Better informing everyday fall risk assessment: experimental studies with contemporary technologies.

Journal: Lancet (London, England)
Published Date:

Abstract

BACKGROUND: Age-related mobility issues and frailty are a major public health concern because of an increased risk of falls. Subjective assessment of fall risk in the clinic is limited, failing to account for an individual's habitual activities in the home or community. Equally, objective mobility trackers for use in the home and community lack extrinsic (ie, environmental) data capture to comprehensively inform fall risk. We propose a contemporary approach that combines artificial intelligence (AI) and video glasses to augment current methods of fall risk assessment.

Authors

  • Jason Moore
    Cedars-Sinai Medical Center, Los Angels, CA 90069, USA.
  • Sam Stuart
    Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom.
  • Peter McMeekin
    Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK.
  • Richard Walker
    Northumbria Healthcare NHS Foundation Trust, Newcastle upon Tyne NE1 8ST, UK.
  • Alan Godfrey
    Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, United Kingdom.