Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Lucena, Diogo Schwerz de; Stoller, Oliver; Rowe, Justin B.; Chan, Vicky; Reinkensmeyer, David J.

doi:10.1109/icorr.2017.8009477

Cited by 36 publications

(58 citation statements)

References 20 publications

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“…Table 1 provides a summary of variables. In addition, two newly proposed variables were calculated, the jerk asymmetry index [34] and the spectral arc length [35,36]. These variables were calculated as they have been proposed to measure smoothness of movement, an aspect of quality of movement, by others in the field.…”

Section: Accelerometer Variablesmentioning

confidence: 99%

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Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Barth

Klaesner

Lang

2020

J NeuroEngineering Rehabil

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Background Standardized assessments are used in rehabilitation clinics after stroke to measure restoration versus compensatory movements of the upper limb. Accelerometry is an emerging tool that can bridge the gap between in- and out-of-clinic assessments of the upper limb, but is limited in that it currently does not capture the quality of a person’s movement, an important concept to assess compensation versus restoration. The purpose of this analysis was to characterize how accelerometer variables may reflect upper limb compensatory movement patterns after stroke. Methods This study was a secondary analysis of an existing data set from a Phase II, single-blind, randomized, parallel dose–response trial (NCT0114369). Sources of data utilized were: (1) a compensatory movement score derived from video analysis of the Action Research Arm Test (ARAT), and (2) calculated accelerometer variables quantifying time, magnitude and variability of upper limb movement from the same time point during study participation for both in-clinic and out-of-clinic recording periods. Results Participants had chronic upper limb paresis of mild to moderate severity. Compensatory movement scores varied across the sample, with a mean of 73.7 ± 33.6 and range from 11.5 to 188. Moderate correlations were observed between the compensatory movement score and each accelerometer variable. Accelerometer variables measured out-of-clinic had stronger relationships with compensatory movements, compared with accelerometer variables in-clinic. Variables quantifying time, magnitude, and variability of upper limb movement out-of-clinic had relationships to the compensatory movement score. Conclusions Accelerometry is a tool that, while measuring movement quantity, can also reflect the use of general compensatory movement patterns of the upper limb in persons with chronic stroke. Individuals who move their limbs more in daily life with respect to time and variability tend to move with less movement compensations and more typical movement patterns. Likewise, individuals who move their paretic limbs less and their non-paretic limb more in daily life tend to move with more movement compensations at all joints in the paretic limb and less typical movement patterns.

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Section: Accelerometer Variablesmentioning

confidence: 99%

“…Two variables have been proposed to reflect movement smoothness as an aspect of quality of movement [34][35][36]. Figure 4 shows the relationship of the compensatory movement score to the jerk asymmetry index (Fig.…”

Section: Relationships Of Variables To Compensatory Movementmentioning

confidence: 99%

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Barth

Klaesner

Lang

2020

J NeuroEngineering Rehabil

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“…Like the use ratio, the mean of magnitude ratio values, compiled across a day or more also has a narrow distribution, with a mean of −0.1 ± 0.3. 21 Other examples of symmetry variables under development include jerk asymmetry and acceleration magnitude asymmetry, 26 percentage of contribution of each arm, 27 and variation ratio. 19 Early data from these more complex symmetry variables suggest that, like the use ratio, they will have a narrow range of distribution in the typical adult population.…”

Section: Variables Of Symmetry In Adultsmentioning

confidence: 99%

Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan

Smith

Lang

2019

Archives of Physical Medicine and Rehabilitation

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Knowledge of upper limb activity in the natural environment is critical for evaluating the effectiveness of rehabilitation services. Wearable sensors allow efficient collection of these data, and have the potential to be less burdensome than self-report measures of activity. Sensors can capture many different variables of activity and daily performance, many of which could be useful in identifying deviation from typical movement behavior and/or measuring outcomes from rehabilitation interventions. While it has potential, sensor measurement is just emerging and there is a lack of consensus on which variables of daily performance are valid, sensitive, specific, and useful. We propose that symmetry of full-day upper limb movement is a key variable. We describe here that symmetry is valid, robustly observed within a narrow range across the lifespan in typical development, and shows evidence of being different in populations with neuromotor impairment. Key next steps include the determination of sensitivity, specificity, minimal detectable change, and minimal clinically important change/difference. This information is needed to determine whether an individual belongs to the typical or atypical group, whether or not change has occurred, and whether or not that change is beneficial.

