Citation

Dolatabadi E, Zhi YX, Flint AJ, Mansfield A, Iaboni A, Taati B. Arch. Gerontol. Geriatr. 2019; 82: 200-206.

Affiliation

Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Vector Institute for Artificial Intelligence, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.archger.2019.02.004

PMID

30831526

Abstract

BACKGROUND: Gait and balance functions decline through the course of dementia, and can serve as a marker of changes in physical status and falls risk. We have developed a technology (AMBIENT), based on a vision-based sensor, which enables the frequent, accurate, and unobtrusive measurement of gait and balance.

OBJECTIVE: The objective of this study was to examine the feasibility of using AMBIENT technology for frequent assessment of mobility in people with dementia within an inpatient setting. In particular, we examined technical feasibility, and the feasibility of participant recruitment, data collection and analysis.

METHODS: AMBIENT was installed in a specialized dementia inpatient unit. AMBIENT captured gait bouts as the participants walked within the view of the sensor during their daily routine and computed the spatiotemporal parameters of gait.

RESULTS: Twenty participants (age: 76.9 ± 6.7 years, female: 50%) were recruited over a period of 6 months. We recorded a total of 3843 gait bouts, of which 1171 could be used to extract gait data. On average, 58 ± 47 walking sequences per person were collected over a recording period of 28 ± 20 days. We were able to consistently extract six quantitative parameters of gait, consisting of stride length, stride time, cadence, velocity, step length asymmetry, and step time asymmetry. SIGNIFICANCE: This study demonstrates the feasibility of longitudinal tracking of gait in a dementia inpatient setting. This technology has important potential applications in monitoring functional status over time, and the development of dynamic falls risk assessments.

Copyright © 2019 Elsevier B.V. All rights reserved.

Language: en

Keywords

Dementia; Gait; Longitudinal monitoring; Postural stability; Quantitative; Sensor technology