Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions. An objective and easily measurable digital marker is crucial for improving the diagnosis and monitoring of PD. Since gait is a complex activity that requires both motor control and cognitive input, this study assumes that kinetic parameters of the foot sensitive to the cognitive load (dual-tasking) for healthy adults can be used to diagnose PD. In this study, walking with a cognitive task has been conducted on healthy subjects, the kinetic parameters have been calculated with algorithms of inverse dynamics in Opensim. Subsequently, the moment-related variables, including the bend and force of the plantar surface, were collected from 13 patients with PD and 32 healthy controls using the wearable system. Statistical analysis of the focused kinetic parameters indicates that the moment of the metatarsophalangeal joint has a significant difference between dual-task walking and single walking. The experimental results demonstrate that features extracted from the bend and force signal of the plantar surface can diagnose PD with an average accuracy of 95.55% with 5-fold cross validation. It demonstrates that kinetic data from the foot captured by wearable sensors can serve as an objective digital marker for PD.