This work highlights the potential of data-driven models grounded in dynamical systems theory to predict complex individualized responses to ankle exoskeletons., without requiring explicit knowledge of the individual’s physiology or motor control
Aim: Evaluate the ability of three classes of subject-specific phase-varying (PV) models to predict kinematic and myoelectric responses to ankle exoskeletons during walking, without requiring prior knowledge of specific user characteristics.
Method: Data from 12 unimpaired adults walking with bilateral passive ankle exoskeletons were captured. PV, linear PV (LPV), and nonlinear PV (NPV) models leveraged Floquet theory to kinematics and muscle activity in response to three exoskeleton torque conditions.
Results: The LPV model’s predictions were more accurate than the PV model when predicting less than 12.5% of a stride in the future and explained 49–70% of the variance in hip, knee and ankle kinematic responses to torque. The LPV model also predicted kinematic responses with similar accuracy to the more-complex NPV model. Myoelectric responses were challenging to predict with all models, explaining at most 10% of the variance in responses.
Interpretation: This work highlights the potential of data-driven PV models to predict complex subject-specific responses to ankle exoskeletons and inform device design and control.
These results suggest that control theory modeling can provide a platform to successfully quantify device performance in the absence of errors arising from motor impairments
Photo (top and bottom) of a user using a slider (top) and muscles (bottom) to control a cursor on the screen. (Top image) Side image of user. User rests their elbow and pinches the slider and moves the slider towards and away from their body to control the cursor. (Bottom image) Side image of user. User is strapped to a rigid device holding a bar with hands supinated towards the ceiling, with the forearms at a 90 degree angle from the upper arms. Electrodes are placed on the biceps and triceps and labelled. Arrows pointing up and down indicate that users move their arm up and down to control the cursor.
Background:Manual device interaction requires precise coordination which may be difficult for users with motor impairments. Muscle interfaces provide alternative interaction methods that may enhance performance, but have not yet been evaluated for simple (eg. mouse tracking) and complex (eg. driving) continuous tasks. Control theory enables us to probe continuous task performance by separating user input into intent and error correction to quantify how motor impairments impact device interaction
Aim: Propose and extend an experimental and analytical method to guide future development of accessible interfaces like muscle interfaces using control theory
Method: We compared the effectiveness of a manual versus a muscle interface for eleven users without and three users with motor impairments performing continuous tasks.
Results: Both user groups preferred and performed better with the muscle versus the manual interface for the complex continuous task.
Interpretation: Results suggest muscle interfaces and algorithms that can detect and augment user intent may be especially useful for future design of interfaces for continuous tasks.
Momona also gave a phenomenal talk on this paper last week in the University of Washington’s ‘DUB Shorts’ series (video posted below). Nice job Momona!
This retrospective analysis demonstrated that energy consumption is not reduced after rhizotomy when compared to matched controls with cerebral palsy.
Aim: To determine whether energy consumption changes after selective dorsal rhizotomy (SDR) among children with cerebral palsy (CP).
Method: We retrospectively evaluated net nondimensional energy consumption during walking among 101 children with bilateral spastic CP who underwent SDR (59 males, 42 females; median age [5th centile, 95th centile] 5y 8mo [4y 2mo, 9y 4mo]) compared to a control group of children with CP who did not undergo SDR. The control group was matched by baseline age, spasticity, and energy consumption (56 males, 45 females; median age [5th centile, 95th centile] 5y 8mo [4y 1mo, 9y 6mo]). Outcomes were compared at baseline and follow‐up (SDR: mean [SD] 1y 7mo [6mo], control: 1y 8mo [8mo]).
Results: The SDR group had significantly greater decreases in spasticity compared to matched controls (–42% SDR vs –20% control, p<0.001). While both groups had a modest reduction in energy consumption between visits (–12% SDR, –7% control), there was no difference in change in energy consumption (p=0.11) or walking speed (p=0.56) between groups.
Interpretation: The SDR group did not exhibit greater reductions in energy consumption compared to controls. The SDR group had significantly greater spasticity reduction, suggesting that spasticity had minimal impact on energy consumption during walking in CP. These results support prior findings that spasticity and energy consumption decrease with age in CP. Identifying matched control groups is critical for outcomes research involving children with CP to account for developmental changes.
Journal Article in Prosthetics & Orthotics International
Assessments of human movement are clinically important. However, accurate measurements are often unavailable due to the need for expensive equipment or intensive processing. For orthotists and therapists, shank-to-vertical angle (SVA) is one critical measure used to assess gait and guide prescriptions. Smartphone-based sensors may provide a widely-available platform to expand access to quantitative assessments.
Assess accuracy and repeatability of smartphone-based measurement of SVA compared to marker-based 3D motion analysis.
Four licensed clinicians (two physical therapists and two orthotists) measured SVA during gait with a smartphone attached to the anterior or lateral shank surface of unimpaired adults. We compared SVA calculated from the smartphone’s inertial measurement unit to marker-based measurements. Each clinician completed three sessions/day on two days with each participant to assess repeatability.
Average absolute differences in SVA measured with a smartphone versus marker-based 3D motion analysis during gait were 0.67 ± 0.25° and 4.89 ± 0.72°, with anterior or lateral smartphone positions, respectively. The inter- and intra-day repeatability of SVA were within 2° for both smartphone positions.
Smartphone sensors can be used to measure SVA with high accuracy and repeatability during unimpaired gait, providing a widely-available tool for quantitative gait assessments.
Try it out!
The app for monitoring shank-to-vertical angle is available for you to download and use on either Android or iOS smartphone. Please complete THIS SURVEY which will then send you an e-mail with instructions for installation and use. This app is not an FDA approved medical device and should be used appropriately.
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.