Background:~Parkinson's disease is a progressively degenerative disorder. Patients need early screening, therapeutic intervention, and personalized interaction with outpatient rehabilitative treatment. In the past, it had been difficult to meet these goals. Recent advances in bio-sensors technology has enabled collection of bio-metric data. Models of brainwave analysis have also matured. In addition, our ability to analyze vibrational spectrogram had also greatly improved. How to combine these enabling technologies to meet the needs of Parkinson's patients is an urgent topic of research.~Objective:~The present study aims to compare the clinical efficacy of intelligent POWER therapy, intelligent LSVT-BIG therapy, and the three exercise models currently in clinical use. DCM_IR analysis will also be incorporated into the analysis to develop a personalized and intelligent Parkinson's rehabilitative therapy platform.~Method:~Patients will be randomly assigned into three groups, i.e. intelligent POWER, intelligent LSVT-BIG, and current protocol group. Single blind data collection will be used. Patients will be evaluated immediately before treatment, immediately after treatment, and 4 weeks after treatment. Evaluated criteria will include mini-BESTest, Unified Parkinson's disease rating scale, muscle power of lower extremity, time up and go, walk velocity, step length, cadence, and Parkinson's disease questionnaire PDQ-39.~Expected Outcome:~An intelligent rehabilitative therapy platform may be built on the sensor data and neural-network analysis of the data. The platform will enable patients to interact with medical personnel on out-patient basis. If further combined with DCM_IR analysis, personalized therapeutic efficacy indicator may be uncovered, thereby, realizing intelligent personalized rehabilitative therapy.
The present study aims to compare the clinical efficacy of intelligent POWER therapy, intelligent LSVT-BIG therapy, and the three exercise models currently in clinical use. DCM_IR analysis will also be incorporated into the analysis to develop a personalized and intelligent Parkinson's rehabilitative therapy platform.