Machine-learned movement determination based on intent identification

A mobility improvement system comprising a non-transitory computer-readable storage medium storing instructions for execution and a hardware processor configured to execute the instructions, the instructions, when executed, cause the hardware processor to perform steps comprising:

• collecting a first set of motor intent data of one or more users from a database;
• labeling the first set of motor intent data with an intent label representative of intended motion characterized by the first set of motor intent data;
• creating a first training set based on the labeled first set of motor intent data;
• training a machine learning model using the first training set, the machine learning model configured to output, based on monitored motor intent data, a movement prediction corresponding to likely motion characterized by the monitored motor intent data;
• creating a second training set based on the movement prediction and a second set of motor intent data representative of movement signals of a target user;
• and
• re-training the machine learning model using the second training set such that the machine learning model is optimized to motions of the target user.

A method comprising:

• collecting a set of motor intent data of one or more users from a database;
• labeling the set of motor intent data with an intent label representative of intended motion characterized by the set of motor intent data;
• creating a training set based on the labeled set of motor intent data;
• and
• training a machine learning model using the training set, the machine learning model configured to output, based on monitored motor intent data, a movement prediction corresponding to likely motion characterized by the monitored motor intent data.