TY - GEN
T1 - Exploring Characteristic Features in Gait Patterns for Predicting Multiple Sclerosis
AU - Kaur, Rachneet
AU - Menon, Sanjana
AU - Zhang, Xiaomiao
AU - Sowers, Richard
AU - Hernandez, Manuel E.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.
AB - Multiple Sclerosis (MS), an autoimmune and demyelinating disease, is one the most prevalent neurological disabilities in young adults. It results in damage of the central nervous system, disrupting communication between the patient's brain, spinal cord and body. Mobility limitations is one of the earliest symptoms and affects a majority of persons with Multiple Sclerosis. We are working towards an effort to characterize individuals with MS, from those without, on the basis of variations in the gait patterns. In the proposed work, statistical methods were used to identify differentiating gait data features for MS characterization. The prediction algorithms built upon these characteristic features will help clinicians develop effective and early cure and therapy designs for persons with Multiple Sclerosis.
KW - Gait patterns
KW - Multiple sclerosis
KW - Spatiotemporal data
KW - Statistical learning
UR - http://www.scopus.com/inward/record.url?scp=85077888079&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077888079&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2019.8857604
DO - 10.1109/EMBC.2019.8857604
M3 - Conference contribution
C2 - 31946799
AN - SCOPUS:85077888079
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4217
EP - 4220
BT - 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019
Y2 - 23 July 2019 through 27 July 2019
ER -