Motor dysfunction is the main clinical symptom and diagnosis basis of patients with Parkinson’s disease (PD). A total of 30 subjects were recruited in this study, including 15 PD patients (PD group) and 15 healthy subjects (control group). Then 5 wearable inertial sensor nodes were worn on the bilateral upper limbs, lower limbs and waist of subjects. When completing the 6 paradigm tasks, the acceleration and angular velocity signals from different parts of the body were acquired and analyzed to obtain 20 quantitative parameters which contain information about the amplitude, frequency, and fatigue degree of movements to assess the motor function. The clinical data of the two groups were statistically analyzed and compared, and then Back Propagation (BP) Neural Network was used to classify the two groups and predict the clinical score. The final results showed that most of the parameters had significant difference between the two groups, ten times of 5-fold cross validation showed that the classification accuracy of the BP Neural Network for the two groups was 90%, and the predictive accuracy of Hoehn-Yahr (H-Y) staging and unified PD rating scale (UPDRS) Ⅲ score of the patients were 72.80% and 68.64%, respectively. This study shows the feasibility of quantitative assessment of motor function in PD patients using wearable sensors, and the quantitative parameters obtained in this paper may have reference value for future related research.