Coupling of shoulder joint torques in individuals with chronic stroke mirrors controls, with additional non-load-dependent negative effects in a combined-torque task.

BACKGROUND: After stroke, motor control is often negatively affected, leaving survivors with less muscle strength and coordination, increased tone, and abnormal synergies (coupled joint movements) in their affected upper extremity. Humeral internal and external rotation have been included in definitions of abnormal synergy but have yet to be studied in-depth. OBJECTIVE: Determine the ability to generate internal and external rotation torque under different shoulder abduction and adduction loads in persons with chronic stroke (paretic and non-paretic arm) and uninjured controls. METHODS: 24 participants, 12 with impairments after stroke and 12 controls, completed this study. A robotic device controlled abduction and adduction loading to 0, 25, and 50% of maximum strength in each direction. Once established against the vertical load, each participant generated maximum internal and external rotation torque in a dual-task paradigm. Four linear mixed-effects models tested the effect of group (control, non-paretic, and paretic), load (0, 25, 50% adduction or abduction), and their interaction on task performance; one model was created for each combination of dual-task directions (external or internal rotation during abduction or adduction). The protocol was then modeled using OpenSim to understand and explain the role of biomechanical (muscle action) constraints on task performance. RESULTS: Group was significant in all task combinations. Paretic arms were less able to generate internal and external rotation during abduction and adduction, respectively. There was a significant effect of load in three of four load/task combinations for all groups. Load-level and group interactions were not significant, indicating that abduction and adduction loading affected each group in a similar manner. OpenSim musculoskeletal modeling mirrored the experimental results of control and non-paretic arms and also, when adjusted for weakness, paretic arm performance. Simulations incorporating increased co-activation mirrored the drop in performance observed across all dual-tasks in paretic arms. CONCLUSION: Common biomechanical constraints (muscle actions) explain limitations in external and internal rotation strength during adduction and abduction dual-tasks, respectively. Additional non-load-dependent effects such as increased antagonist co-activation (hypertonia) may cause the observed decreased performance in individuals with stroke. The inclusion of external rotation in flexion synergy and of internal rotation in extension synergy may be over-simplifications.

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

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.

Determining User Intent of Partly Dynamic Shoulder Tasks in Individuals With Chronic Stroke Using Pattern Recognition.

Stroke remains the leading cause of long-term disability in the US. Although therapy can achieve limited improvement of paretic arm use and performance, weakness and abnormal muscle synergies-which cause unintentional elbow, wrist, and finger flexion during shoulder abduction-contribute significantly to limb disuse and compound rehabilitation efforts. Emerging wearable exoskeleton technology could provide powered abduction support for the paretic arm, but requires a clinically feasible, robust control scheme capable of differentiating multiple shoulder degrees-of-freedom. This study examines whether pattern recognition of sensor data can accurately identify user intent for 9 combinations of 1- and 2- degree-of-freedom shoulder tasks. Participants with stroke (n = 12) used their paretic and non-paretic arms, and healthy controls (n = 12) used their dominant arm to complete tasks on a lab-based robot involving combinations of abduction, adduction, and internal and external rotation of the shoulder. We examined the effect of arm (paretic, non-paretic), load level (25% vs 50% maximal voluntary torque), and dataset (electromyography, load cell, or combined) on classifier performance. Results suggest that paretic arm, lower load levels, and using load cell or EMG data alone reduced classifier accuracy. However, this method still shows promise. Further work will examine classifier-user interaction during active control of a robotic device and optimization/minimization of sensors.

Feasibility of Two Different EMG-Based Pattern Recognition Control Paradigms to Control a Robot After Stroke - Case Study.

Stroke often results in chronic motor impairment of the upper-extremity yet neither traditional- nor robotics-based therapy has been able to affect this in a profound way. Supporting the weak affected shoulder against gravity improves reaching distance and minimizes abnormal co-contraction of the elbow, wrist, and fingers after stroke. However, it is necessary to assess the feasibility and efficacy of real-time controllers for this population as technology advances and a wearable shoulder device comes closer to reality. The aim of this study is to test two EMG-based controllers in this regard. A linear discriminant analysis based classifier was trained using extracted time domain and auto-regressive features from electromyographic data acquired during muscle effort required to move a load equivalent to 50 and 100% limb weight (abduction) and 150 and 200% limb weight (adduction). While rigidly connected to a custom lab-based robot, the participant was required to complete a series of lift and reach tasks under two different control paradigms: position-based control and force-based control. The participant successfully controlled the robot under both paradigms as indicated by first moving the robot arm into the proper vertical window and then reaching out as far as possible while remaining within the vertical window. This case study begins to assess the feasibility of using electromyographic data to classify the intended shoulder movement of a participant with stroke during a functional lift and reach type task. Next steps will assess how this type of support affects reaching function.

Application of an LDA Classifier for Determining User-Intent in Multi-DOF Quasi-Static Shoulder Tasks in Individuals with Chronic Stroke: Preliminary Analysis.

Abnormal synergies commonly present after stroke, limiting function and accomplishment of ADL's. They cause co-activation of sets of muscles spanning multiple joints across the affected upper-extremity. These synergies present proportionally to the amount of shoulder effort, thus the effects of the synergy reduce with reduced effort of shoulder muscles. A promising solution may be the application of a wearable exoskeletal robotic device to support the paretic shoulder in hopes to maximize function. To date, control strategies for such a device remain unknown. This work examines the feasibility of using two different linear discriminant analysis classifiers to control shoulder abduction and adduction as well as external and internal rotation simultaneously, two primary degrees of freedom that have gone largely unstudied in hemiparetic stroke. Forces, moments, and muscle activity were recorded during single and dual-tasks involving these degrees of freedom. A classifier that classified all tasks was able to determine user-intent in 14 of the 15 tasks above 90% accuracy. A classifier using force and moment data provided an average 94.3% accuracy, EMG 79%, and data sets combined, 94.9% accuracy. Parallel classifiers identifying user-intent in either abduction and adduction or internal and external rotation were 95.4%, 92.6%, and 97.3% accurate for the respective data sets. These preliminary results indicate that it seems possible to classify user-intent of the paretic shoulder in these degrees of freedom to an adequate accuracy using load cell data or load cell and EMG data combined that would enable control of a powered exoskeletal device.