Newswise — COLUMBUS, Ohio – A new smart insole system that monitors how people walk in real time could help users improve posture and provide early warnings for conditions from plantar fasciitis to Parkinson’s disease.

Constructed using 22 small pressure sensors and fueled by small solar panels on the tops of shoes, the system offers real-time health tracking based on how a person walks, a biomechanical process that is as unique as a human fingerprint. 

This complex personal health data can then be transmitted via Bluetooth to a smartphone for quick and detailed analysis, said Jinghua Li, co-author of the study and an assistant professor of materials science and engineering at The Ohio State University. 

“Our bodies carry lots of useful information that we’re not even aware of,” said Li. “These statuses also change over time, so it’s our goal to use electronics to extract and decode those signals to encourage better self health care checks.”

It’s estimated that at least 7% of Americans suffer from ambulatory difficulties, activities that include walking, running or climbing stairs. While efforts to manufacture a wearable insole-based pressure system have risen in popularity in recent years, many previous prototypes were met with low energy limitations and unstable performances. 

To overcome the challenges of their precursors, Li and Qi Wang, the lead author of the study and a current PhD student in materials science and engineering at Ohio State, sought to ensure that their wearable is durable, has a high degree of precision when collecting and analyzing data, and can provide consistent and reliable power, said Li. 

“Our device is innovative in terms of high resolution, spatial sensing, self-powering capability, and its ability to combine with machine learning algorithms,” she said. “So we feel like this research can go further based on the pioneering successes of this field.”

The study was recently published in the journal Science Advances.

This team’s system is also made unique through its use of AI. Using an advanced machine learning model, the wearable can recognize eight different motion states, including static ones like sitting and standing to more dynamic movements such as running and squatting. 

Additionally, since the materials the insoles are made of are flexible and safe, the device, much like a smartwatch, is low-risk and safe for continuous use. For instance, after the solar cells convert sunlight to energy, that power is stored in tiny lithium batteries that don’t harm the user or affect daily activities.

Because of the distribution of sensors from toe to heel, the researchers could see how the pressure on parts of the foot is different in activities such as walking versus running.  

During walking, pressure is applied sequentially from the heel to the toes, whereas during running, almost all sensors are subjected to pressure simultaneously. In addition, during walking, the pressure application time accounts for about half of the total time, while during running, it accounts for only about a quarter.

In health care, the smart insoles could support gait analysis to detect early abnormalities associated with foot pressure-related conditions (such as diabetic foot ulcers), musculoskeletal disorders (such as plantar fasciitis) and neurological conditions (such as Parkinson’s disease).

The new system also used machine learning to learn and classify different types of motion. That offers opportunities for personalized health management, including real-time posture correction, injury prevention and rehabilitation monitoring. Customized fitness training may also be a future use, the researchers said.

According to the study, these smart insoles showed no notable deterioration in performance after 180,000 cycles of compression and decompression, showing their long-term durability. 

“The interface is flexible and quite thin, so even during repetitive deformation, it can remain functional,” said Li. “The combination of the software and hardware means it isn’t as limited.”

Researchers expect the technology will likely be available commercially within the next three to five years. Next steps to advance the work will be aimed at improving the system’s gesture recognition abilities, which, according to Li, will likely be helped with further testing on more diverse populations. 

“We have so many variations among individuals, so demonstrating and training these fantastic capabilities on different populations is something we need to give further attention to,” said Li.

Computational model reveals details on how certain synaptic proteins organize into unique structures that are vital for learning and memory

 

Complex protein interactions at synapses are essential for memory formation in our brains, but the mechanisms behind these processes remain poorly understood. Now, researchers from Japan have developed a computational model revealing new insights into the unique droplet-inside-droplet structures that memory-related proteins form at synapses. They discovered that the shape characteristics of a memory-related protein are crucial for the formation of these structures, which could shed light on the nature of various neurological disorders. 

Our brain’s remarkable ability to form and store memories has long fascinated scientists, yet most of the microscopic mechanisms behind memory and learning processes remain a mystery. Recent research points to the importance of biochemical reactions occurring at postsynaptic densities—specialized areas where neurons connect and communicate. These tiny junctions between brain cells are now thought to be crucial sites where proteins need to organize in specific ways to facilitate learning and memory formation. 

More specifically, a 2021 study revealed that memory-related proteins can bind together to form droplet-like structures at postsynaptic densities. What makes these structures particularly intriguing is their unique “droplet-inside-droplet” organization, which scientists believe may be fundamental to how our brains create lasting memories. However, understanding exactly how and why such complex protein arrangements form has remained a significant challenge in neuroscience. 

Against this backdrop, a research team led by Researcher Vikas Pandey from the International Center for Brain Science (ICBS), Fujita Health University, Japan, has developed an innovative computational model that reproduces these intricate protein structures. Their paper, published online in Cell Reports on April 07, 2025, explores the mechanisms behind the formation of multilayered protein condensates. The study was co-authored by Dr. Tomohisa Hosokawa and Dr. Yasunori Hayashi from the Department of Pharmacology, Kyoto University Graduate School of Medicine, and Dr. Hidetoshi Urakubo from ICBS, Fujita Health University. 

The researchers focused on four proteins found at synapses, with special attention to Ca²⁺/calmodulin-dependent protein kinase II (CaMKII)—a protein particularly abundant in postsynaptic densities. Using computational modeling techniques, they simulated how these proteins interact and organize themselves under various conditions. Their model successfully reproduced the formation of the above-mentioned “droplet-inside-droplet” structures observed in earlier experiments. Through simulations and detailed analyses of the physical forces and chemical interactions involved, the research team shed light on a process called liquid-liquid phase separation (LLPS); it involves proteins spontaneously organizing into condensates without membranes that sometimes resemble the organelles found inside cells.

Crucially, the researchers found that the distinctive “droplet-inside-droplet” structure appears as a result of competitive binding between the proteins and is significantly influenced by the shape of CaMKII, specifically its high valency (number of binding sites) and short linker length. These shape-related properties of CaMKII result in low surface tension and slow diffusion, allowing the protein condensates to remain stable for extended periods. This stability enables the sustained activation of downstream signaling pathways necessary for synaptic plasticity, which is the cellular basis for learning and memory. “Our results revealed new structure–function relationships for CaMKII as a synaptic memory unit. This is the first systematic and mechanistic study investigating the divergent structure of protein-regulated multiphase condensates,” highlights Dr. Pandey. 

These findings could pave the way toward a better understanding of the possible mechanisms of memory formation in humans. However, the long-term implications of this research extend well beyond basic neuroscience. 

Defects in synapse formation have been associated with numerous neurological and mental health conditions, including schizophrenia, autism spectrum disorders, Down syndrome, and Rett syndrome. “Overall, the computational model developed in this study could serve as an important platform for investigating these conditions, potentially leading to new diagnostic tools and therapeutic approaches,” explains Dr. Pandey. 

Let us hope scientists continue to unravel the mysteries of how memories form at the molecular level, leading us to a more thorough comprehension of one of the brain’s most fundamental and complex functions.