The results of clinical experiments show that the rehabilitation
solution generated by the smart rehabilitation system is similar to
the doctor’s, which is effective and practicable. There still exist
some differences in the prescription, which may be caused by the
insufficiency of datasets in the knowledge base and the difference
among different doctors. Self-learning method should be applied
to the system to modify related parameters and improve the
performance of the system gradually with the enlargement of the datasets. Still it demonstrates that the automating design
methodology is effective and can help the smart system in
generating prescription. The clinical result of the second part is better than the first part. It may be caused by the expansion of
the database after the preliminary experiments and first part
experiment, and the self-learning method take effect.
IoT-related technologies including RFID have been applied in
the system. These technologies make it possible to interconnect
all the resources, inform the remote sever the medical resources
condition during the planning of reconfiguration, update the
database, and implement the final solution quickly