Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data

Abstract

One of the core problems for people with multiple sclerosis (pwMS) is the impairment of their ability to walk, which can be severely restrictive in everyday life. Therefore, monitoring of ambulatory function is of great importance to be able to effectively counteract disease progression. An extensive gait analysis, such as the Dresden protocol for multidimensional walking assessment, covers several facets of walking impairment including a 2-min walk test, in which the distance taken by the patient in two minutes is measured by an odometer. Using this approach, it is questionable how precise the measuring methods are at recording the distance traveled. In this project, we investigate whether the current measurement can be replaced by a digital measurement method based on accelerometers (six Opal sensors from the Mobility Lab system) that are attached to the patient’s body. We developed two algorithms using these data and compared the validity of these approaches using the results from 2-min walk tests from 562 pwMS that were collected with a gold-standard odometer. In 48.4% of pwMS, we detected an average relative measurement error of less than 5%, while results from 25.8% of the pwMS showed a relative measurement error of up to 10%. The algorithm had difficulties correctly calculating the walking distances in another 25.8% of pwMS; these results showed a measurement error of more than 20%. A main reason for this moderate performance was the variety of pathologically altered gait patterns in pwMS that may complicate the step detection. Overall, both algorithms achieved favorable levels of agreement (r = 0.884 and r = 0.980) with the odometer. Finally, we present suggestions for improvement of the measurement system to be implemented in the future.

Automatic Assessment of the 2-Minute Walk Distance for Remote Monitoring of People with Multiple Sclerosis

“…Furthermore, using D i , 43.9% of the 2MWDs exhibit an error ε of less than 5%. Previous studies found similar errors for 2MWTs performed at hospital, with 48.4% of pwMS exhibiting measurement errors of less than 5% [28]. Other studies examining the accuracy and precision of wearable motion sensors reported even lower errors, but their primary limitation is that the walk tests were conducted on a motorized treadmill [29,30].…”

Transparent Quality Optimization for Machine Learning-Based Regression in Neurology

“…In addition, rotation at the end of a gait is insufficiently detected because the reversal angle of the odometer is different. A digitized approach with the use of Inertial Measurement Units (IMUs) is increasingly being considered to avoid high inter-rater reliability and to increase the efficiency and accuracy of the measurement process [ 13 , 14 ]. We want to take this approach even further and develop and optimize an automated system for distance measurement by using ML technologies based on the aggregated multidimensional data of PwMS from the MLS.…”

Digital Innovation in Multiple Sclerosis Management

“…The strength of this solution is the integration of hand motor testing, gait tests, as well as cognitive and affective assessment under highest regulatory standards. With a special focus on gait testing, Trentzsch et al developed a digitally assisted measurement system that uses accelerometers and multiple algorithms to assess the distance of a 2-min walk test [ 5 ]. The benefit of such a system is the simultaneous realization of standardization as well as objectification of measurement, which can be applied independently of human resources.…”