Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

Validation of Inter-Subject Training for Hidden Markov Models Applied to Gait Phase Detection in Children with Cerebral Palsy

Gait-phase recognition is a necessary functionality to drive robotic rehabilitation devices for lower limbs. Hidden Markov Models (HMMs) represent a viable solution, but they need subject-specific training, making data processing very time-consuming. Here, we validated an inter-subject procedure to...

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Bibliographic Details
Main Authors: Juri Taborri, Emilia Scalona, Eduardo Palermo, Stefano Rossi, Paolo Cappa
Format: Article
Language:English
Published: MDPI AG 2015-09-01
Series:Sensors
Subjects:
Hidden Markov Model
inter-subject training
gait phase partitioning
Cerebral Palsy
Inertial Measurement Units
Wearable Sensor System
pediatric subjects
Online Access:http://www.mdpi.com/1424-8220/15/9/24514

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