‘Smart insole’ can detect poor running gaits
Image credit: Dreamstime
Engineers have developed an AI-powered insole which can turn any shoe into a gait-analysis device, allowing for runners to monitor their technique.
The team, which is based at Stevens Institute Of Technology, said that the insole could also benefit clinical researchers by providing a way to precisely measure walking function in patients with movement disorders or musculoskeletal injuries.
“From a practical standpoint, that’s invaluable,” said lead author Professor Damiano Zanotto. “We’re now able to accurately analyse a person’s gait in real time, in real-world environments.”
Taking a single step is intuitive, but capturing reliable information about gait in real-life environments remains a major challenge for researchers.
Many gait-analysis technologies in use today - such as camera-based motion-capture systems and force plates - are expensive and can only be used inside laboratories, meaning they offer few insights into how people walk in the real world.
In their work, Zanotto and his team show that their smart insole can deliver real-time data on the length, speed, and power of a wearer’s stride with better accuracy than existing foot-worn technologies and at a fraction of the cost of traditional laboratory equipment.
The insole uses accelerometers and gyroscopes to monitor its own movement and orientation, and an array of force sensors to detect foot plantar pressure, allowing it to capture 500 readings per second. These reading are fed into a machine learning algorithm capable of rapidly extracting gait parameters accurate to within two per cent.
The team states that this is a significant improvement over other AI gait-analysis tools, which are computationally intensive and require data to be recorded for later analysis. Their system is far more efficient, allowing it to be incorporated into a microcontroller capable of delivering real-time gait analysis.
It also functions whether the wearer is walking or running, and generates accurate results without requiring calibration or customization for individual users.
Preliminary testing suggests it even works with children as young as three years of age and elderly with vestibular disorders, whose gait patterns are very different from those of healthy adults.
“We’re achieving the same or better results [than competing technologies] at a far lower cost, and that’s a big deal when it comes to scaling this technology,” said Zanotto.
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