Biofeedback is a promising noninvasive strategy to enhance gait training among individuals with cerebral palsy (CP). Commonly, biofeedback systems are designed to guide movement correction using audio, visual, or sensorimotor (i.e., tactile or proprioceptive) cues, each of which has demonstrated measurable success in CP.
Aim: The aim of this study is to evaluate how the modality of biofeedback may influence user response which has significant implications if systems are to be consistently adopted into clinical care.
Method: In this study, we evaluated the extent to which adolescents with CP (7M/1F; 14 [12.5,15.5] years) adapted their gait patterns during treadmill walking (6 min/modality) with audiovisual (AV), sensorimotor (SM), and combined AV + SM biofeedback before and after four acclimation sessions (20 min/session) and at a two-week follow-up. Both biofeedback systems were designed to target plantarflexor activity on the more-affected limb, as these muscles are commonly impaired in CP and impact walking function. SM biofeedback was administered using a resistive ankle exoskeleton and AV biofeedback displayed soleus activity from electromyography recordings during gait. At every visit, we measured the time-course response to each biofeedback modality to understand how the rate and magnitude of gait adaptation differed between modalities and following acclimation.
Results: Participants significantly increased soleus activity from baseline using AV + SM (42.8% [15.1, 59.6]), AV (28.5% [19.2, 58.5]), and SM (10.3% [3.2, 15.2]) biofeedback, but the rate of soleus adaptation was faster using AV + SM biofeedback than either modality alone. Further, SM-only biofeedback produced small initial increases in plantarflexor activity, but these responses were transient within and across sessions (p > 0.11). Following multi-session acclimation and at the two-week follow-up, responses to AV and AV + SM biofeedback were maintained.
Interpretation: This study demonstrated that AV biofeedback was critical to increase plantarflexor engagement during walking, but that combining AV and SM modalities further amplified the rate of gait adaptation. Beyond improving our understanding of how individuals may differentially prioritize distinct forms of afferent information, outcomes from this study may inform the design and selection of biofeedback systems for use in clinical care.
Steele Lab members – Kat, Christina, Nick, and Momona – were invited to present their research about wearable sensors for stroke recovery and device control to the Seattle Young Adult Stroke Survivors (YASS). YASS is a support group for individuals who have experienced a stroke and creates a community to learn, listen, share, and more. Steele lab was one of the first research groups to come and share our work with them.
Our presentation began with background information regarding neurophysiological changes after stroke to provide insight into upper extremity functional impairments – including weakness, loss of dexterity, and abnormal tone. Wearable sensors, such as electromyography (EMG), can provide information regarding muscle function. Many of the listeners were surprised to hear that their own smartphones or watches can act as wearable sensors!
A focus of our research is detecting muscle activity early after stroke using EMG. One member recalled thinking their muscle was firing during their acute recovery but could not see any physical movement. EMG allows us to capture that type of activity and any functional changes throughout recovery, empowering patients and clinicians to track their recovery and adjust their therapy regimen. The crowd was interested in using EMG to evaluate their own muscles, identify which were firing, and guide their rehabilitation.
EMG not only helps us track recovery, but can be paired with consumer technology. Nick demonstrated how using muscle activity from the affected limb can incorporate rehabilitation into daily computer use. EMG signals can simulate pressing keys on a keyboard or moving a mouse cursor, making it easier for people with limited mobility to use technology. YASS members expressed enthusiasm about the increasing commercial availability of such devices so they can buy them and give them a try.
It was a great opportunity to connect with stroke survivors and hear their thoughts on wearable sensors. Thank you to YASS for having us come in and share our research!
In collaboration with University of Texas – Austin, we evaluated a new flexible, gold-based epidermal electrode for sensing muscle activity.
Background: Commercially available electrodes can only provide quality surface electromyography (sEMG) measurements for a limited duration due to user discomfort and signal degradation, but in many applications, collecting sEMG data for a full day or longer is desirable to enhance clinical care. Few studies for long-term sEMG have assessed signal quality of electrodes using clinically relevant tests. The goal of this research was to evaluate flexible, gold-based epidermal sensor system (ESS) electrodes for long-term sEMG recordings.
Methods: We collected sEMG and impedance data from eight subjects from ESS and standard clinical electrodes on upper extremity muscles during maximum voluntary isometric contraction tests, dynamic range of motion tests, the Jebsen Taylor Hand Function Test, and the Box & Block Test. Four additional subjects were recruited to test the stability of ESS signals over four days.
Results: Signals from the ESS and traditional electrodes were strongly correlated across tasks. Measures of signal quality, such as signal-to-noise ratio and signal-to-motion ratio, were also similar for both electrodes.
Conclusions: Over the four-day trial, no significant decrease in signal quality was observed in the ESS electrodes, suggesting that thin, flexible electrodes may provide a robust tool that does not inhibit movement or irritate the skin for long-term measurements of muscle activity in rehabilitation and other applications.
Filtering parameters impact the results from muscle synergy analyses.
Abstract: Muscle synergies calculated from electromyography (EMG) data identify weighted groups of muscles activated together during functional tasks. Research has shown that fewer synergies are required to describe EMG data of individuals with neurologic impairments. When considering potential clinical applications of synergies, understanding how EMG data processing impacts results and clinical interpretation is important. The aim of this study was to evaluate how EMG signal processing impacts synergy outputs during gait. We evaluated the impacts of two common processing steps for synergy analyses: low pass (LP) filtering and unit variance scaling. We evaluated EMG data collected during barefoot walking from five muscles of 113 children with cerebral palsy (CP) and 73 typically-developing (TD) children. We applied LP filters to the EMG data with cutoff frequencies ranging from 4 to 40 Hz (reflecting the range reported in prior synergy research). We also evaluated the impact of normalizing EMG amplitude by unit variance. We found that the total variance accounted for (tVAF) by a given number of synergies was sensitive to LP filter choice and decreased in both TD and CP groups with increasing LP cutoff frequency (e.g., 9.3 percentage points change for one synergy between 4 and 40 Hz). This change in tVAF can alter the number of synergies selected for further analyses. Normalizing tVAF to a z-score (e.g., dynamic motor control index during walking, walk-DMC) reduced sensitivity to LP cutoff. Unit variance scaling caused comparatively small changes in tVAF. Synergy weights and activations were impacted less than tVAF by LP filter choice and unit variance normalization. These results demonstrate that EMG signal processing methods impact outputs of synergy analysis and z-score based measures can assist in reporting and comparing results across studies and clinical centers.
Our lab had a great time sharing our research at the College of Engineering Discovery Days. Our booth was entitled, “The Ultimate Machine” because we think of the human body as a complex system with our brain as a controller/computer and our muscles as our motors. Elementary and middle school students used their neural pathway, from brain to muscle, to control a robot gripper by either relaxing or activating their muscle.