JournalArticle

AM Spomer, BC Conner, MH Schwartz, ZF Lerner, KM Steele (2024) “Multi-session adaptation to audiovisual and sensorimotor biofeedback is heterogeneous among adolescents with cerebral palsy”

Katie Landwehr JournalArticle, MotorControl, News, Publications , , , , ,

Journal Article in PLoS ONE

There is growing interest in the use of biofeedback-augmented gait training in cerebral palsy (CP). Audiovisual, sensorimotor, and immersive biofeedback paradigms are commonly used to elicit short-term gait improvements; however, outcomes remain variable. Because biofeedback training requires that individuals have the capacity to both adapt their gait in response to feedback and retain improvements across sessions, changes in either capacity may affect outcomes. Yet, neither has been explored extensively in CP.

Experimental protocol used to evaluate multi-session adaptation to multimodal biofeedback. Participants completed a four-day protocol using combined audiovisual and sensorimotor biofeedback. Audiovisual biofeedback on soleus activity was provided unilaterally on the more-affected limb whereas sensorimotor biofeedback was administered bilaterally using a resistive ankle exoskeleton. Each session was separated into baseline (1 minute), biofeedback (2, 10-minute bouts), and washout (1 minute) phases. The nominal torque value of the ankle exoskeleton was set at 0.1 Nm/kg during the first bout of the first session and incrementally adjusted by 0.025 Nm/kg over the subsequent bouts, according to the schedule shown. Overground walking data were collected pre- and post-intervention. A licensed physical therapist also performed a full physical examination at the pre-intervention session. Motion capture data were collected during at the pre- and post-intervention sessions and electromyography (EMG) data were collected bilaterally from the vastus lateralis, semitendinosus, soleus, and tibialis anterior across all sessions.Aim: The aim of this study was to evaluate the extent to which individuals with CP adapt gait and retain improvements during multi-session practice with a multimodal biofeedback paradigm, designed to promote plantarflexor recruitment. Secondarily, we compared overground walking performance before and after biofeedback sessions to understand if any observed in-session improvements were transferred. 

Methods: In this study, we evaluated the extent to which adolescents with CP (7M/1F; 14 years (12.5,15.26)) could adapt gait and retain improvements across four, 20-minute sessions using combined audiovisual and sensorimotor biofeedback. Both systems were designed to target plantarflexor activity. Audiovisual biofeedback displayed real-time soleus activity and sensorimotor biofeedback was provided using a bilateral resistive ankle exoskeleton. We quantified the time-course of change in muscle activity within and across sessions and overground walking function before and after the four sessions.

Results: All individuals were able to significantly increase soleus activity from baseline using multimodal biofeedback (p < 0.031) but demonstrated heterogeneous adaptation strategies. In-session soleus adaptation had a moderate positive correlation with short-term retention of the adapted gait patterns (0.40 ≤ ρ ≤ 0.81), but generally weak correlations with baseline walking function (GMFCS Level) and motor control complexity (ρ ≤ 0.43). The latter indicates that adaptation capacity may be a critical and unique metric underlying response to biofeedback. Notably, in-session gains did not correspond to significant improvements in overground walking function (p > 0.11).

Interpretation: This work suggests that individuals with CP have the capacity to adapt their gait using biofeedback, but responses are highly variable. Characterizing the factors driving adaptation to biofeedback may be a promising avenue to understand the heterogeneity of existing biofeedback training outcomes and inform future system optimization for integration into clinical care.

 

CR DeVol, SR Shrivastav, AM Spomer, KF Bjornson, D Roge, CT Moritz, KM Steele (2024) “Effects of interval treadmill training on spatiotemporal parameters in children with cerebral palsy: A machine learning approach”

Katie Landwehr JournalArticle, Publications , , , ,

Journal Article in Journal of Biomechanics

Quantifying individualized rehabilitation responses and optimizing therapy for each person is challenging. For interventions like treadmill training, there are multiple parameters, such as speed or incline, that can be adjusted throughout sessions.

A) Pre-post effect of SBLTT on step length for the more affected side. B) BART results quantify direct effects of SBLTT on step length. Accumulated Local Effects (ALE) plots for each input variable show the effect of that variable on step length including session number, treadmill speed (Froude number), time within session, side, and treadmill incline. The size of the data point on each ALE plot depicts the relative number of data points in each bin. C) BART model fit (R2) for each participant. D) Direct effects of each input variable on the response variable, step length, calculated from the change in the ALE plots in B).Aim: This study evaluates if causal modeling and Bayesian Additive Regression Trees (BART) can be used to accurately track the direct effects of treadmill training on gait.

Methods: We developed a Directed Acyclic Graph (DAG) to specify the assumed relationship between training input parameters and spatiotemporal outcomes during Short Burst Locomotor Treadmill Training (SBLTT), a therapy designed specifically for children with cerebral palsy (CP). We evaluated outcomes after 24 sessions of SBLTT for simulated datasets of 150 virtual participants and experimental data from four children with CP, ages 4–13 years old. Individual BART models were created from treadmill data of each step.

Results: Simulated datasets demonstrated that BART could accurately identify specified responses to training, including strong correlations for step length progression (R2 = 0.73) and plateaus (R2 = 0.87). Model fit was stronger for participants with less step-to-step variability but did not impact model accuracy. For experimental data, participants’ step lengths increased by 26 ± 13 % after 24 sessions. Using BART to control for speed or incline, we found that step length increased for three participants (direct effect: 13.5 ± 4.5 %), while one participant decreased step length (−11.6 %). SBLTT had minimal effects on step length asymmetry and step width.

