Relationship Between Kernel Density Function Estimates Of Gait Time Series And Clinical Data

Multiple sclerosis (MS) is a neurological disorder which interrupts the communication between the brain and other parts of the body resulting in neurologic and physical and functional limitations. Gait deterioration is one of the most common problems and hence assessments of walking quality is a crucial part of MS diagnosis. In-clinic evaluations use physical examinations and an expanded disability status scale (EDSS) to label MS subjects into various disability groups such as mild, moderate, etc. Current research in MS focuses on leveraging the inertial data for accurate gait assessments to overcome the shortcomings of qualitative methods and enhancing the separability performance between MS and control subjects. However, MS symptoms vary among individuals. In [1], we showed that the inertial gait density estimates can be used to identify the multiple types of walk within each disability group. In this work, we show that the peak value of the inertial gait density estimate correlates significantly to distance covered in six minutes (r = -0.8028, p < 0.0001), making it clinically meaningful. The peak values also correlate with other related subjective data discussed in the paper. Thus the gait density of an MS subject can be evaluated to objectively assess the impact of MS on his/her functional capacities. We believe that we are supplementing existing information with a new, high-precision objective anchor to help reduce dependence on subjective and burdensome questionnaires.