Journal Article in Journal of NeuroEngineering & Rehabilitation
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.
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.
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.
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.
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.
This study demonstrated that muscle activations estimated from static optimization using generic musculoskeletal modeling does not accurately predict EMG profiles for children with CP or TD peers. Constraining activation patterns to experimentally measured synergies increased estimated muscle stresses, but did not improve the estimation of muscle activations for either group.
Neuromusculoskeletal simulation provides a promising platform to inform the design of assistive devices or inform rehabilitation. For these applications, a simulation must be able to accurately represent the person of interest, such as an individual with a neurologic injury. If a simulation fails to predict how an individual recruits and coordinates their muscles during movement, it will have limited utility for informing design or rehabilitation. While inverse dynamic simulations have previously been used to evaluate anticipated responses from interventions, like orthopaedic surgery or orthoses, they frequently struggle to accurately estimate muscle activations, even for tasks like walking. The simulated muscle activity often fails to represent experimentally measured muscle activity from electromyographic (EMG) recordings. Research has theorized that the nervous system may simplify the range of possible activations used during dynamic tasks, by constraining activations to weighted groups of muscles, referred to as muscle synergies. Synergies are altered after neurological injury, such as stroke or cerebral palsy (CP), and may provide a method for improving subject-specific models of neuromuscular control.
The aim of this study was to test whether constraining simulation to synergies could improve estimated muscle activations compared to EMG data.
We evaluated modeled muscle activations during gait for six typically developing children (TD) and six children with CP. Muscle activations were estimated with: 1) static optimization (SO), minimizing muscle activations squared, and 2) synergy static optimization (SynSO), minimizing synergy activations squared using the weights identified from EMG data for 2-5 synergies.
While SynSO caused changes in estimated activations compared to SO, the correlation to EMG data was not higher in SynSO than SO for either TD or CP groups . The correlations to EMG were higher in CP than TD for both SO (CP: 0.48, TD: 0.36) and SynSO (CP: 0.46, TD: 0.26 for 5 synergies). Constraining activations to SynSO caused the simulated muscle stress to increase compared to SO for all individuals, causing a 157% increase with two synergies.
These results suggest that constraining simulated activations in inverse dynamic simulations to subject-specific synergies alone does not improve estimation of muscle activations during gait for generic musculoskeletal models.
We partnered with NBC Learn to share some of our work on exoskeletons to help encourage students to consider a career in engineering. What can be more exciting than musculoskeletal modeling, exoskeletons, horses, and stuffed animals?
Congratulations to Nicole Zaino for being awarded the ESMAC (European Society of Movement Analysis for Adults and Children) Best Paper Award. Nicole received this award at the 2019 ESMAC conference in Amsterdam, September 23-28, 2019 where she gave her talk: “Spasticity reduction in children with cerebral palsy is not associated with reduced energy during walking.” For more information, visit ESMAC.
Congratulations to Nicole Zaino and our colleague Mike Schwartz at Gillette Children’s Specialty Healthcare for both being nominated as finalists for the Best Presentation Award at the upcoming ESMAC Conference in Amsterdam. Their abstracts are among the top 16 submissions to the conference and the final award will be determined based upon their presentations.
Nicole will be presenting her research:
Spasticityreductioninchildrenwithcerebralpalsyisnotassociatedwith reduced energy during walking
Selective dorsal rhizotomy reduces spasticity, but does it also reduce energy consumption during walking? In an analysis of over 300 children with cerebral palsy, Nicole demonstrated that although rhiztomy does reduce spasticity, it does not reduce energy consumption. These results provide further evidence that spasticity is not a main contributor to elevated energy among people with cerebral palsy. You can also learn more about this study from our recent submitted manuscript, available on bioRxiv.
Mike will be presenting his research:
The effects of walking speed and age on energy consumption in children with cerebral palsy and their typically developing peers
We know that walking energy is high among people with cerebral palsy, and that energy varies with speed and age. Using retrospective data of over 300 kids with cerebral palsy and 150 typically-developing peers, Mike used a statistical model to evaluate these speed and age effects. He found that energy decreases until 8-10 years of age for kids with CP, while it remains stable beyond age 5 for typically-developing peers. Kids with CP also have a greater elevation in energy with greater walking speeds. These results are important to help quantify and understand impacts of interventions, like surgery or assistive devices, which are often done during this time period when kids are still growing and developing.
They will both be presenting in the Optimizing Energy Cost session from 11:40-12:30 on Thursday, September 26th.