Gait Archives - Steele Lab - University of Washington

Amina El-Zatmah presents at the CNT 2023 Summer Undergraduate Research Symposium

Katie Landwehr News, Presentation September 1, 2023September 12, 2023Cerebral Palsy, Exoskeleton, Gait

This summer, the Steele Lab hosted undergraduate researcher, Amina El-Zatmah, from Santa Monica College. She finished up her 10-week summer Research Experience for Undergraduate (REU) by presenting at the 2023 Summer Undergraduate Research Symposium with the Center for Neurotechnology (CNT).

Amina gave a podium and poster presentation titled “Take A Step: The Effects of Transcutaneous Spinal Cord Stimulation and Exoskeleton Use on Step Length for Children with Cerebral Palsy“.

Amina was supported through mentorship from Charlotte Caskey, Siddhi Shrivastav, Chet Moritz, and Kat Steele.

Way to go, Amina!

MR Ebers, MC Rosenberg, JN Kutz, KM Steele (2023) “A machine learning approach to quantify individual gait responses to ankle exoskeletons”

Yusuke Maruo JournalArticle, Publications February 9, 2023February 9, 2023Exoskeleton, Gait, Modeling

Journal Article in bioRxiv:

Discrepancy modeling is a unique and innovative tool that complements current biomechanical modeling approaches and may accelerate the discovery of individual-specific mechanisms driving responses to exoskeletons, other assistive devices, and clinical interventions.

Aim: This study aims to leverage a neural network-based discrepancy modeling framework to quantify complex changes in gait in response to passive ankle exoskeletons in non-disabled adults. It hypothesized that (i) the Nominal model would predict Exo kinematics and EMG less accurately than for the Nominal condition, and (ii) the Augmented (Nominal+Discrepancy) model would capture greater variance in Exo kinematics and EMG than the Nominal model.

Method: This study analyzed gait data for 12 non-disabled adults during treadmill walking in bilateral passive ankle exoskeletons at a self-selected speed, results of which were used in participant-specific continuous-time neural network with discrepancy models to predict gait responses.

Results: Discrepancy modeling successfully quantified individuals’ exoskeleton responses without requiring knowledge about physiological structure or motor control. However, additional measurement modalities and/or improved resolution are needed to characterize Exo gait, as the discrepancy may not comprehensively capture response due to unexplained variance in Exo gait.

Interpretation: These techniques can be used to accelerate the discovery of individual-specific mechanisms driving exoskeleton responses, thus enabling personalized rehabilitation.

MC Rosenberg, BS Banjanin, SA Burden, KM Steele (2020) “Predicting walking response to ankle exoskeleton using data driven models”

Elijah JournalArticle, News October 20, 2020October 12, 2021Exoskeleton, Floquent theory, Gait, generalizable models, Modeling, Orthoses, Walking

Journal Article in The Royal Society:

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.

NL Zaino, KM Steele, JM Donelan, MH Schwartz (2020) “Energy consumption does not change after selective dorsal rhizotomy in children with spastic cerebral palsy” Developmental Medicine & Child Neurology

Nicole JournalArticle, News, Publications April 23, 2020October 12, 2021Cerebral Palsy, energy consumption, Gait, selective dorsal rhizotomy, Spasticity

Journal Article in Developmental Medicine & Child Neurology:

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.

B Nguyen, N Baicoianu, D Howell, KM Peters, KM Steele (2020) “Accuracy and repeatability of smartphone sensors for measuring shank-to-vertical angle” Prosthetics & Orthotics International

Dr. Steele HuskySTEPS, JournalArticle, Orthoses, UbiquitousRehabilitation March 9, 2020October 12, 2021accuracy, AFO, app, Gait, Orthoses, Repeatability, wearable sensors

Journal Article in Prosthetics & Orthotics International

Example of how the smartphone app was used for this research. The top images show a black smartphone attached with a running arm band to the side or front of the shank - the two positions tested in this research. The middle figure shows the placement of the reflective markers for 3D motion analysis to evaluate the accuracy of the smartphone measurements. Markers were placed on the lateral epicondyle of the knee, lateral maleolus of the ankle, tibial tuberosity, and the distal tibia. Blacklight was used to mark the position of each marker and hide the position from the clinicians. The bottom panel shows screenshots from the app. The first screen is used to align the device and has arrows at the top and bottom that remind the clinician which anatomical landmarks should be used to align the device while displaying the shank-to-vertical angle in real time. The second screenshot shows an example of the calculated shank-to-vertical angle while someone was walking. The average is shown with a bold black line, with all other trials shown in blue and excluded trials (e.g., when someone was stopping or turning) that deviated more than one standard deviation from other trials are shown in red. There is also text below the graph that provides summary measures, like shank-to-vertical angle in mid stand and cadence (steps/min). The results can be exported as a picture or sent via e-mail using the app. — A) Smartphone positioning on the front or side of the shank. B) Reflective markers on the the tibial tuberosity (TT) – distal tibia (DT) and lateral epicondyle (LE) – lateral malleolus (LM) were used to compare the accuracy of the smartphone to traditional motion capture. UV markings were used to keep placement of these markers constant while blinding clinicians. C) Sample screenshots of the mobile application, including the set-up screen and results automatically produced after a walking trial.

Background

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.

Objectives

Assess accuracy and repeatability of smartphone-based measurement of SVA compared to marker-based 3D motion analysis.

Method

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.

Results

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.

Conclusions

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.

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