Monitoring gait in multiple sclerosis with novel wearable motion sensors.
BACKGROUND
Mobility impairment is common in people with multiple sclerosis (PwMS) and there is a need to assess mobility in remote settings. Here, we apply a novel wireless, skin-mounted, and conformal inertial sensor (BioStampRC, MC10 Inc.) to examine gait characteristics of PwMS under controlled conditions. We determine the accuracy and precision of BioStampRC in measuring gait kinematics by comparing to contemporary research-grade measurement devices.


METHODS
A total of 45 PwMS, who presented with diverse walking impairment (Mild MS = 15, Moderate MS = 15, Severe MS = 15), and 15 healthy control subjects participated in the study. Participants completed a series of clinical walking tests. During the tests participants were instrumented with BioStampRC and MTx (Xsens, Inc.) sensors on their shanks, as well as an activity monitor GT3X (Actigraph, Inc.) on their non-dominant hip. Shank angular velocity was simultaneously measured with the inertial sensors. Step number and temporal gait parameters were calculated from the data recorded by each sensor. Visual inspection and the MTx served as the reference standards for computing the step number and temporal parameters, respectively. Accuracy (error) and precision (variance of error) was assessed based on absolute and relative metrics. Temporal parameters were compared across groups using ANOVA.


RESULTS
Mean accuracy±precision for the BioStampRC was 2±2 steps error for step number, 6±9ms error for stride time and 6±7ms error for step time (0.6-2.6% relative error). Swing time had the least accuracy±precision (25±19ms error, 5±4% relative error) among the parameters. GT3X had the least accuracy±precision (8±14% relative error) in step number estimate among the devices. Both MTx and BioStampRC detected significantly distinct gait characteristics between PwMS with different disability levels (p<0.01).


CONCLUSIONS
BioStampRC sensors accurately and precisely measure gait parameters in PwMS across diverse walking impairment levels and detected differences in gait characteristics by disability level in PwMS. This technology has the potential to provide granular monitoring of gait both inside and outside the clinic.
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Yaejin
http://orcid.org/0000-0002-1187-6847
Y
Moon
Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, Vermont, United States of America.
Ryan S
RS
McGinnis
MC10 Inc., Lexington, Massachusetts, United States of America.
Kirsten
K
Seagers
Department of Physical Therapy, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.
Robert W
RW
Motl
MC10 Inc., Lexington, Massachusetts, United States of America.
Nirav
N
Sheth
MC10 Inc., Lexington, Massachusetts, United States of America.
John A
JA
Wright
MC10 Inc., Lexington, Massachusetts, United States of America.
Roozbeh
R
Ghaffari
Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Jacob J
JJ
Sosnoff
D000328:Adult; D000368:Aged; D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait; D020233:Gait Disorders, Neurologic / Q000175:diagnosis / Q000209:etiology / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology*; D011597:Psychomotor Performance; D015203:Reproducibility of Results