Rehabilitation

YC Pan, B Goodwin, E Sabelhaus, KM Peters, KF Bjornson, KLD Pham, WO Walker, KM Steele (2020) “Feasibility of using acceleration-derived jerk to quantify bimanual arm use” Journal of NeuroEngineering and Rehabilitation

Dr. Steele JournalArticle, UbiquitousRehabilitation , , , , ,

Journal Article in Journal of NeuroEngineering & Rehabilitation

Two plots illustrating jerk ratio results. The plot on the left shows the probability distribution from one child with cerebral palsy before, during, and after constraint induced movement therapy. Before therapy, the probability distribution is shifted to the left of the center line, indicating that the individual relies much more on their non-paretic hand during daily life. During therapy, when their non-paretic hand is in a cast, the curve shifts to the right of the center line. This indicates they are using their paretic hand much more - which makes sense, since the other hand is in a cast. Unfortunately, after the cast is removed at the end of therapy, the curve is nearly identical to the curve before treatment, suggesting that after this intensive therapy the child did not use their paretic hand more during daily life. The figure on the right shows the summary metric from this plot, called jerk ratio 50 - which is just the 50% value of the probability density function - for all 5 children with cerebral palsy before, during, and after therapy. All the children have JR50 greater than 0.5 before therapy, which means they use their non-paretic hand more during daily life. During therapy, these values drop to 0.2 - 0.5, indicating that they use their paretic hand much more during CIMT. However, after therapy the JR50 values for all five participants return to close to their baseline value before therapy.
(Left) Example of jerk ratio distribution for one child with cerebral palsy before, during, and after constraint induced movement therapy. (Right) Summary metric of jerk ratio (jerk ratio-50) for all five children with cerebral palsy.

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 (non-paretic) 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 non-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.

Code

The algorithm for calculating jerk ratio, as well as user-friendly code to produce plots similar to the figure above are provided open-source as Python 3.6 code as a Python Jupyter Notebook within Google Colab. With this resource, research groups can use existing or newly created data from accelerometers to analyze jerk ratio as a complementary metric to existing measures, enabling comparison between research studies or centers that may rely on different sensors and activity count algorithms.

BR Shuman, M Goudriaan, K Desloovere, MH Schwartz, KM Steele (2018) “Associations Between Muscle Synergies and Treatment Outcomes in Cerebral Palsy Are Robust Across Clinical Centers.” Archives of Physical Medicine and Rehabilitation

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Journal article in Archives of Physical Medicine and Rehabilitation:

In collaboration with Gillette Children’s Hospital and University Hospital Pellenberg we examined whether associations between treatment outcomes and muscles synergies are robust between clinical centers.

Objective: To determine whether patient-specific differences in motor control quantified using muscle synergy analysis were associated with changes in gait after treatment of cerebral palsy (CP) across 2 clinical centers with different treatments and clinical protocols.
Design: Retrospective cohort study.
Setting: Clinical medical center.
Participants: Center 1: children with CP (n=473) and typically developing (TD) children (n=84). Center 2: children with CP (n=163) and TD children (n=12).
Interventions: Standard clinical care at each center.
Main outcome measures: The Dynamic Motor Control Index During Walking (walk-DMC) was computed from electromyographic data during gait using muscle synergy analysis. Regression analysis was used to evaluate whether pretreatment walking speed or kinematics, muscle synergies, treatment group, prior treatment, or age were associated with posttreatment changes in gait at both clinical centers.
Results: Walk-DMC was significantly associated with changes in speed and kinematics after treatment with similar regression models at both centers. Children with less impaired motor control were more likely to have improvements in walking speed and gait kinematics after treatment, independent of treatment group.
Conclusions: Dynamic motor control evaluated with synergy analysis was associated with changes in gait after treatment at both centers, despite differences in treatments and clinical protocols. This study further supports the finding that walk-DMC provides additional information, not captured in traditional gait analysis, that may be useful for treatment planning.