NEWS
The Future of Parkinson’s Diagnosis
A materials breakthrough unlocks a device that can detect tremors and neurodegenerative diseases with a mere pen stroke.
Written by Cassandra Kelly
HANDWRITING IS A DYNAMIC TASK that combines cognition and fine motion control abilities. It’s also one of the first areas in which someone with a neurodegenerative disease like Parkinson’s might notice symptoms, such as hand tremors or slower movements. Recognizing this, a team at the University of California, Los Angeles has developed a smart pen that converts motion into electrical signals, capable of detecting early signs of movement disorders.
“It’s not about the handwriting result,” said Jun Chen, associate professor of bioengineering and lead author of the project. “But rather about the process and how the patients perform the movements.”
The theory behind the pen is that a patient in the very early stages of Parkinson’s may experience subtle delays in their writing, taking longer to form letters or struggling to maintain consistent pressure. The device captures these motion patterns and translates them into quantifiable digital biomarkers, allowing clinicians to identify early irregularities that might otherwise go unnoticed. Earlier detection can lead to earlier intervention, helping patients seek intervention and maintain a higher quality of life.
Experts across engineering, medicine, and computation came together on the effort, as detailed in “Neural network-assisted personalized handwriting analysis for Parkinson’s disease diagnostics.”
The magnetoelastic diagnostic pen’s design and working mechanism. Source: Jun Chen Research Group
“There’s a lot to discover when we think in an interdisciplinary way,” Chen said. “We brought all our efforts together to make this work come true.”
At the technology’s foundation is a breakthrough the team made in 2021, when they discovered a new phenomenon called magnetoelasticity in soft materials. These materials couple mechanical deformation like stretching, bending, or compressing, with magnetic response.
Traditionally, magnetoelastic effects were observed in rigid metallic alloys such as certain iron-nickel or cobalt-based compounds. When these materials are mechanically stressed, their internal magnetic domains shift, altering the magnetic flux density. While useful in industrial sensors and transducers, these alloys are stiff and brittle, making them impractical for biomedical or wearable applications.
By embedding magnetic particles within a stretchable silicone matrix, the UCLA team engineered a magnetoelastic soft material that preserves magnetic responsiveness while remaining flexible and skin safe.
The ferrofluid ink’s reaction to an increasing magnetic field. Source: Jun Chen Research Group
This material is in the pen’s soft silicone magnetoelastic tip, interlaced with copper wire and connected to an internal chamber filled with ferrofluid ink—a magnetic liquid that responds predictably to motion and vibration. When the pen moves or presses against a surface, mechanical stress within the tip causes its magnetic particles to realign. This shift alters the local magnetic field, which a surrounding copper coil detects and converts into an electrical signal according to Faraday’s law of induction.
On paper, the deformation of the tip captures each stroke. In the air, oscillations in the ferrofluid register hand movements between words. Together, these dual sensing modes provide a continuous, high-resolution record of motion, mapping the subtleties of a person’s neuromotor control in real time.
“Our device is highly sensitive to dynamic body motions, especially the high-frequency movements seen in handwriting,” Chen said.
This mechanical-to-electrical translation is a significant leap forward from traditional handwriting-based assessments, which typically analyze the finished script rather than the act of writing itself. By focusing on motion dynamics like speed, acceleration, and stability, the pen offers a more objective view of motor function, independent of language, literacy, or stylistic variation.
“Our device is highly sensitive to dynamic body motions, especially the high-frequency movements seen in handwriting.”
—Jun Chen, associate professor of bioengineering at the University of California, Los Angeles
Since the mechanical-to-electrical translation requires no external power and the pen can be fabricated with soft, biocompatible materials, it opens doors to a new class of human-centered sensors that could eventually extend to wearable devices, rehabilitation systems, or remote patient monitoring.
To evaluate the device, the researchers partnered with Katy Cross, a neurologist at UCLA’s David Geffen School of Medicine who specializes in movement disorders. Together, they conducted a pilot study with 16 participants, including three with clinically diagnosed Parkinson’s disease. Each subject used the pen to perform standard handwriting tasks while the device captured and transmitted motion data.
The team then used machine-learning models developed in collaboration with UCLA’s computer science department to classify the signals. In early trials, the system achieved about 96 percent accuracy in distinguishing Parkinson’s patients from healthy participants.
Video: Jun Chen Research Group
While promising, Chen noted that this figure reflects a small, controlled dataset. The next phase of research will expand testing to a larger, more diverse participant group that captures handwriting from patients across different stages of disease progression and compares results with other movement disorders such as essential tremor or dystonia.
“It’s very hard to attribute changes in biomarkers directly to one specific disease,” Chen explained. “Just like a smartwatch can detect an abnormal heart rate, it doesn’t mean a person has a cardiac disease.”
The team envisions future iterations that not only detect disease but also track progression over time, providing clinicians with a quantitative measure of treatment efficacy or symptom management. As machine-learning algorithms become more refined, the device could also help establish personalized baselines, detecting deviations before major symptoms appear.
The UCLA team’s work blurs the boundaries between materials science, neuroengineering, and mechanical design. It demonstrates how a familiar tool like the pen can become a window into brain and motor function when equipped with the right materials and physics.
Cassandra Kelly is a technology writer in Columbus, Ohio.
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