Low-Power Wearable System Accurately Tracks Motion

This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.

Many research teams have been exploring ways to passively monitor people’s movements, which could be used in rehabilitation or improving interactions between humans and robotic systems. However, these systems often require a lot of power or interfere with a person’s natural movement, making it challenging to find a solution that works well outside of laboratory conditions.

In a recent study, one team in India tested a motion-sensing system that is easily incorporated on top of a person’s clothing, requires very little power, and offers high accuracy, at more than 99 percent. What’s more, the system uses long, flexible sensors that are able to withstand more than 12,000 cycles of being stretched to three times their original length, suggesting they could be durable for long-term use.

Aman Arora, a principal scientist at CSIR-Central Mechanical Engineering Research Institute, in Durgapur, India, was involved in the study. He says the team was interested in developing a system that’s integrated with clothing, making it easier to use in natural settings. “We therefore asked whether the deformation of the garment itself could be used as the sensing mechanism,” he explains.

To achieve this, they designed highly stretchable piezoresistive sensors that deform with the body and translate these deformations into electrical signals. Six sensors—placed over a person’s clothes—are positioned over the user’s hips, knees, and ankles, while a machine-learning algorithm running on a microcontroller assesses how all of the joints move together simultaneously.

“The model looks at short time windows of the six synchronized sensor signals, rather than relying on a single sensor or a single joint,” Arora explains.

In the study, 12 participants used the system while walking on flat ground and inclines, as well as climbing or descending stairs. The results, published 3 August in IEEE Sensors Journal, show that the model could classify users’ motions with 99.83 percent accuracy after some training with each individual, and achieved 89 percent accuracy on a person who had not yet worn the sensing system. Arora emphasizes that this ability to maintain accuracy on new users is especially important, because people differ in their body dimensions, movement patterns, and preferences for how they position the suit—and thus the sensors—on their body.

Motion tracking for rehab and robotics

In total, the six sensors required 0.318 milliwatts of power. “It is an extremely low power requirement for the sensing layer and can easily be supplied by a small battery,” Arora says.

He notes that, while right now the system remains a prototype that only classifies movements, it could have much broader applications in the future. For example, athletes training for competitions or patients going through rehabilitation could use the sensing system to track their progress over time.

Arora adds that systems like this could be used as humans interact with robotic systems that adapt to the user’s motions as they move. “If a wearable system can continuously understand aspects of how a person is moving, it can potentially respond to the person rather than simply follow a predefined control strategy,” he explains. “We see this as an important direction for developing more personalized and context-aware assistive and rehabilitation technologies.”

Building upon this work, Arora says his team is interested in developing a more personalized movement-sensing platform, which offers a more nuanced look at movements of individuals. He says the initial results of this study are encouraging, but individual differences still influence the sensor signals.

Next, he says, “We would like to investigate ways of making the system more robust to these variations and validate it with a larger and more diverse group of users and under more natural movement conditions.”

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