Detection Of Freezing Of Gait For Parkinson’s Disease Patients With Motion Sensors

Freezing of gait(FOG)is a common gait disorder among patients with advanced Parkinson’s disease,which is associated with falls and negatively impact the patient’s quality of life.Wearable systems with rhythmic auditory stimulation can be applied to help patients resume walking and reduce the risk of falls.As the basis of the system,the detection of freezing of gait can also provide relevant information for disease assessment,which has important research significance and application value.The thesis designed the relevant experimental procedures to obtain FOG signals from PD patients.Accelerometers and gyroscopes were placed on the patient’s waist,left calve,right calve and the remaining 6 body parts.A total of 2 hours and 31 minutes of data was recorded in the experiment.Signals recorded from 10 patients with PD who presented the symptom of FOG and 2 patients who suffered from PD but they do not present FOG events.Professional physicians identified 276 FOG events from video recordings.On this basis,the research is carried out around segmentation,feature extraction,and classification.In this thesis,13 typical time-frequency domain features were extracted from the sensor signals.Due to the low classification accuracy and high computational cost caused by high-dimensional feature space,mutual information and analysis of variance were selected to evaluate the importance of features and the effectiveness of the two methods were compared.The thesis evaluated the detection effects using several different configurations of sensors in order to conclude to the set of sensors which can produce optimal FOG episode detection and selected the best features for the optimal sensor configuration.After that,random forest,AdaBoost,linear discriminant analysis,and multi-layer perceptual neural network algorithm were applied to classification.The effects of the ratio of positive to negative samples and window size on classifier performance were studied,and the performance of the detection model was gradually optimized.The final results indicated that the proposed model was able to detect FOG events with 87.3% sensitivity,91.2% specificity,89.5% AUC when using 1.25 s time window(125 sample points)and 0.15s(15 sample points)step,the 35 features obtained from the gyro and accelerometer placed on patients’ left shank and AdaBoost classifier.