Validation of a new model-free signal processing method for gait feature extraction using inertial measurement units to diagnose and quantify the severity of Parkinson's disease

Gait analysis is important in diagnosing and quantifying the severity of Parkinson's disease. Different motion tracking systems such as inertial measurement units (IMU) are widely used to detect gait parameters associated with the severity of Parkinson's disease. Although these systems are accurate enough to measure different gait parameters, they utilize a predefined model of human gait to measure these parameters. Model- based signal processing, that takes into account the kinematics of human body, enforces that sensors be placed in a certain configuration in terms of orientation and location which introduces a burden at the signal processing development phase. In addition, it affects the accuracy and robustness of the system when the user does not place the sensors at their pre-defined locations and with a pre-define orientation. In this paper, we introduce a set of model-free features to estimate gait parameters for the applications of diagnosing and quantifying the severity of Parkinson's disease. A model-free signal processing technique does not limit sensor placement, in addition, it does not require the knowledge on the kinematics of the users and the human subjects. We show that our proposed features, using a model-free signal processing technique, are highly correlated (R-value up to 0.96 for suitable locations) with gait parameters obtained from model-based sophisticated algorithms. Therefore, these simple model-free features may be suitable for ongoing assessment of Parkinson's disease and they can be an alternative for conventional gait parameters used for rapid application development.