A new smart template system that monitors how people walk in real time could help users improve posture and provide early warnings of conditions from plantar fasciitis to Parkinson’s disease.
Built with 22 small pressure sensors and fed by small solar panels at the top of the shoes, the system offers real -time health monitoring depending on how a person walks, a biomechanical process that is as unique as a human fingerprint.
These complex personal health data can be transmitted through Bluetooth to a smartphone for a quick and detailed analysis, said Jinghua Li, co -author of the study and assistant professor of Science and Materials Engineering at Ohio State University.
“Our bodies have a lot of useful information that we are not even aware,” Li said. “These states also change over time, so it is our goal to use electronics to extract and decode those signals to encourage better medical care controls.”
It is estimated that at least 7% of Americans suffer from outpatient difficulties, activities that include walking, running or climbing stairs. While efforts to make a pressure system based on portable dishes have increased in popularity in recent years, many previous prototypes were fulfilled with low energy limitations and unstable actions.
To overcome the challenges of their precursors, LI and Qi Wang, the main author of the study and a current doctoral student in Materials Science and Engineering in the state of Ohio, sought to guarantee that its laptop is durable, has a high degree of precision when collecting and analyzing data, and can provide consistent and reliable power, Li said.
“Our device is innovative in terms of high resolution, spatial detection, autopoera capacity and its ability to combine with automatic learning algorithms,” he said. “So we feel that this research can go further depending on the pioneer successes of this field.”
The study was recently published in the magazine Scientific advances.
The system of this equipment is also unique through its use of AI. Using an advanced automatic learning model, the laptop can recognize eight different states, including static such as sitting and standing for more dynamic movements, such as running and squatting.
In addition, since the materials of which the templates are made are flexible and safe, the device, as is an smart watch, is low risk and safe for continuous use. For example, after solar cells turn sunlight into energy, that energy is stored into small lithium batteries that do not harm the user or affect daily activities.
Due to the distribution of sensors from the foot to the heel, the researchers could see how the pressure on parts of the foot is different in activities such as walking versus running.
During the walk, the pressure is applied sequentially from the heel to the fingers of the feet, while during the race, almost all the sensors are subject to pressure simultaneously. In addition, during the walk, the pressure application time represents approximately half of the total time, while during the execution, it represents only approximately a quarter.
In medical care, smart templates could support the analysis of the march to detect early anomalies associated with conditions related to feet pressure (such as diabetic foot ulcers), musculoskeletal disorders (such as plantar fasciitis) and neurological conditions (such as Parkinson’s disease).
The new system also used automatic learning to learn and classify different types of movement. This offers opportunities for personalized health management, including real -time posture correction, injury prevention and rehabilitation monitoring. Personalized fitness training can also be a future use, researchers said.
According to the study, these smart templates did not show a remarkable deterioration in performance after 180,000 compression and decompression cycles, showing their long -term durability.
“The interface is flexible and quite thin, so even during repetitive deformation, it can remain functional,” Li said. “The combination of software and hardware means that it is not so limited.”
Researchers expect technology to probably be commercially available within the next three to five years. The next steps to advance at work will be aimed at improving system’s gesture recognition skills, which, according to LI, will probably receive more evidence in more diverse populations.
“We have so many variations between people, so demonstrating and training these fantastic abilities in different populations is something to pay more attention to,” Li said.
Other co -authors include Hui Guan, Chen Wang, Peiming Lei, Hongwei Sheng, Huasheng Bi, Jinkun Hu, Chenhui Guo, Yichuan Mao, Jiao Yuan, Mingjiao Shao, Zhiwen Jin and Wei LAN of the Lanzhou University in China.