Literature DB >> 25571356

Gait as a biomarker? Accelerometers reveal that reduced movement quality while walking is associated with Parkinson's disease, ageing and fall risk.

Matthew A Brodie, Nigel H Lovell, Colleen G Canning, Hylton B Menz, Kim Delbaere, Stephen J Redmond, Mark Latt, Daina L Sturnieks, Jasmine Menant, Stuart T Smith, Stephen R Lord.   

Abstract

Humans are living longer but morbidity has also increased; threatening to create a serious global burden. Our approach is to monitor gait for early warning signs of morbidity. Here we present highlights from a series of experiments into gait as a potential biomarker for Parkinson's disease (PD), ageing and fall risk. Using body-worn accelerometers, we developed several novel camera-less methods to analyze head and pelvis movements while walking. Signal processing algorithms were developed to extract gait parameters that represented the principal components of vigor, head jerk, lateral harmonic stability, and oscillation range. The new gait parameters were compared to accidental falls, mental state and co-morbidities. We observed: 1) People with PD had significantly larger and uncontrolled anterioposterior (AP) oscillations of the head; 2) Older people walked with more lateral head jerk; and, 3) the combination of vigorous and harmonically stable gait was demonstrated by non-fallers. Our findings agree with research from other groups; changes in human gait reflect changes to well-being. We observed; different aspects of gait reflected different functional outcomes. The new gait parameters therefore may be complementary to existing methods and may have potential as biomarkers for specific disorders. However, further research is required to validate our observations, and establish clinical utility.

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Year:  2014        PMID: 25571356     DOI: 10.1109/EMBC.2014.6944988

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

1.  Towards motor evaluation of Parkinson's Disease Patients using wearable inertial sensors.

Authors:  Vibha Anand; Erhan Bilal; Bryan Ho; John J Rice
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

2.  IMU-based gait analysis in lower limb prosthesis users: Comparison of step demarcation algorithms.

Authors:  Gerasimos Bastas; Joshua J Fleck; Richard A Peters; Karl E Zelik
Journal:  Gait Posture       Date:  2018-05-22       Impact factor: 2.840

3.  Impact of Parkinson's Disease on Functional Mobility at Different Stages.

Authors:  Sara Mollà-Casanova; Jose Pedrero-Sánchez; Marta Inglés; Juan López-Pascual; Elena Muñoz-Gómez; Marta Aguilar-Rodríguez; Nuria Sempere-Rubio; Pilar Serra-Añó
Journal:  Front Aging Neurosci       Date:  2022-06-15       Impact factor: 5.702

Review 4.  How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review.

Authors:  Erika Rovini; Carlo Maremmani; Filippo Cavallo
Journal:  Front Neurosci       Date:  2017-10-06       Impact factor: 4.677

5.  Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.

Authors:  Sanghee Moon; Hyun-Je Song; Vibhash D Sharma; Kelly E Lyons; Rajesh Pahwa; Abiodun E Akinwuntan; Hannes Devos
Journal:  J Neuroeng Rehabil       Date:  2020-09-11       Impact factor: 4.262

  5 in total

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