Development and Research of an Individualized Intelligent Platform for Rehabilitaion in Parkinson's Disease
Development and Research of an Individualized Intelligent Platform for Rehabilitaion in Parkinson's Disease
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.
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.
Inclusion Criteria:

 1. .Parkinson's disease diagnosis, Hoehn-Yahr level I-III

 2. .Stable medicine intake for 2 weeks at least

 3. .Able to walk independently for 15 meters

 4. .Aged 40-85 years old

 Exclusion Criteria:

 1. .Cognition deficits(MMSE score<24)

 2. .Combined other neurological disease, such as stork, SCI, and so on.

 3. .Pregnant or Breastfeeding