Interpretation: Tools such as BART can leverage step-by-step data collected during training for researchers and clinicians to monitor progression, optimize rehabilitation protocols, and inform the causal mechanisms driving individual responses.

KA Ingraham, HA Feldner, KM Steele (2024) “An Instrumented ‘Explorer Mini’ for Quantitative Analysis of Toddlers Using Powered Mobility for Exploratory, Mobile, and Digital Play”

Katie Landwehr JournalArticle, News, Presentation, Publications , , , ,

Journal Article in the 10th IEEE RAS EMBS Intl. Conference on Biomedical Robotics and Biomechatronics (BioRob).

For toddlers with disabilities, assistive technologies can enable developmentally appropriate exploration, play, and participation, but little is known about how children interact with accessible interfaces, such as joysticks.

The instrumented explorer mini measures joystick position, wheel rotations, and bodyweight loading at 100 Hz. Representative raw data collected from the device are shown here for 100 seconds.Aim: The Permobil Explorer Mini is currently the only commercially available, FDA-cleared pediatric powered mobility device in the United States designed for children ages 12–36 months. In this paper, we present an instrumented Explorer Mini that enables us to quantitatively analyze how young children with disabilities learn to use and interact with joystick-based technology.

Methods: We discuss preliminary results from two studies conducted with two toddlers with motor disabilities using the instrumented Explorer Mini in different contexts: 1) during exploratory mobile play (i.e., driving) and 2) during interactive digital play (i.e., playing a simple computer game).

Results: In the first study, we found that, for a given 15–20 minute play session, participants drove between 11.3 and 65.6 m, and engaged with the joystick between 53 and 165 times. In the second study, we found that children could use the joystick to play a simple cause-and-effect computer game, but that they disproportionately used the ‘forward’ direction of the joystick, regardless of the direction of the displayed target.

Interpretation: The novel experimental platform, research framework, and preliminary data presented in this paper lay the foundation to study how children with disabilities learn to use and interact with joystick-based assistive technologies. This knowledge is critical to inform the design and advancement of developmentally appropriate technologies that equitably support toddlers in exploration, mobility, and play.

NL Zaino, KA Ingraham, ME Hoffman, HA Feldner, KM Steele (2024) “Quantifying toddler exploration in different postures with powered mobility”

Katie Landwehr JournalArticle, News, Publications , , , ,

Journal Article in Assistive Technology

Access to powered mobility can support play and development for toddlers with disabilities. Using powered mobility in a standing posture has been theorized to support development of muscle coordination, balance, head and trunk stability, and transition to ambulation.

3 graphs showing the number of joystick activations, longest joystick activation, and loading through the base over the 13% bodyweight threshold in each play session for each participant for both SIT (left, open) and STAND (right, filled).

Aim: The purpose of this study was to quantify and characterize joystick control, bodyweight support, and muscle activity while using the Permobil Explorer Mini in seated and supported standing postures.

Methods: Nine children with mobility disabilities participated in four visits where they completed two, 15–20 minute play sessions, one in each posture, with a break between.

Results: We found that all toddlers engaged with the joystick in both postures, with individual differences in favored directions and control patterns. Participants had similar loading through their feet in both postures, but had slightly higher muscle activity in standing, especially while driving.

Interpretation: These results demonstrate that young children with disabilities quickly engage with joystick-based powered mobility in seated and standing postures, with important individual differences that can inform future design of devices and interventions to support play and development.

MR Ebers, JP Williams, KM Steele, JN Kutz (2024) “Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction,”

Katie Landwehr JournalArticle, Publications , ,

Journal Article in IEEE Access

Sensing is a fundamental task for the monitoring, forecasting, and control of complex systems. In many applications, a limited number of sensors are available and must move with the dynamics. Currently, optimal path planning, like Kalman filter estimation, is required to enable sparse mobile sensing for state estimation. However, we show that arbitrary mobile sensor trajectories can be used. By adapting the Shallow REcurrent Decoder (SHRED) network with mobile sensors, their time-history can be used to encode global information of the measured high-dimensional state space.

Summary figure of a shallow recurrent decoder network (SHRED) leveraging mobile sensors to reconstruct full state-space estimates from sparse dynamical trajectories. (Left) Sensor trajectory history encodes global information of the spatio-temporal dynamics of the sparsely measured system. In this work, we evaluate three challenging datasets, including forced isotropic turbulence, global sea-surface temperature, and human biomechanics. (Middle) The mobile SHRED architecture can (i) embed the multiscale physics of a system into a compact and low-dimensional latent space, and (ii) provide a mapping from the sparse mobile sensors to a full state estimate. (Right) The high-dimensional and complex system states can be reconstructed, provided training data for the dynamical trajectory of the sensor(s) is available.Aim: We leverage sparse mobile sensor trajectories for full-state estimation, agnostic to sensor path.

Methods: Using modern deep learning architectures, we show that a sequence-to-vector model, such as an LSTM (long, short-term memory) network, with a decoder network, dynamic trajectory information can be mapped to full state-space estimates.

Results: We demonstrate that by leveraging mobile sensor trajectories with shallow recurrent decoder networks, we can train the network (i) to accurately reconstruct the full state space using arbitrary dynamical trajectories of the sensors, (ii) the architecture reduces the variance of the mean-squared error of the reconstruction error in comparison with immobile sensors, and (iii) the architecture also allows for rapid generalization (parameterization of dynamics) for data outside the training set. Moreover, the path of the sensor can be chosen arbitrarily, provided training data for the spatial trajectory of the sensor is available.

Interpretation: The time-history of mobile sensors can be used to encode global information of the measured high-dimensional state space.