Detection and localisation of hesitant steps in people with Alzheimer's disease navigating routes of varying complexity.

Alzheimer's disease Alzheimer's disease navigating routes IMU acceleration abnormal gait parameters adaptive locomotor responses biomedical measurement corridors counterbalanced repeated-measures design diseases dog-leg gait analysis gait characteristics gait irregularities patient monitoring position data posterior cortical atrophy route complexity shoe-mounted inertial measurement units spatial navigation statistical analysi statistical analysis step hesitancy step time data walking paths

Journal

Healthcare technology letters
ISSN: 2053-3713
Titre abrégé: Healthc Technol Lett
Pays: England
ID NLM: 101646459

Informations de publication

Date de publication:
Apr 2019
Historique:
received: 12 06 2018
revised: 19 12 2018
accepted: 03 01 2019
entrez: 24 5 2019
pubmed: 24 5 2019
medline: 24 5 2019
Statut: epublish

Résumé

People with Alzheimer's disease (AD) have characteristic problems navigating everyday environments. While patients may exhibit abnormal gait parameters, adaptive gait irregularities when navigating environments are little explored or understood. The aim of this study was to assess adaptive locomotor responses of AD subjects in a complex environment requiring spatial navigation. A controlled environment of three corridors was set up: straight (I), U-shaped (U) and dog-leg (S). Participants were asked to walk along corridors as part of a counterbalanced repeated-measures design. Three groups were studied: 11 people with posterior cortical atrophy (PCA), 10 with typical Alzheimer's disease (tAD) and 13 controls. Spatio-temporal gait parameters and position within the corridors were monitored with shoe-mounted inertial measurement units (IMUs). Hesitant steps were identified from statistical analysis of the distribution of step time data. Walking paths were generated from position data calculated by double integration of IMU acceleration. People with PCA and tAD had similar gait characteristics, having shorter steps and longer step times than controls. Hesitant steps tended to be clustered within certain regions of the walking paths. IMUs enabled identification of key gait characteristics in this clinical population (step time, length and step hesitancy) and environmental conditions (route complexity) modifying their expression.

Identifiants

pubmed: 31119037
doi: 10.1049/htl.2018.5034
pii: HTL.2018.5034
pmc: PMC6498402
doi:

Types de publication

Journal Article

Langues

eng

Pagination

42-47

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Auteurs

Ian McCarthy (I)

Pedestrian Accessibility and Movement Environment Laboratory, Department of Civil Environmental and Geomatic Engineering, University College London, London N19 5UN, UK.

Tatsuto Suzuki (T)

Pedestrian Accessibility and Movement Environment Laboratory, Department of Civil Environmental and Geomatic Engineering, University College London, London N19 5UN, UK.

Catherine Holloway (C)

UCL Interaction Centre, Department of Computer Science, University College London, London, UK.

Teresa Poole (T)

Department of Medical Statistics, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, UK.
Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Chris Frost (C)

Department of Medical Statistics, Faculty of Epidemiology and Public Health, London School of Hygiene & Tropical Medicine, London, UK.
Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Amelia Carton (A)

Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Nick Tyler (N)

Pedestrian Accessibility and Movement Environment Laboratory, Department of Civil Environmental and Geomatic Engineering, University College London, London N19 5UN, UK.

Sebastian Crutch (S)

Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Keir Yong (K)

Dementia Research Centre, Institute of Neurology, University College London, London, UK.

Classifications MeSH