12 Jun 2017 |
World innovation news |
Sensors in the Palm of the Hand Help Measure Spasticity
People suffering from a stroke or multiple sclerosis have to cope with muscle rigidity, which makes everyday tasks such as extending the arm extremely difficult and painful. And since there is no effective way to objectively assess muscle rigidity, these patients often receive drug doses that are either too low or too high. An interdisciplinary team of researchers at the University of California San Diego and Rady Children’s Hospital has developed new technologythat could help in accurately measuring muscle rigidity during physical examinations.
Subjective Assessment of Spasticity
Many clinical examinations are based on diagnoses through touch. Some examinations lead to important medical decisions although they are based on subjective assessments. This technology will make it possible to obtain objective measurements in this type of examination.
The level of muscle stiffness, known as spasticity, is usually assessed using a six-point scale called the Modified Ashworth scale. This scale, commonly used by medical organizations and based on which drug doses to relieve spasticity are prescribed, often leads to assessments that vary from one physician to another. Inconsistent or inaccurate assessments can lead to dangerous overdoses, or ineffective treatments due to doses that are too low. Patient feedback can also distort these assessments, which can result in thousands of dollars wasted on wrongly dosed drugs.
Sensor-filled Gloves and Robotics Technology
The researchers developed a technology using sensors that will allow physicians to obtain objective, accurate and consistent measurements when assessing spasticity in patients. It is a regular glove that a physician wears when moving affected limbs during auscultation. Three hundred pressure sensors are placed on the palm of the glove to measure the amount of force required to move a patient’s limb. A motion sensor embedded on the back of the glove measures the speed at which the limb is being moved.
Data from the sensors is transmitted to a computer where it is integrated, processed and mapped in real time using advanced signal processing algorithms developed by the University’s research group. The computer calculates the actual power required to move a patient’s limb. The greater the power needed, the more severe will be the patient’s spasticity. This technology will help equip physicians while avoiding the need for patients to wear a number of sensors on their entire body.
The researchers designed another robotic testing innovation, dubbed the “mock patient,” to monitor and validate their results. It is an artificial arm, equipped with sensors, which can be moved up and down, simulating the flexing motion of an actual patient’s arm. The artificial arm is connected to a rotating disc that can be manually adjusted to different levels of resistance, similar to bicycle gears. Researchers can adjust the resistance exerted by the mock patient, experience the amount of power required to move the arm and validate whether the glove produces a similar result.
In a preliminary test, two physicians trained in spasticity evaluations tested the glove by examining five different patients with cerebral palsy. Each physician evaluated different types of movement, including flexion and extension of arms and legs. The physicians also provided their own spasticity assessments, using the Modified Ashworth scale, which were then compared to the results obtained with the glove. Also, they were not informed of the assessments made by their colleague. After tests and comparisons, the team found that only 27% of the physicians’ assessments were identical. On the other hand, 64% of the measurements made with the glove were equal to the values generated by the artificial arm. Although highly satisfactory, these results have prompted the researchers to further optimize their technology.
Indeed, they will improve it so that it can be used in other types of exams where physicians must make diagnoses through touch to evaluate a patient’s condition: monitoring spine health, assessing the severity of hip dislocation in infants, rehabilitation therapy, physical therapy, etc.
The researchers are continuing their work on this technology by developing more robust sensors that can be directly printed on the glove. They are also working on the artificial arm so that it can actively push back against the physician’s arm and replay real spasticity situations in order to improve evaluation abilities and provide data, thus improving the measurements provided by the glove.
The study entitled “An Instrumented Glove for Improving Spasticity Assessment ” was conducted by Jonnalagedda, Saisri Padmaja, Fei Deng, Kyle Douglas, Leanne Chukoskie, Michael Yip, Tse Nga Ng, Truong Nguyen, Andrew Skalsky, and Harinath Garudadri. It was presented in November 2016 at the “Healthcare Innovations and Point-of-Care Technologies” conference in Cancun.