Paretic limb rehabilitation methods and systems

A system for treating a paretic limb of a person, the system comprising:

• at least first and second neuromuscular sensors that are respectively configured for placement at first predefined locations on or in agonist and antagonist muscles of a healthy limb of a person, the first and second neuromuscular sensors configured to produce first neuromuscular signals upon the person performing a predefined exercise with the healthy limb;
• a first plurality of motion sensors that are configured for placement at second predefined locations on the healthy limb, the first plurality of motion sensors configured to produce first motion signals upon the person performing the predefined exercise with the healthy limb;
• at least third and fourth neuromuscular sensors that are respectively configured for placement at third predefined locations on or in agonist and antagonist muscles of a paretic limb of the person, the agonist and antagonist muscles of the paretic limb corresponding to the agonist and antagonist muscles of the healthy limb, the third predefined locations corresponding to the first predefined locations, the third and fourth neuromuscular sensors configured to produce second neuromuscular signals upon the person attempting to perform the predefined exercise with the paretic limb;
• a second plurality of motion sensors that are configured for placement at fourth predefined locations on the paretic limb, the fourth predefined locations on the paretic limb corresponding to the second predefined locations of the healthy limb, the second plurality of motion sensors configured to produce second motion signals upon the person attempting to perform the predefined exercise with the paretic limb;
• a body actuator associated with the paretic limb that is configured to stimulate the paretic limb to perform the predetermined exercise;
• and
• a control system comprising one or more memories and one or more hardware processors, wherein the one or more memories comprise instructions, that when executed by the one or more hardware processors, are operable to:
• generate a neuromuscular-to-motion decoder by mapping the first neuromuscular signals to the first motion signals over time using a mapping method during the person performing the predefined exercise with the healthy limb, the mapping method selected from the group consisting of a machine learning method, a statistical method, a data mining method and any combination of the machine learning method, statistical method and data mining method;
• input the second neuromuscular signals to the neuromuscular-to-motion decoder for causing the neuromuscular-to-motion decoder to output first motion commands;
• produce trajectory data defining a trajectory to be followed by the paretic limb depending on a deviation between the second motion signals and predefined exercise data defining the predefined exercise;
• produce second motion commands depending on the trajectory data to be followed by the paretic limb;
• produce final motion commands depending on the first motion commands and the second motion commands;
• and
• send the final motion commands to the body actuator associated to the paretic limb to cause the body actuator to stimulate the paretic limb to perform the predefined exercise,
• one or more of the first, second, third and fourth neuromuscular sensors being a sensor selected from the group consisting of an electromyography sensor, an electroneurography sensor, an ultrasound sensor, and an optical sensor,
• one or more of the first and second plurality of motion sensors being selected from the group consisting of an inertial sensor, a magnetic sensor, and an optical sensor.