In this article, we present a pervasive solution for gait pattern classification that uses accelerometer data retrieved from a waist-mounted inertial sensor. The proposed algorithm has been conceived to operate continuously for long-term applications. With respect to traditional approaches that use a large number of features and sophisticated classifiers, our solution is able to assess four different gait patterns (standing, level walking, stair ascending and descending) by using three features and a decision tree. We assess the algorithm detection performances using data that we retrieved from a validation group composed by nine young and healthy volunteers, for a total number of 36 tests and 12.5 h of recorded acceleration data. Experimental results show that in continuous applications the proposed algorithm is able to effectively discriminate between standing (100%), level walking (∼99%), stair ascending (∼84%), and descending (∼85%), with an average classification accuracy for the four patterns that exceeds 92% in continuous, long-lasting applications.

Long-term gait pattern assessment using a tri-axial accelerometer

Setola R;
2017-01-01

Abstract

In this article, we present a pervasive solution for gait pattern classification that uses accelerometer data retrieved from a waist-mounted inertial sensor. The proposed algorithm has been conceived to operate continuously for long-term applications. With respect to traditional approaches that use a large number of features and sophisticated classifiers, our solution is able to assess four different gait patterns (standing, level walking, stair ascending and descending) by using three features and a decision tree. We assess the algorithm detection performances using data that we retrieved from a validation group composed by nine young and healthy volunteers, for a total number of 36 tests and 12.5 h of recorded acceleration data. Experimental results show that in continuous applications the proposed algorithm is able to effectively discriminate between standing (100%), level walking (∼99%), stair ascending (∼84%), and descending (∼85%), with an average classification accuracy for the four patterns that exceeds 92% in continuous, long-lasting applications.
ADLs classification; gait assessment using accelerometry; Gait pattern discrimination; inertial-based human activity recognition
File in questo prodotto:
Non ci sono file associati a questo prodotto.

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/20.500.12610/6517
 Attenzione

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

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 9
  • ???jsp.display-item.citation.isi??? ND
social impact