Spectral Analyses of Wrist Motion in Individuals Poststroke: The Development of a Performance Measure With Promise for Unsupervised Settings

Useless, measuring something, NOT creating protocols for recovery.

Spectral Analyses of Wrist Motion in Individuals Poststroke: The Development of a Performance Measure With Promise for Unsupervised Settings

 

Eric Wade, PhD, Christina Chen, Carolee J. Winstein, PhD

First Published November 8, 2013 Research Article Find in PubMed 

https://doi.org/10.1177/1545968313505911

Article information

 

Abstract

Background. 

Upper extremity use in daily life is a critical ingredient of continued functional recovery after cerebral stroke. However, time-evolutions of use-dependent motion quality are poorly understood due to limitations of existing measurement tools.  

Objective. 

Proof-of-concept study to determine if spectral analyses explain the variability of known temporal kinematic movement quality (ie, movement duration, number of peaks, jerk) for uncontrolled reach-to-grasp tasks.  

Methods. 

Ten individuals with chronic stroke performed unimanual goal-directed movements using both hands, with and without task object present, wearing accelerometers on each wrist. Temporal and spectral measures were extracted for each gesture. The effects of performance condition on outcome measures were determined using 2-way, within subject, hand (nonparetic vs paretic) × object (present vs absent) analysis of variance. Regression analyses determined if spectral measures explained the variability of the temporal measures.  

Results. 

There were main effects of hand on all 3 temporal measures and main effects of object on movement duration and peaks. For the paretic limb, spectral measures explain 41.2% and 51.1% of the variability in movement duration and peaks, respectively. For the nonparetic limb, spectral measures explain 40.1%, 42.5%, and 27.8% of the variability of movement duration, peaks, and jerk, respectively.  

Conclusions. 

Spectral measures explain the variability of motion efficiency and control in individuals with stroke. Signal power from 1.0 to 2.0 Hz is sensitive to changes in hand and object. Analyzing the evolution of this measure in ambient environments may provide as yet uncharted information useful for evaluating long-term recovery.

Keywords stroke rehabilitation, hemiplegia, kinematics, upper extremity, accelerometry, motion sensing

Introduction

Optimal and meaningful recovery after stroke requires spontaneous use and seamless integration of the paretic arm and hand into the natural environment.^1,2 Use of the paretic upper extremity (UE) is directly tied to an overall sense of recovery after stroke; stroke survivors and their families reported that arm impairment was not only of extreme detriment to quality of life but also poorly understood, appreciated, and addressed.³ From a clinical perspective, we know that arm use and recovery are important for behavioral and neurological changes. Imaging studies have shown that after a brain injury such as stroke, cortical areas tied to a damaged limb can be “taken over” by adjacent areas. Countering this phenomenon requires increased use of the paretic limb to promote positive, functional neuroplasticity in cortical areas representing that limb.^4,5 Thus, from the perspective of the patient, and from neurobehavioral studies, skilled, voluntary use of the stroke-affected upper limb is required to optimize restitution and recovery. Because recovery after stroke depends on a number of variables including the type and dose of therapy, understanding normal patterns of recovery and use is necessary to guide monitoring of future patients.⁶ Unfortunately, in spite of its well-established importance, there is a lack of understanding of the time-evolution of arm recovery after stroke.

In certain laboratory settings, kinematic data have provided insight into recovery associated with motoric changes. Rohrer et al demonstrated in some participants that poststroke recovery can be characterized by increased motion smoothness, as measured by mean squared jerk cost (the derivative of acceleration, first cited as a measure of performance by Flash and Hogan⁷).⁸ Panarese et al demonstrated recovery of UE motor function as measured by velocity, path error, and the number of peaks in the velocity profile.⁹ Colombo et al demonstrated that changes in mean movement velocity and forces applied to a robotic manipulandum correlated with changes in Fugl-Meyer and Motor Status Score scales.¹⁰ These studies indicate that kinematic measures provide some understanding of the mechanisms of recovery. More recently, researchers have sought to relate kinematics to known functional assessments.^11,12 These authors used tasks from the Wolf Motor Function Test and the Fugl-Meyer Assessment to correlate quantitative measures with clinician assessments. This body of work focuses on controlled, ballistic targeted reaching tasks. How these movements might correspond to activities of daily living (ADL)–inspired uncontrolled movements is not known. Here, uncontrolled refers to movements for which the instructions provide a defined end goal, but no restriction on the strategy used to achieve it (eg, put the pencil in the cup). Uncontrolled movements are more similar to the types of movements performed in ambient settings.

We are interested in developing novel tools with the sensitivity to detect and potentially reward incremental and immediate changes in upper limb use in natural environments based on spectral analyses. Such analyses have been used to determine (a) critical changes in performance for those on medication for Parkinson’s disease,¹³ (b) behavior that anticipates falls in elderly walkers,¹⁴ (c) differentiation of lower extremity movements after stroke,¹⁵ and (d) UE practice of skilled tasks by nondisabled individuals.^16,17 From these studies, frequency ranges between 0 and 2 Hz, and up to 10 Hz, were useful in distinguishing relative levels of motion quality during task performance. Such studies have yet to be conducted for UE task performance in the chronic poststroke population. This article presents the first of a 2-part research project designed to capture meaningful information about motor patterns of people with motor deficits in real-world settings. We believe that spectral analyses of unsegmented continuous in-home data are promising tools for revealing meaningful information regarding motion quality. This is important because, in ambient environments, standard temporal kinematic measures cannot be obtained without knowing, according to a secondary measure, how to extract, or segment, a gesture. The first step is to establish the meaningfulness of these spectral measures on manually segmented gesture data, which is the focus of this article.

Our objective is to determine the nature of the relationship between spectral measures and known temporal measures of performance (movement duration, DUR; number of peaks, PKS; and mean squared jerk cost, MSJ) during uncontrolled goal-directed reaching and grasping movements inspired by ADLs. These dependent measures were selected because their relationship to motor ability metrics such as efficiency, motor control, smoothness, and coordination has been well established for controlled movements.^8,18-22 For this proof-of-concept study, we hypothesize that spectral measures will explain the variability of known temporal measures of motion quality. To test this hypothesis, we use a 2-stage process. First, we find how both temporal and spectral measures vary under different task performance conditions. Second, we investigate the ability of the spectral measures to explain the variability of the temporal measures. We designed a study wherein participants performed a number of simulated unimanual ADLs in a laboratory environment using the paretic and nonparetic limb in separate runs. Differences in performance with the paretic and nonparetic limb are reflective of differences in motor control, spasticity, and strength.²³ To elicit motoric changes within a task, participants were also asked to perform each task with and without the task object present. Object affordance was shown by Wu and colleagues to create noticeable changes in motion quality¹⁸ reflective of differences in planning.^18,24

Our long-term aim is to use spectral analyses of inertial sensor data to develop a novel measure of UE use that can be determined without a priori knowledge of the task being performed. The advantage of such a measure, if proven to be valid and reliable, is that it can be applied to data sets obtained in unsupervised, natural environments such as the home to measure motion quality and to quantitatively measure recovery of paretic limb use.