EMG-only, four of the eight classes using load cell-only, and six of the eight classes using a combined feature set with average accuracies of 83, 91, and 92% respectively.

Conclusions

The most common misclassifications were between shoulder adduction and internal rotation followed by shoulder abduction and external rotation. It is unknown whether the strategies used were due to abnormal synergy or other factors. LDA-based pattern recognition may be a viable control option for predicting movement intention and providing a control signal for a wearable exoskeletal assistive device. Future work will need to test the approach in a more complex multi-joint task, specifically one that attempts to tease apart shoulder abduction/external rotation and adduction/internal rotation.