Using mobile phones for activity recognition in Parkinson's patients

Abstract

Mobile phones with built-in accelerometers promise a convenient, objective way to quantify everyday movements and classify those movements into activities. Using accelerometer data we estimate the following activities of 18 healthy subjects and eight patients with Parkinson's disease: walking, standing, sitting, holding, or not wearing the phone. We use standard machine learning classifiers (support vector machines, regularized logistic regression) to automatically select, weigh, and combine a large set of standard features for time series analysis. Using cross validation across all samples we are able to correctly identify 96.1% of the activities of healthy subjects and 92.2% of the activities of Parkinson's patients. However, when applying the classification parameters derived from the set of healthy subjects to Parkinson's patients, the percent correct lowers to 60.3%, due to different characteristics of movement. For a fairer comparison across populations we also applied subject-wise cross validation, identifying healthy subject activities with 86.0% accuracy and 75.1% accuracy for patients. We discuss the key differences between these populations, and why algorithms designed for and trained with healthy subject data are not reliable for activity recognition in populations with motor disabilities.

Keywords: Parkinson’s disease; accelerometer; activity recognition; mobile phone.

Figure 1

Recording device and software. (A) The subjects carried T-mobile G1 android phones in their pockets. (B) The axes of the accelerometer relative to the orientation of the phone in (A). (C) The screen which subjects selected which activity they were performing.

Figure 2

Typical examples of accelerometer readings for Parkinson’s patients and healthy subjects for the four activities studied. Red, green, and blue lines are the x, y, and z-axis accelerations, as specified in Figure 1B. The patient shown here exhibited dyskinesia in the arm that is clearly visible while holding the phone and somewhat visible during standing and sitting. The patient also had an irregular gait cycle during walking.