Abstract
A system for an automated remotely adjustable Controlled Ankle Movement (CAM) boot including a processor of a CAM boot control server node configured to host a machine learning (ML) module and connected to at least one user mobile device connected to at least one target CAM boot controller over a wireless network connection and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire sensory data from a plurality of sensors integrated into a base of the at least one target CAM boot; process the sensory data for noise reduction and fluctuation consistency; derive a plurality of features reflecting metrics related to wearing of the at least one target CAM boot by a user; generate at least one classifier vector based on the plurality of features; provide 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; generate a control command for adjustment of the CAM boot based on the at least one CAM boot adjustment parameter and send the control command to the at least one target CAM boot controller.