Skip to main content

written by Kathleen Clardy for Joint BME Communications


A recently published research study explores the integration of ultrasound with functional electrical stimulation (FES), which is traditionally used to manage drop foot, a condition that results in an unnatural gait to avoid tripping or falling. The research was led by Nitin Sharma, associate professor in the Joint Department of Biomedical Engineering and principal investigator of the Neuromuscular Control and Robotics Lab, and Qiang Zhang, a Ph.D. student at the time the studies were conducted. Zhang is now a postdoctoral research fellow in the Closed Loop Engineering for Advanced Rehabilitation (CLEAR) Core in the joint department.

Drop foot is sometimes seen in people with multiple sclerosis, stroke or some people with incomplete spinal cord injury, which is noted by weakened ankle muscles and may cause the foot to drag while walking. To correct for drop foot, FES is used to apply electrical stimulation across skeletal muscles to provide orthotic-like support at the ankle joint. These electrical impulses mimic the natural electrical currents our bodies perform to produce muscle contractions.

“Current FES systems largely rely only on tilt or movement sensors to control functional electrical stimulation timing to correct ankle function. However, these sensors miss the actual physiological state of the muscle. The use of ultrasound, unlike surface electromyography, will allow direct visualization of muscle state,” stated Sharma. “When combined with algorithms, this feedback improves preview of muscle’s current and future state and facilitates proactive adjustment of the FES parameters.”

An additional study conducted by researchers from the Neuromuscular Control and Robotics Lab examined the use of ultrasound paired with surface electromyography in an “assist as-needed”(AAN) framework for exoskeleton movement. Surface electromyography (sEMG) is a process that measures and records the electrical output of a muscle, which can provide feedback to a device in order to guide its function. The proposed framework paves a foundation for using multimodal biological signals to enhance rehabilitative or assistive robots. 

The results of the study concluded that the AAN control approach using ultrasound paired with sEMG produced higher accuracy of human motion intent, less ankle joint trajectory tracking error and less robotic assistance than the sEMG-based method alone. Overall, the addition of ultrasound improves a rehabilitation exoskeleton’s performance and increases voluntary participation from those wearing the device.

“Future directions include scaling the work to monitor multiple muscles via wearable ultrasound transducers,” stated Sharma. While the study focused on analyzing participants with no disabilities, the results have implications for further focus on those who have weakened muscles due to neurological disorders. “Our proposed research methods lay the foundation for future FES controllers for improving the gait of people with various neurological disorders, including spinal cord injury, hemiparesis due to stroke, and multiple sclerosis,” said Sharma.

The research discussed was funded by an NSF CAREER Award and was published in IEEE. The studies are available for further reading and reference at the following links:

Q. Zhang, K. Lambeth, A. Iyer, Z. Sun and N. Sharma, “Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction,” in IEEE Transactions on Control Systems Technology, 2022, doi: 10.1109/TCST.2022.3207999.

Q. Zhang, K. Lambeth, Z. Sun, A. Dodson, X. Bao, and N. Sharma, “Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton,” in IEEE Transactions on Robotics, doi: 10.1109/TRO.2023.3236958.
Comments are closed.