In this paper we present a classifier based on a hidden Markov model (HMM) that was applied to a gait treadmill dataset for gait phase detection and walking/jogging discrimination. The gait events foot strike, foot flat, heel off, toe off were detected using a uni-axial gyroscope that measured the foot instep angular velocity in the sagittal plane. Walking/jogging activities were discriminated by processing gyroscope data from each detected stride. Supervised learning of the classifier was undertaken using reference data from an optical motion analysis system. Remarkably good generalization properties were achieved across tested subjects and gait speeds. Sensitivity and specificity of gait phase detection exceeded 94% and 98%, respectively, with timing errors that were less than 20 ms, on average; the accuracy of walking/jogging discrimination was approximately 99%.

Gait phase detection and discrimination between walking–jogging activities using hidden Markov models applied to foot motion data from a gyroscope

Mannini, Andrea
;
SABATINI, Angelo Maria
2012-01-01

Abstract

In this paper we present a classifier based on a hidden Markov model (HMM) that was applied to a gait treadmill dataset for gait phase detection and walking/jogging discrimination. The gait events foot strike, foot flat, heel off, toe off were detected using a uni-axial gyroscope that measured the foot instep angular velocity in the sagittal plane. Walking/jogging activities were discriminated by processing gyroscope data from each detected stride. Supervised learning of the classifier was undertaken using reference data from an optical motion analysis system. Remarkably good generalization properties were achieved across tested subjects and gait speeds. Sensitivity and specificity of gait phase detection exceeded 94% and 98%, respectively, with timing errors that were less than 20 ms, on average; the accuracy of walking/jogging discrimination was approximately 99%.
2012
File in questo prodotto:
File Dimensione Formato  
G&P 2012.pdf

solo utenti autorizzati

Tipologia: Documento in Post-print/Accepted manuscript
Licenza: Non pubblico
Dimensione 437.12 kB
Formato Adobe PDF
437.12 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/389046
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 127
social impact