| dc.description.abstract | Parkinson's disease (PD) is the second most common neurodegenerative disease in the United States. The cardinal symptoms of PD are tremor, rigidity, slowed movement, and impaired balance. These symptoms often interfere with the daily activities of people with Parkinson's (PwPD) and negatively affect quality of life (QoL). Therefore, monitoring PD symptoms is essential for clinical evaluations and adjusting medication to help maintain QoL for PwPD. We are developing a mobile app to conduct at-home PD symptom monitoring to provide more timely, frequent, and accurate measurements of PD symptoms. While the tremor and finger-tapping results collected in the mobile app have been discussed in previous publications, this paper focuses on the design and evaluation of postural stability tests in the app and validating the reliability of the embedded accelerometers and gyroscopes in smartphones. During the test, a shaker was employed to provide vibration in amplitude and frequency ranges similar to human postural stability signals, and both the accelerometer and gyroscope measurements were evaluated. We used signal processing algorithms to extract postural stability factors, such as the root-mean-square (RMS) value, the derivative of acceleration, and frequency factors for the accelerations, and the ranges and RMS for the angular velocity. Our findings show that smartphone devices have good consistency over multiple trials and between devices, and motion patterns achieved from multiple data points are reliable for postural stability analysis. | |
