The present technique provides a dynamic assessmentrehabilitation system for balance hypofunctionvertigo patients clinically. This development comprises four subsystems. The sensing subsystems were installed to measure the motions of the subjectsto convert those signals to interact with the control system. Furthermore, in the sensing subsystem, an inertial measurement unitan image module were used to observe posturemovement during the experiment. On the top of the frame of the platform, a monitor was used to implement the locomotionvisual acuity test.

The present invention can be applied to many wearable devices. Its technologies include wireless signal transmission control systems, airbag pneumatic methods,airbag analogue variants. Its various system controls are designed to be modularized so that users can repairreplace them. Possible applications include VR AR wearable systems, unmanned vehicle control systemmedical rehabilitation system.

We have developed a movement assistive system using EEG-controlled functional electric stimulation system. Users’ movement intentions are recognized by brain computer interface, implemented by deep learning network,the detected users’ intentions are then translated into commands to trigger a functional electric stimulation (FES) device to activate users’ particular movements, such as grasping, hand raising, holding a cup, etc. In our study, we have designed our own wireless dry-electrode EEG systemour own FES system.

We develops a precision smart healthcare platform to deliver a comprehensive medical care experience to the elderly by Artificial Intelligence of Things (AIoT) technology. The platform offers health education, community interaction, personal health information,rehabilitation logs. It can be used as a basis for clinical staff to conduct diagnosistreatment. The system development tools use PHP programming languageLaravel framework to develop all arithmetic logic,gradually complete the dynamic association databaseinteractive web page functions.