Abstract: A device may include a high-level control layer comprising a convolutional neural network (CNN) configured to receive exoskeleton sensor data from one or more sensors on an exoskeleton and generate a user state estimate. The device may include a mid-level control layer configured to receive the user state estimate and generate a torque command for an actuator based on the user state estimate. The user state estimate may be an estimated gait phase, where the mid-level control layer generates the torque command as a function of the estimated gait phase based on an assistance profile. The high-level control layer may include a backward labeler and a real-time adaptation trainer. The backward labeler relabels ground truth gait phase from the exoskeleton sensor data using a local peak detection. The real-time adaptation trainer trains the CNN in a single epoch of backpropagation with the ground truth gait phase.

Abstract: An exemplary embodiment of the present invention provides an exosuit support system, comprising first and second orthotic interfaces, first and second members, and first and second actuators. The first and second orthotic interfaces can be configured to attach to left and right legs of a user, respectively. The first member can comprise a first end connected to the first orthotic interface and a second end connected to a portion of the system configured to be positioned proximate a right shoulder of the user. The second member can comprise a first end connected to the second orthotic interface and a second end connected to a portion of the system configured to be positioned proximate a left shoulder of the user. The first actuator can be configured to apply a first tension to the first member. The second actuator can be configured to apply a second tension to the second member.