Participants: Junhao Wang, Arya Farahi and Xinlin Song (Challenge 1); Yi-Lun Wu, Chun-Yu Hsiung and Xinlin Song (Challenge 2)
Parkinson’s disease (PD) is a degenerative disorder of central nervous system that mainly affects the motor system. Currently, there is no objective test to diagnose PD and the bedside examination by a neurologist remains the most important diagnostic tool. The examination is performed using the assessment of motor symptoms such as shaking, rigidity, slowness of movement and postural instability. However, these motor symptoms begin to occur at a very late stage. Smartphones and smart watches have sensitive sensors (accelerometer, gyroscope, and pedometer) that can track the user’s motion more frequently than clinical examinations at much lower cost. While the movement information is recorded by the sensors, the rough sensor data is hard to interpret and give limited help to PD diagnosis.
In the Parkinson’s Biomarker Challenge, we are tasked to extract useful features from time series accelerometer and gyroscope data. The data of Challenge 1 consist of ~ 35000 records collected from ~ 3000 participants with phone APP in their daily life. The final goal is to predict whether a participant has Parkinson’s disease or not. The data of Challenge 2 consist of records from ~ 20 patients doing different tasks (such as drinking water, folding towels, assembling nuts and bolts etc.). And the goal is to predict how severe is the limb action tremor.
The general method we used in both two challenges is generating multiple features from the time series sensor data and performing feature selection to get the top features. Finally, a machine learning model is built based on the top features. The details of the methods we use can be found here:
The highest ranking the team received was 4th place in Challenge 2.