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“…The dotted grey lines represent the range of JR50 values across the TD cohort kinematic metrics and existing functional tests. Using principal component analysis to identify the most indicative metric of human motion, they concluded that metrics based on acceleration and jerk contributed to the second principal component and accounted for 31% of variance across nine adult stroke survivors [22]. However, this work relied on an IMU, including both accelerometer and gyroscope, which increases sensor cost and decreases battery life.…”

Section: Discussionmentioning

confidence: 99%

“…As early as 1985, Flash & Hogan described the coordination of arm movements with jerk, noting its advantage of capturing smoothness of movement [20], which is also commonly altered after neurologic injury [21]. Lucena and colleagues in 2017 showed the potential benefits of using jerk measured from inertial measurement units (IMUs) to evaluate bimanual arm use among stroke survivors [22]. They showed strong correlation with activity count measures; however, an IMU requires significantly more power, data storage, and cost than an accelerometer alone.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of using acceleration-derived jerk to quantify bimanual arm use

Pan

Goodwin

Sabelhaus

et al. 2020

J NeuroEngineering Rehabil

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Background: Accelerometers have become common for evaluating the efficacy of rehabilitation for patients with neurologic disorders. For example, metrics like use ratio (UR) and magnitude ratio (MR) have been shown to differentiate movement patterns of children with cerebral palsy (CP) compared to typically-developing (TD) peers. However, these metrics are calculated from "activity counts"a measure based on proprietary algorithms that approximate movement duration and intensity from raw accelerometer data. Algorithms used to calculate activity counts vary between devices, limiting comparisons of clinical and research results. The goal of this research was to develop complementary metrics based on raw accelerometer data to analyze arm movement after neurologic injury. Method: We calculated jerk, the derivative of acceleration, to evaluate arm movement from accelerometer data. To complement current measures, we calculated jerk ratio (JR) as the relative jerk magnitude of the dominant (nonparetic) and non-dominant (paretic) arms. We evaluated the JR distribution between arms and calculated the 50th percentile of the JR distribution (JR50). To evaluate these metrics, we analyzed bimanual accelerometry data for five children with hemiplegic CP who underwent Constraint-Induced Movement Therapy (CIMT) and five typically developing (TD) children. We compared JR between the CP and TD cohorts, and to activity count metrics. Results: The JR50 differentiated between the CP and TD cohorts (CP = 0.578 ± 0.041 before CIMT, TD = 0.506 ± 0.026), demonstrating increased reliance on the dominant arm for the CP cohort. Jerk metrics also quantified changes in arm use during and after therapy (e.g., JR50 = 0.378 ± 0.125 during CIMT, 0.591 ± 0.057 after CIMT). The JR was strongly correlated with UR and MR (r = − 0.92, 0.89) for the CP cohort. For the TD cohort, JR50 was repeatable across three data collection periods with an average similarity of 0.945 ± 0.015. Conclusions: Acceleration-derived jerk captured differences in motion between TD and CP cohorts and correlated with activity count metrics. The code for calculating and plotting JR is open-source and available for others to use and build upon. By identifying device-independent metrics that can quantify arm movement in daily life, we hope to facilitate collaboration for rehabilitation research using wearable technologies.

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Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Cited by 36 publications

References 20 publications

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Relationships between accelerometry and general compensatory movements of the upper limb after stroke

Sensor Measures of Symmetry Quantify Upper Limb Movement in the Natural Environment Across the Lifespan

Feasibility of using acceleration-derived jerk to quantify bimanual arm use

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