Publications

M Yamagami, KM Steele, SA Burden (2020) “Decoding Intent With Control Theory: Comparing Muscle Versus Manual Interface Performance”

Elijah JournalArticle, Media, News, Presentation, Publications, UbiquitousRehabilitation , , , ,

Journal Article in ACM Conference on Human Factors in Computing Systems (CHI) 2020 Preceedings:

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

Split image of upper body of user holding rod and slider with computer screen

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!

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 , , , ,

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.

Spasticity and net-nondimensionalized (NN) energy consumption for children with cerebral palsy (CP) who underwent a selective dorsal rhizotomy (SDR) and matched peers with CP who did not undergo SDR (control). (a) Baseline spasticity and NN energy consumption were similar between groups. Gray lines show normative values for typically developing (TD) peers from Gillette Children’s Specialty Healthcare. (b) Spasticity and NN energy consumption decreased significantly at follow-up for both groups. The SDR cohort had a significantly greater decrease in spasticity compared to the no-SDR group, but a similar decrease in NN energy consumption. Bars represent distributions for each group including outliers (*).

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 , , , , , ,

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.

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.