The Kinematics of Parkinson’s Disease: Utilizing Foot Sensors as a Predictor of Postural Instability and Severity

Abstract

Around 60,000 individuals are diagnosed with Parkinson’s Disease (PD) in the United States each year. With the disease’s increasing prevalence, early severity diagnosis is critical for effective symptom management. The MDS-UPDRS is the current gold standard of PD severity diagnosis, which is highly comprehensive. Still, it lacks the utilization of complex data, and its potential subjectivity contributes to the accuracy of only 80.6% of clinical diagnoses of PD. This research project aims to incorporate kinematic data in the MDS-UPDRS by analyzing the impact of left and right foot velocities on the Postural Stability MDS-UPDRS score and, thus, PD severity. Three primary data analyses were conducted to investigate the relationships between (1) velocity and time (i.e., constructing velocity curves), (2) step length for control and PD patients, and (3) step length and MDS-UPDRS scores. Velocity and step length demonstrated a positive correlation; the trends among participant groups in velocity curves were similar to those of the step length boxplot analyses, as confirmed by confidence intervals and p-values. Additionally, a statistically significant (p=0.0237) inverse relationship between step length and MDS-UPDRS scores was observed, indicating that larger step lengths correlate with better postural stability. These results culminate in a negative correlation between foot velocity and MDS-UPDRS scores, and therefore, incorporating kinematic data into the MDS-UPDRS may reduce subjectivity and improve early diagnosis accuracy. By combining this modified rating scale with other diagnostic methods, researchers can develop a device that accurately diagnoses and treats Parkinson’s disease.

Key Words: Biomedical Engineering; Biomechanics; Parkinson’s Disease; Gait Analysis; Foot Sensors