The degree of fatigue in patients with multiple sclerosis (MS) can be predicted on the basis of spatio-temporal gait measurements from a system based on an inertial measurement unit (IMU). This is evident from study results published in the Journal of NeuroEngineering and Rehabilitation.
Gait disorders are often a result of the effects of MS on whole body function. However, the most common symptom of MS is fatigue. Previous data have shown a relationship between the two, but wearable biosensors that provide gait data have not been used in previous studies.
In the current study, researchers tried to further determine the relationship between patient-reported fatigue and the spatio-temporal gait measurements worn by wearable IMU sensors on the foot. They also looked at the change in gait measurements during a 6-minute walk test (6MWT) and the best way to predict the level of fatigue in patients with MS.
This study included 49 patients (women, 32; mean age, 41.6 years) with MS. Study researchers collected data from the MS Center of the University Hospital Erlangen and the Neurological Rehabilitation Center Quellenhof in Germany. Each patient wore an IMU on the lateral ankle of each foot. The study researchers then determined the spatio-temporal gait parameters from the 6MWT and assessed the fatigue using the Borg scale.
The researchers then performed a regression analysis using fatigue as the dependent variable and combinations of normalized gait parameters as the independent variables. To minimize the Type 1 error, they performed principal component analysis of the data to convert the normalized gait parameters into components with significant deviations.
Six main components that were used explained over 90 percent of the variance in the data set. The researchers used random forest regression to predict fatigue. This model was confirmed by 10-fold cross-validation (mean absolute error, 1.38 ± 1.07 points).
The 4 most important components with significant contributions to the predictions of the random forest regression consisted mainly of stride time, maximum toe distance, heel strike angle and stride length. This indicated a possible connection between fatigue and such gait measures. The results also showed only a moderate association between fatigue and the expanded disability status scale.
Limitations of this study included the relatively small data set that did not take into account factors that influenced fatigue (such as insomnia, depression, and cognitive impairment), as well as the need to use other scales to measure fatigue and how it relates to the Borg scale examine.
Ultimately, the study’s researchers came to the conclusion that “wearable sensors enable gait analysis during unhindered, continuous and prolonged above-ground walking movements. We observed a correlation between self-perceived fatigue and spatiotemporal gait parameters of MS patients at the end of an exhausting task like 6MWT. As a result, “the system can be used by physicians as a monitoring tool in their treatment and intervention programs to reduce the level of fatigue in MS patients.”
Ibrahim AA, Küderle A, Gaßner H., Klucken J., Eskofier BM, Kluge F. Inertia sensor-based gait parameters reflect the patient-reported fatigue in multiple sclerosis. J NeuroEngineering Rehabil. Published online December 18, 2020. doi: 10.1186 / s12984-020-00798-9