Researchers in Italy and Mexico have designed a smart glove that, using sensors, may help to track symptoms and evaluate motor function in people with Parkinson’s disease.
The device’s development is described in the study, “A Smart Glove to Evaluate Parkinson’s Disease by Flexible Piezoelectric and Inertial Sensors,” which was published in Sensors International.
Parkinson’s is marked by disease-specific motor symptoms, including Parkinsonian tremor and rigidity, and by problems with coordination.
Wearable devices allow doctors to track motor changes over time
Currently, the standard for evaluating a person’s motor function and symptom severity is through assessments made by a specialist. But this approach has notable drawbacks — human evaluations are always somewhat subjective, and specialists see patients for a limited, often brief, window of time, so they may not be able to realistically assess how symptoms affect a person’s day-to-day life.
Wearable devices may help to address these limitations by allowing doctors to collect objective information as patients goes about daily life.
“Through wearable devices, the specialist doctor could better understand the disease and all its nuances more effectively than the sporadic controls and self-assessment,” the researchers wrote.
They created a wearable glove aiming to help in symptom monitoring and motor assessment for Parkinson’s. The glove is worn across the forefinger and thumb, with integrated sensors that track the movement of these digits and of the hand more generally; the scientists noted that the device is “discrete, non-invasive, and ready-to-use.”
Data collected by the glove’s sensors are processed using artificial intelligence. More specifically, use is made of neural networks, or computer algorithms designed to process data in a similar way to how the human brain makes sense of information.
Accuracy seen in identifying specific hand movement, resting tremor
As a proof-of-concept test of the device, the researchers evaluated how well it could detect three types of movements: tapping the thumb and forefinger together, and opening and closing the fist — both of which are part of current motor assessments for Parkinson’s — as well as for resting tremor, a common motor symptom.
Seven adults, six with Parkinson’s and one without, took these motor tests, and algorithms identified each of the three types of movement with more than 95% accuracy: 95.12% for finger tapping, 98.39% for hand-fist closure, and 96.62% for resting tremor.
“[The smart glove] is discrete, non-invasive, and ready-to-use, not involving external acquisition and processing sections,” the researchers wrote.
Patients can perform these tests at home, with results “sent, processed, and stored in a digital record on a cloud platform, allowing neurologists to monitor the patient’s course easily,” they added.
“It is clear that the [neural network]-based models perform excellently in evaluating the execution of the three exercises,” the researchers wrote, noting that they consider the device ready to be explored in tracking Parkinson’s symptoms and assessing motor abilities.
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