New walking-based model can predict fall rate

Researchers at Ohio State University have developed a mathematical model that allows them to predict the landing of a person’s next step with more than 80% accuracy based on the position of the person’s body during the current step.

The results of the study show that a mid-stance examination of linear function of the hip position and velocity state allow predictions of the seemingly random step-to-step variation in foot position.

“We were able to show that the next foot position can be predicted way in advance of when the foot is placed — as early as the middle of the previous step — based on how the body is falling,” Manoj Srinivasan, PhD, assistant professor of mechanical engineering and head of the Movement Lab at Ohio State, stated in a press release. “Nobody knew that such high predictability was possible with such a simple model and with only normal walking data.”

The researchers fitted 10 participants with motion capture markers and tracked them walking on a treadmill at various speeds, from a leisurely stroll to a pace of 2 miles to 3 miles per hour. They then studied the way the participants’ bodies “fell” and their foot placement during each step.

Participants’ bodies initiated an almost imperceptible fall to the right before taking a step to the right, and a fall to the left before taking a step to the left. If the pelvis moved a millimeter differently one way or the other in a particular step, it created a tiny imbalance, which participants unconsciously compensated for by placing the next step in an appropriate position.

Srinivasan will continue this work in the Movement Lab, where his team will corroborate these results with other experiments and develop their model to aid diagnosis and treatment of stability problems.


Srinivasan M. Stepping in the direction of the fall: the next foot placement can be predicted from current upper body state in steady-state walking. Biology Letters. Published 24 September 2014 doi: 10.1098/rsbl.2014.0405

Disclosure: This work was supported in part by the National Science Foundation.

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