Quantifying intra- and interlimb use during unimanual and bimanual tasks in persons with hemiparesis post-stroke

 Quantification and prediction DO ABSOLUTELY NOTHING TO GET SURVIVORS RECOVERED.   Useless.

Quantifying intra- and interlimb use during unimanual and bimanual tasks in persons with hemiparesis post-stroke

• Susan V. Duff,
• Aaron Miller,
• Lori Quinn,
• Gregory Youdan Jr.,
• Lauri Bishop,
• Heather Ruthrauff &
• Eric Wade 

Journal of NeuroEngineering and Rehabilitation volume 19, Article number: 44 (2022) Cite this article

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Abstract

Background

Individuals with hemiparesis post-stroke often have difficulty with tasks requiring upper extremity (UE) intra- and interlimb use, yet methods to quantify both are limited.

Objective

To develop a quantitative yet sensitive method to identify distinct features of UE intra- and interlimb use during task performance.

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 × 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.

Introduction

Finely tuned upper extremity (UE) intra- and interlimb use is controlled through intact neural coupling [1], which requires timing of movements and sequential, rhythmic use of limb segments on one or both sides of the body [2]. This upper limb coupling enables interaction with the environment and the performance of goal-oriented tasks such as activities of daily living (ADLs). The type of tasks performed range from unimanual (single limb use), to bimanual symmetric (mirrored), to bimanual asymmetric with different motion exhibited in each limb. For persons with hemiparesis post-stroke, tasks requiring UE coupling can be difficult to execute due to limited strength, mobility, and motor control resulting in the execution of compensatory, yet functional, movement patterns [3,4,5]. Compensatory strategies may include increased trunk involvement during arm motion, limb disuse or asymmetry during mirrored bimanual tasks, and inefficient motion or atypical synergistic movements during task performance [6,7,8]. Although compensation promotes independence in everyday tasks, it can also impede recovery of intra- and interlimb use inherent in unimanual and bimanual performance [5, 9]. Determining the extent of coupling within and between the arms and how it changes with recovery and rehabilitation requires assessment measures sensitive to subtle changes in motion and task performance.

Most UE clinical assessments evaluate function of the paretic limb during unimanual tasks with limited emphasis on bimanual function [10,11,12]. An exception to this is the Assisting Hand Assessment (AHA) [13, 14], a tool originally designed to assess how effectively the more affected limb is used during bimanual tasks in children with unilateral UE dysfunction. The AHA has been recently adapted for use in adults post-stroke, i.e. the Adult AHA Stroke (Ad-AHA Stroke) [15]. However, as with other observation-based tools, the Ad-AHA Stroke may not be sensitive enough to detect small yet significant changes in motor behavior occurring with natural recovery or rehabilitation. A highly sensitive, objective measure requiring minimal equipment is needed to quantify intra- and interlimb use across a range of tasks and settings.

Inertial measurement units (IMU), are body-worn sensors that monitor and transmit changes in movement during the execution of everyday tasks [16]. IMU sensors have been used with individuals post-stroke and other neurological conditions, to capture the quality and quantity of motion during typical and atypical motor behaviors [16, 17]. These sensors can detect quantitative changes in movement patterns that differentiate between typical and atypical motor behavior. A challenge in using IMU sensors is that they produce derived, differential motion measures, such as linear acceleration and angular rate of change. Therefore, unlike traditional marker-based motion capture systems, raw data cannot be easily used to directly reconstruct changes in limb position. Instead, IMU data requires custom signal and data processing techniques to produce clinically relevant metrics [18].

Development of an accurate yet sensitive system using IMU data to identify distinct features of UE intra- and interlimb use is a sequential process. For our purposes, we operationalize intra- and interlimb use in regard to amplitude, time domain and frequency domain. Results of our pilot work suggest that development of a single motion parameter per sensor, using a novel algorithm, would allow comparison by task type between groups and allow for initial validation against widely used clinical measures [19]. The objectives of this current study were to: (1) evaluate the ability of sensor-derived motion parameters to distinguish between UE task type (unimanual, bimanual symmetric, and bimanual asymmetric tasks) in healthy controls; (2) evaluate the ability of motion parameters to differentiate between UE intra- and interlimb use in healthy controls and individuals post stroke; and (3) validate findings from sensor-derived motion parameters against clinical measures commonly used to assess performance in persons post-stroke, including the UE Fugl-Meyer (UEFM) and the Ad-AHA Stroke Assessments.