Date:24/04/17
When it comes to assessing the chronic muscle stiffness of patients with conditions such as cerebral palsy and multiple sclerosis, doctors pretty much just go by feel. They bend the affected limbs back and forth, then assign them a rating on a six-point scale. The problem is, the system is very subjective – different doctors could assign different ratings to the same patient, resulting in either more or less medication than is actually needed. That's why a team from the University of California San Diego and Rady Children's Hospital are developing a glove that measures muscle stiffness objectively.
Currently in prototype form, the glove has over 300 pressure sensors attached to the front, along with an accelerometer on the back. It's hard-wired to a computer via a USB cable.
As the doctor moves a patient's limb, the pressure sensors measure how much force is being applied. At the same time, the accelerometer measures how fast that limb is being moved. A program on the computer processes that data to come up with a spasticity (muscle stiffness) rating, that will be the same regardless of who is using the glove.
In order to test the system, the scientists built a bendable mechanical arm that could be set to different levels of resistance – sensors within it show how much force is required to move it, at different settings. The idea is that if the glove is working properly, the readings from its sensors will match those of the arm. In tests performed so far, the numbers agreed 64 percent of the time.
"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," says lead scientist Harinath Garudadri.
The team is now working on boosting the accuracy of the glove, and plan on printing the sensors directly onto the final version, instead of just taping them on as is currently the case. It is believed that the technology could also be used in applications such as physical therapy, the checking of spine health, or the assessment of hip dislocation in infants.
Smart glove measures muscle stiffness
When it comes to assessing the chronic muscle stiffness of patients with conditions such as cerebral palsy and multiple sclerosis, doctors pretty much just go by feel. They bend the affected limbs back and forth, then assign them a rating on a six-point scale. The problem is, the system is very subjective – different doctors could assign different ratings to the same patient, resulting in either more or less medication than is actually needed. That's why a team from the University of California San Diego and Rady Children's Hospital are developing a glove that measures muscle stiffness objectively.Currently in prototype form, the glove has over 300 pressure sensors attached to the front, along with an accelerometer on the back. It's hard-wired to a computer via a USB cable.
As the doctor moves a patient's limb, the pressure sensors measure how much force is being applied. At the same time, the accelerometer measures how fast that limb is being moved. A program on the computer processes that data to come up with a spasticity (muscle stiffness) rating, that will be the same regardless of who is using the glove.
In order to test the system, the scientists built a bendable mechanical arm that could be set to different levels of resistance – sensors within it show how much force is required to move it, at different settings. The idea is that if the glove is working properly, the readings from its sensors will match those of the arm. In tests performed so far, the numbers agreed 64 percent of the time.
"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," says lead scientist Harinath Garudadri.
The team is now working on boosting the accuracy of the glove, and plan on printing the sensors directly onto the final version, instead of just taping them on as is currently the case. It is believed that the technology could also be used in applications such as physical therapy, the checking of spine health, or the assessment of hip dislocation in infants.
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