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

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.

Keywords:

• Gait pattern discrimination
• inertial-based human activity recognition
• ADLs classification
• gait assessment using accelerometry

Acknowledgements

This project is partially supported by the ERA-NET SAF€RA project, the Italian National Institute for Insurance against Accidents at Work (INAIL) and the Basque Institute for Occupational Health and Safety (OSALAN). This publication only reflects the views of the authors; funding Institutes cannot be held responsible for any use of the information contained therein.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This project is partially supported by the ERA-NET SAF€RA project (Codice Unico Progetto - CUP): E86D15000740005, the Italian National Institute for Insurance against Accidents at Work (INAIL) and the Basque Institute for Occupational Health and Safety (OSALAN).