A prosthetic leg that learns from the user’s motion could help amputees walk more naturally.

What’s new: Researchers from the University of Utah designed a robotic leg that uses machine learning to generate a human-like stride. It also helps wearers step over obstacles in a natural way.

How it works: Rather than trying to recognize obstacles in the user’s path, the prosthesis relies on cues from the user’s body to tell it when something is in the way. Sensors in the user’s hip feed data a thousand times per second into a processing unit located in the unit’s calf. For instance, the way a user rotates their hip might tell the leg to tuck its knee to avoid tripping over an obstacle.

A finite state machine (a logic-based controller) determines when and how to flex the knee based on angles of the ankle and thigh and the weight on the prosthetic foot.
A second model called the minimum-jerk planner kicks in when the angle and speed of the artificial limb reach a certain point. It works to minimize sharp, sudden actions.
The prosthesis applies reinforcement learning to adjust its motion as the user walks, using smoothness as the cost function.

Behind the news: A new generation of AI-powered prosthetics could give amputees more control over robotic limbs.

Researchers from the University of Michigan developed an open-source bionic leg that extrapolates knee and ankle movements by analyzing the wearer’s hip muscles, similar to the University of Utah’s method.
A pair of Canadian students won Microsoft’s 2018 Imagine Cup with a camera-equipped prosthetic hand that uses computer vision to detect objects it is about to grasp and adjusts its grip accordingly.
A mechanical arm from École polytechnique fédérale de Lausanne learns to associate common movements with cues from the user’s muscles.

Why it matters: Battery-powered prostheses allow amputees to walk more easily, but they tend to stumble on unfamiliar terrain. This smart leg could provide them with smooth, hazard-free perambulation.

We’re thinking: AI is helping people with the most basic human functions as well as the most abstract scientific problems.

University of Utah

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