Researchers have developed an algorithm designed with a robot-assistive rehabilitation approach, to help people learn to walk again after neurological injuries such as stroke.
With a new approach, a team led by Dr. Jean-Baptiste Mignardot have used digital technology to operate a robotic harness to help resist the downward force of gravity while also permitting patients to walk forwards, backwards, and side-to-side. This robotic harness is aided by an algorithm which can give personalized support to address patient-specific motor defects. In other words, recognizing that each patient is different.
The system is controlled by an artificial neural network that is capable of assessing by how much the upward and forward force needs to be varied, and to use this information to program the cable harness. The machine is able to assess 120 different variables relating to body movement and to apply the optimum requirements for the individual patient. This device has been tested, so far, on 26 patients (either recovering from spinal cord injuries or strokes). The participants were tested on four tasks—standing on two separate plates, walking on a straight path, walking on a wavy path, or walking on a ladder with irregularly positioned rungs. Each patient who took part in the first trial was able to walk with motor abilities comparable to healthy individuals.