Method and system for ai-based adjustable cam boot

A method an automated remotely adjustable Controlled Ankle Movement (CAM) boot comprising, comprising of:

• acquiring, by a CAM boot control server (CBCS) node configured to host a machine-learning (ML) module, sensory data from a plurality of sensors integrated into a base of the at least one target CAM boot;
• processing, by the CBCS node, the sensory data for noise reduction and fluctuation consistency;
• deriving, by the CBCS node, a plurality of features reflecting metrics related to wearing of the at least one target CAM boot by a user;
• generating, by the CBCS node, at least one classifier vector based on the plurality of features;
• providing, by the CBCS node, the at least one classifier vector to the ML module configured to generate a predictive model for producing at least one CAM boot adjustment parameter;
• generating, by the CBCS node, a control command for adjustment of the CAM boot based on the at least one CAM boot adjustment parameter;
• and
• sending, by the CBCS node, the control command to the at least one target CAM boot controller.

A non-transitory computer-readable medium comprising instructions, that when read by a processor, cause the processor to perform:

• acquiring sensory data from a plurality of sensors integrated into a base of the at least one target CAM boot;
• processing the sensory data for noise reduction and fluctuation consistency;
• deriving a plurality of features reflecting metrics related to wearing of the at least one target CAM boot by a user;
• generating at least one classifier vector based on the plurality of features;
• providing the at least one classifier vector to the ML module configured to generate a predictive model for producing at least one CAM boot adjustment parameter;
• generating a control command for adjustment of the CAM boot based on the at least one CAM boot adjustment parameter;
• and
• sending the control command to the at least one target CAM boot controller.