Abstract
BackgroundMultiple sclerosis (MS) is a disabling disease affecting the central nervous system and consequently the whole body’s functional systems resulting in different gait disorders. Fatigue is the most common symptom in MS with a prevalence of 80%. Previous research studied the relation between fatigue and gait impairment using stationary gait analysis systems and short gait tests (e.g. timed 25 ft walk). However, wearable inertial sensors providing gait data from longer and continuous gait bouts have not been used to assess the relation between fatigue and gait parameters in MS. Therefore, the aim of this study was to evaluate the association between fatigue and spatio-temporal gait parameters extracted from wearable foot-worn sensors and to predict the degree of fatigue.MethodsForty-nine patients with MS (32 women; 17 men; aged 41.6 years, EDSS 1.0–6.5) were included where each participant was equipped with a small Inertial Measurement Unit (IMU) on each foot. Spatio-temporal gait parameters were obtained from the 6-min walking test, and the Borg scale of perceived exertion was used to represent fatigue. Gait parameters were normalized by taking the difference of averaged gait parameters between the beginning and end of the test to eliminate inter-individual differences. Afterwards, normalized parameters were transformed to principle components that were used as input to a Random Forest regression model to formulate the relationship between gait parameters and fatigue.ResultsSix principal components were used as input to our model explaining more than 90% of variance within our dataset. Random Forest regression was used to predict fatigue. The model was validated using 10-fold cross validation and the mean absolute error was 1.38 points. Principal components consisting mainly of stride time, maximum toe clearance, heel strike angle, and stride length had large contributions (67%) to the predictions made by the Random Forest.ConclusionsThe level of fatigue can be predicted based on spatio-temporal gait parameters obtained from an IMU based system. The results can help therapists to monitor fatigue before and after treatment and in rehabilitation programs to evaluate their efficacy. Furthermore, this can be used in home monitoring scenarios where therapists can monitor fatigue using IMUs reducing time and effort of patients and therapists.
Highlights
Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and the whole body’s functional systems resulting in different gait disorders
Gait parameters changed by the end of the test which could be a result of muscle fatigue that appeared after an exhaustive test
Our model revealed that there is a strong relation between fatigue and some gait parameters
Summary
Multiple sclerosis (MS) is a disabling disease affecting the central nervous system and the whole body’s functional systems resulting in different gait disorders. MS patients reported fatigue to be the most irritating symptom [3] occurring at all stages of the disease [4] It significantly affects functional capabilities of patients at both home and work, limiting daily activities and reducing quality of life [5]. Previous studies showed that there is a strong association between symptomatic fatigue and muscle fatigue, impaired balance and motor function in MS patients [6, 7], in particular affecting the ability to walk which can be measured by gait analysis systems such as instrumented treadmills [8], camera-based systems [9, 10], or wearable sensors [11,12,13]. Interventions to reduce fatigue can contribute to decreasing fall risk and fall-related injuries and improving overall quality of life
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