Citation

Di Rosa M, Hausdorff JM, Stara V, Rossi L, Glynn L, Casey M, Burkard S, Cherubini A. Gait Posture 2017; 55: 6-11.

Affiliation

Geriatrics and Geriatric Emergency Care, National Institute of Health and Science on Aging - I.N.R.C.A., Ancona, Italy. Electronic address: a.cherubini@inrca.it.

Copyright

(Copyright © 2017, Elsevier Publishing)

DOI

10.1016/j.gaitpost.2017.03.037

PMID

28407507

Abstract

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Copyright © 2017 Elsevier B.V. All rights reserved.

Language: en

Keywords

Fall risk; Gait analysis; Insole; Pattern analysis; Pressure sensors; Self-learning analysis algorithms