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.
MESA Day brought high school and middle school students from the Seattle area to North Seattle College for a morning of competitions and STEM activities, put on by volunteers in the community. Gaurav and Michael developed an activity using electromyography (EMG) sensors to teach students about neural control of muscles, how we quantify muscle activity, and how we can use that knowledge to improve quality of life. Small groups selected a “test subject” and hooked up an EMG sensor to a muscle of their choice. They then picked tasks to perform, generated corresponding hypothetical muscle activation curves, and experimentally tested their hypotheses. The attendees were impressive. Students, ages 13-18, surprised our PhD students with their curiosity, knowledge, and ability to generate hypotheses and explain their results. Overall, the students seemed to enjoy the event and we hope that we helped them think about how understanding the mechanisms of the human body can be used to improve lives.