Identification of Gait Events in Healthy and Parkinson's Disease Subjects Using Inertial Sensors: A Supervised Learning Approach

• Published in: IEEE sensors journal Vol. 20; no. 24; pp. 14984 - 14993
• Main Authors: Perez-Ibarra, Juan C., Siqueira, Adriano A. G., Krebs, Hermano I.
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Classifiers; Event detection; Foot; Gait; Gait analysis; gait phases; Heuristic methods; Inertial sensing devices; Legged locomotion; Loading; machine learning; meta-heuristics; Optimization; Parkinson's disease; Phases; Real-time systems; Sensors; Simulated annealing; Supervised learning; Support vector machines; Treadmills; Walking; wearable sensors; Wearable technology

Automatic detection of gait phases through supervised learning is a feasible approach that takes advantage of the consistency of the gait cycle among healthy subjects. However, gait patterns among subjects with impairments are not as consistent and most of the existing algorithms have limited perfor...