Wearable inertial sensors are highly sensitive in the detection of gait disturbances and fatigue at early stages of multiple sclerosis

Abstract

Background The aim of the current study was to examine multiple gait parameters obtained by wearable inertial sensors and their sensitivity to clinical status in early multiple sclerosis (MS). Further, a potential correlation between gait parameters and subjective fatigue was explored. Methods Automated gait analyses were carried out on 88 MS patients and 31 healthy participants. To measure gait parameters (i.e. walking speed, stride length, stride duration, duration of stance and swing phase, minimal toe-to-floor distance), wearable inertial sensors were utilized throughout a 6-min 25-ft walk. Additionally, self-reported subjective fatigue was assessed. Results Mean gait parameters consistently revealed significant differences between healthy participants and MS patients from as early as an Expanded Disability Status Scale (EDSS) value of 1.5 onwards. Further, MS patients showed a significant linear trend in all parameters, reflecting continuously deteriorating gait performance throughout the test. This linear deterioration trend showed significant correlations with fatigue. Conclusions Wearable inertial sensors are highly sensitive in the detection of gait disturbances, even in early MS, where global scales such as the EDSS do not provide any clinical information about deviations in gait behavior. Moreover, these measures provide a linear trend parameter of gait deterioration that may serve as a surrogate marker of fatigue. In sum, these results suggest that classic timed walking tests in routine clinical practice should be replaced by readily and automatically applicable gait assessments, as provided by inertial sensors.

Impact of the COVID-19 Pandemic on Physical Activity and Associated Technology Use in Persons With Multiple Sclerosis: An International RIMS-SIG Mobility Survey Study

“…Wearables were the most frequently used technology to support physical activity. Wearables are highly sensitive in detection of gait disturbances and fatigue in PwMS, 31,32 and evidence of their use to sustain physical activity behavior is largely growing 33 ; thus, they can be a valuable addition to walking programs. We noticed a mismatch between what PwMS were doing (in terms of physical activity) with what the research and clinical community made available during the pandemic.…”

A topological data analysis-based method for gait signals with an application to the study of multiple sclerosis

“…Standard features such as velocity, step time or step length can be extracted from IMU signals. They have been used to discriminate healthy subjects from patients, or groups of patients with different levels of disease severity, but those studies rely on long protocols (walking for several minutes to get a high number of steps) [14][15][16] and/or gait event detection [14,15,[17][18][19][20]. Long protocols are incompatible with patients with severely altered gait who have trouble walking a few meters, and are more difficult to include in clinical day-to-day practice.…”

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

“…Postural sway captures the horizontal acceleration of the person's center in all directions, most often in the mediolateral and anterior-posterior planes. Typically, sway area, sway range, sway velocity, and jerk, defined as the smoothness of the trunk sway (rate of change), are extracted and Gait cycle / stride duration s, ms Blin et al, 1990;Ginis et al, 2017;Shah et al, 2020Benedetti et al, 1999Straudi et al, 2013;Müller et al, 2021 Cadence steps/min Curtze et al, 2015;Horak et al, 2016;Iijima et al, 2017Martin et al, 2006Straudi et al, 2013;Leone et al, 2018 Gait velocity / speed m/s, cm/s Herman et al, 2014;Galna et al, 2015;Mancini, 2020 Benedetti et al, 1999;Remelius et al, 2012;Müller et al, 2021 Stride / step length m Rochester et al, 2014;Ferrari et al, 2016;Cebi et al, 2020Martin et al, 2006Remelius et al, 2012;Leone et al, 2018 Double support time % cycle, % stride Blin et al, 1990;Curtze et al, 2015;Shah et al, 2020Benedetti et al, 1999Straudi et al, 2013;Leone et al, 2018 Stride / step time variability s Herman et al, 2014;Galna et al, 2015;Ma et al, 2020aMoon et al, 2015Allali et al, 2016;Kalron et al, 2018 Knee (lower leg) ROM degree Dewey et al, 2014;Curtze et al, 201...…”