Long-term gait pattern assessment using a tri-axial accelerometer. (4th July 2017)

• Record Type: Journal Article
• Authors: De Cillis, Francesca; De Simio, Francesca; Setola, Roberto
• Is Part Of: Journal of medical engineering & technology. Volume 41:Number 5(2017)
• Page Start: 346
• Page End: 361
• Publication Date: 2017-07-04
• Subjects: Gait pattern discrimination -- inertial-based human activity recognition -- ADLs classification -- gait assessment using accelerometry; Biomedical engineering -- Periodicals; Medical technology -- Periodicals; 610.28
• DOI: 10.1080/03091902.2017.1293741
• Languages: English
• ISSNs: 0309-1902

Abstract

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.