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Gait as a Biomarker? Accelerometers Reveal that Reduced Movement Quality while Walking is Associated with Parkinson's Disease, Ageing and Fall Risk

Overview of attention for article published in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), January 2014
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Article details
Title
Gait as a Biomarker? Accelerometers Reveal that Reduced Movement Quality while Walking is Associated with Parkinson's Disease, Ageing and Fall Risk
Published in
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), January 2014
DOI 10.1109/embc.2014.6944988
Pubmed ID
Authors
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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The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
India 1 <1%
United Kingdom 1 <1%
Germany 1 <1%
Unknown 140 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 29 20%
Student > Bachelor 16 11%
Researcher 15 10%
Student > Master 14 10%
Professor 6 4%
Other 17 12%
Unknown 46 32%
Readers by discipline
Readers by discipline Count As %
Engineering 27 19%
Medicine and Dentistry 16 11%
Nursing and Health Professions 15 10%
Neuroscience 7 5%
Computer Science 6 4%
Other 16 11%
Unknown 56 39%