Now showing 1 - 4 of 4
  • Publication
    Digital assessment of falls risk, frailty, and mobility impairment using wearable sensors
    Falls are among the most frequent and costly population health issues, costing $50bn each year in the US. In current clinical practice, falls (and associated fall risk) are often self-reported after the “first fall”, delaying primary prevention of falls and development of targeted fall prevention interventions. Current methods for assessing falls risk can be subjective, inaccurate, have low inter-rater reliability, and do not address factors contributing to falls (poor balance, gait speed, transfers, turning). 8521 participants (72.7 ± 12.0 years, 5392 female) from six countries were assessed using a digital falls risk assessment protocol. Data consisted of wearable sensor data captured during the Timed Up and Go (TUG) test along with self-reported questionnaire data on falls risk factors, applied to previously trained and validated classifier models. We found that 25.8% of patients reported a fall in the previous 12 months, of the 74.6% of participants that had not reported a fall, 21.5% were found to have a high predicted risk of falls. Overall 26.2% of patients were predicted to be at high risk of falls. 29.8% of participants were found to have slow walking speed, while 19.8% had high gait variability and 17.5% had problems with transfers. We report an observational study of results obtained from a novel digital fall risk assessment protocol. This protocol is intended to support the early identification of older adults at risk of falls and inform the creation of appropriate personalized interventions to prevent falls. A population-based approach to management of falls using objective measures of falls risk and mobility impairment, may help reduce unnecessary outpatient and emergency department utilization by improving risk prediction and stratification, driving more patients towards clinical and community-based falls prevention activities.
      324Scopus© Citations 29
  • Publication
    Sensor-based Assessment of Falls Risk of the Timed Up and Go in Real-World Settings
    Falls are the leading cause of older adult injury and cost $50bn annually. New digital technologies can quantitatively measure falls risk. Objective is to report on a validated wearable sensor-based Timed Up and Go (QTUG) assessment detailing 11 measures of falls risk, frailty and mobility impairment in older adults in six countries in 38 clinical and community settings.
  • Publication
    How Many Steps to Represent Individual Gait?
    Assessing and reproducing user's mobility has multiple purposes for interactive systems. In particular, the quantification of gait parameters has been used for user modelling, virtual environments, and augmented reality. While many technologies can be used to assess gait, measuring spatio-temporal parameters and their fluctuations, it is important to evaluate how many steps are necessary to represent the gait pattern of an individual, in order to provide better feedback to the user and improve user experience. In this preliminary study, we evaluate the intra-session reliability of spatio-temporal gait parameters for 24 healthy adults walking two trials of 15m in a corridor. Angular velocity data were acquired from body-worn inertial measurement units attached to participants' right and left shanks. An adaptive algorithm was applied for gait event detection, and gait parameters were analyzed according to pre-defined numbers of steps extracted from the full length of the trial. The main contribution of the present analysis is to present a method of gait event detection, segmentation and analysis that can be used for adjusting interactive systems to individual users.
      237Scopus© Citations 3
  • Publication
    Short Bouts of Gait Data and Body-Worn Inertial Sensors Can Provide Reliable Measures of Spatiotemporal Gait Parameters from Bilateral Gait Data for Persons with Multiple Sclerosis
    Background: Wearable devices equipped with inertial sensors enable objective gait assessment for persons with multiple sclerosis (MS), with potential use in ambulatory care or home and community-based assessments. However, gaitdata collected in non-controlled settings is often fragmented and may not provide enough information forreliable measures. We evaluate a novel approach, extracting pre-defined numbers of gait cycles from the fulllength of a walking task, and their effects on the reliability of spatiotemporal gait parameters. Methods: The present study evaluates intra-session reliability of spatiotemporal gait parameters for short bouts of gaitdata extracted from the full length of the walking tasks to 1) determine the effects of the length of the walkingtask on the reliability of calculated measures and 2) identify spatiotemporal gait parameters that can providereliable measures for gait assessments and reference data in different settings. Thirty-seven participants (37) diagnosed with relapsing-remitting MS (EDSS rage 0 to 4.5) executed two trials,walking 20m each, with inertial sensors attached to their right and left shanks. Previously published algorithms were applied to identify gait events from the medio-lateral angular velocity. Short bouts of gait data wereextracted from each trial, with lengths varying from 3 to 9 gait cycles. Twenty-one measures of spatiotemporalgait parameters were calculated. Intraclass correlation coefficients (ICCs) were calculated to evaluate how the degree of agreement between the two trials of each participant varied with the number of gait cycles included inthe analysis. Results: Spatiotemporal gait parameters calculated as the mean across included gait cycles reach excellent reliabilityfrom three gait cycles. Stride time variability and asymmetry, as well as stride velocity variability and asymmetry, reach good reliability from six gait cycles and should be further explored for persons with MS, whilestride time asymmetry and step time asymmetry do not seem to provide reliable measures and should bereported carefully. Conclusion: Short bouts of gait data, including at least six gait cycles of bilateral data, can provide reliable gait measurements for persons with MS, opening new perspectives for gait assessment using wearable devices in non-controlled environments, to support monitoring of symptoms of persons with neurological diseases.