Stiff muscles are experienced by everyone at some point due to cold weather, falling asleep in an unusual position, or after a rigorous workout. But people with stroke, cerebral palsy, and multiple sclerosis deal with stiff muscles on a daily basis, making everyday functions such as extending an arm very difficult and painful for them.
As there is no precise way to objectively rate muscle stiffness, most of the time these patients receive doses of medication that are either too high or too low.
Recently, an interdisciplinary team of researchers at the University of California San Diego and Rady Children’s Hospital have come up with new wearable sensors and robotics technology to accurately measure muscle stiffness during physical exams. “Our goal is to create a system that could augment existing medical procedures by providing a consistent, objective rating,” said Harinath Garudadri, a research scientist at the university’s Qualcomm Institute and the project’s lead investigator.
Many clinical exams and procedures are very subjective and rely on measurements that are done with a physician’s hands. We often make major medical decisions and diagnoses based on touch and feel. With this technology, we can start to develop objective measurements for subjective processes.
Andrew Skalsky, Director of the Division of Rehabilitation Medicine, Rady Children’s Hospital
The level of muscle stiffness is termed as spasticity, and is normally assessed using a six-point rating scale called the Modified Ashworth Scale. This scale is the existing hospital standard, but it is subjective and frequently yields ratings that differ from one doctor to another. These ratings help prescribe the dose of medication for patients to manage their spasticity. Varying and inaccurate ratings can either cause ineffective treatment or dangerous overdose as a result of doses that are very low.
Patient feedback can also tilt these ratings, Skalsky said. “Sometimes, patients think that they aren’t getting enough medicine and end up being put on a higher dose than they should actually be on. That’s thousands of dollars’ worth of medicine that could potentially be saved.”
Garudadri and Skalsky partnered with neuroscientists and electrical engineers at UC San Diego to build a glove fitted with sensors that is a more reliable tool and will assist doctors to provide objective, consistent, and accurate number ratings when assessing spasticity in patients undergoing treatment.
The device is incorporated into on a standard sports glove that a doctor can wear while holding and moving a patient’s limb. Over 300 pressure sensors are taped onto the palm in order to measure the amount of force needed to move a patient’s limb. A motion sensor taped on the back measures how quick the limb is being moved. The glove is linked to a computer via USB.
Data from all the sensors are sent to the computer, where they are combined, processed and mapped in real time using advanced signal processing algorithms developed by Garudadri’s research group. The computer offers a numerical reading that measures the actual power required to shift a patient’s limb—the more power required, the more severe the patient’s spasticity.
We’re instrumenting the doctor instead of the patients. It’s more convenient for patients to not have to wear all these sensors all over their bodies. It’s also more practical to equip just the doctor when you think about the large patient to doctor ratio, especially in developing nations or rural areas around the world.
Padmaja Jonnalagedda, Electrical Engineering Graduate Student, UC San Diego
The researchers design another robotic device that they call the “mock patient” to act as a control to corroborate their results. The mock patient has an artificial arm that is capable of moving up and down, mimicking the flexing motion of a real patient’s arm. The artificial arm is connected to a rotating disc that can be manually customized to different resistance levels, like bike gears. The arm is fixed with its own set of sensors that measure the power required to overcome the resistance and make it to move. Researchers can fix the resistance, be aware of the amount of power needed to move the arm, and then test whether the glove creates a matching result.
“The mock patient provides a ground truth to verify that what the glove is measuring is indeed a real number,” said Fei Deng, an electrical engineering graduate student who was in charge of developing the mock patient.
In a preliminary study, two physicians trained in spasticity evaluation were asked to test the glove on five different patients with cerebral palsy. Each physician wore the glove while performing a variety of movement tasks, including extending and flexing the patients’ legs and arms. The physicians were asked to give their own spasticity ratings according to the Modified Ashworth Scale, without knowing the glove’s readings. They also were not aware of what spasticity ratings the other was giving.
The research team studied the two results. They discovered that only 27 % of the physicians’ spasticity ratings matched with each other. In comparison, 64 % of the measurements performed by the glove matched with the numbers produced by the mock patient. “This number needs to be higher if we want to deploy our system for use in the hospital, but it shows better consistency than existing spasticity assessments,” Garudadri said.
The multidisciplinary nature of our team is what makes this project so exciting and successful. Experts in signal processing, robotics, printable electronics, neurosciences and medicine came together to transform a subjective process into something that’s objective and could improve patient care and outcomes.
Leanne Chukoskie, Research Scientist, Institute for Neural Computation, UC San Diego.
The researchers say the technology could possibly be applied in other procedures where doctors have to depend on touch and feel to assess a patient's condition: assessing the severity of hip dislocation in infants, monitoring spine health, physical therapy, rehabilitation therapy, and more.
The team is looking for spasticity-trained medical experts to test their system and provide feedback.
The team is also going to focus on improving the system. Tina Ng, one of the electrical engineering professors on the project, is building sensors that are more robust and can be directly printed onto the glove, instead of being taped onto the surface like they are presently. “This will make it easier to create different sizes of gloves,” Ng said.
Michael Yip, an electrical engineering professor and a core member of the Contextual Robotics Institute at UC San Diego, is incorporating haptics, or force feedback, into the new mock patient. “Now, being able to actively push back on the doctor’s arms and replaying real profiles of patients’ spasticity on the simulator will allow doctors to improve their ability to assess and treat patients, and provide data to improve objective metrics from the glove,” Yip said.
Other members of the team are electrical engineering professor Truong Nguyen at UC San Diego and Kyle Douglas, an electrical engineering undergraduate student at UC Santa Barbara.
The sensor-filled glove for measuring muscle stiffness (Credit: UC San Diego)