NDLI: Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Content Provider	Springer Nature : BioMed Central
Author	Ibrahim, Alzhraa A. Küderle, Arne Gaßner, Heiko Klucken, Jochen Eskofier, Bjoern M. Kluge, Felix
Abstract	Background Multiple 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. Methods Forty-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. Results Six 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. Conclusions The 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.
Related Links	https://jneuroengrehab.biomedcentral.com/counter/pdf/10.1186/s12984-020-00798-9.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
ISSN	17430003
DOI	10.1186/s12984-020-00798-9
Journal	Journal of NeuroEngineering and Rehabilitation
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2020-12-18
Access Restriction	Open
Subject Keyword	Neurosciences Neurology Rehabilitation Medicine Biomedical Engineering and Bioengineering MS Gait Fatigue Accelerometer IMU Machine learning Digital biomarker
Content Type	Text
Resource Type	Article
Subject	Health Informatics Rehabilitation
Journal Impact Factor	5.2/2023
5-Year Journal Impact Factor	5.6/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Association between perceived fatigue and gait parameters measured by an instrumented treadmill in people with multiple sclerosis: a cross-sectional study

Bilateral step length estimation using a single inertial measurement unit attached to the pelvis

Objective assessment of motor fatigue in multiple sclerosis using kinematic gait analysis: a pilot study

Frailty assessment based on trunk kinematic parameters during walking

Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis

Similar Documents

Association between perceived fatigue and gait parameters measured by an instrumented treadmill in people with multiple sclerosis: a cross-sectional study

Bilateral step length estimation using a single inertial measurement unit attached to the pelvis

Objective assessment of motor fatigue in multiple sclerosis using kinematic gait analysis: a pilot study

Frailty assessment based on trunk kinematic parameters during walking

Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Automatic ML-based vestibular gait classification: examining the effects of IMU placement and gait task selection

Correction to: Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

Hidden Markov Model based stride segmentation on unsupervised free-living gait data in Parkinson’s disease patients

Inertial sensor-based gait parameters reflect patient-reported fatigue in multiple sclerosis