Montesinos-Silva, Luis (2019) Ageing and sleep in human balance and falls: the role of wearable sensors and nonlinear signal analysis. PhD thesis, University of Warwick.

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Abstract

Wearable sensors and nonlinear signal analysis methods are empowering innovative ways of assessing balance and fall risk in older adults. However, their adoption in research and clinical practice creates new challenges. This thesis and the studies herein address some of these challenges and provide some insights concerning their optimal use.

Wearable inertial sensors offer the means for developing instrumented versions of clinical balance assessment tools, producing objective and accurate quantitative descriptors on the timing and execution of functional tasks. However, this research proves that selecting an adequate combination of sensor placement, movement task and the measured variable is crucial for discriminating subjects at a higher risk of falling. An optimal protocol for assessing fall risk based on wearable inertial sensors is identified and presented in this thesis.

Additionally, wearable devices offer the means for continuously monitoring physiological and behavioural variables, which can be used to infer outcomes linked to impaired balance and increased risk of falling in older adults. This research shows that wearable devices can be used to capture day-to-day variations in sleep quantity and quality, which in turn produce variations in balance. This situation can potentially expand the prevailing paradigm in fall prevention, from the current one focusing on the occasional assessment of risk factors and changes in the balance control system to a new one also including the continuous monitoring and detection of short-lived factors that might result in an imminent fall.

Finally, this research demonstrates that quantitative descriptors of nonlinear dynamics are more sensitive than linear measures to differences in balance control associated with ageing and risk of falling (e.g. non-fallers and fallers). The adequate selection of the input parameters required for the calculation of nonlinear measures is of paramount importance to achieve positive results. This thesis provides some recommendations for the parameter selection.

Collectively, the findings of this research confirm that wearable sensors and nonlinear signal analysis methods can improve and extend current tools and practices in balance and fall risk assessment.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QP Physiology R Medicine > RC Internal medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering
Library of Congress Subject Headings (LCSH):	Aging, Sleep, Falls (Accidents), Wearable technology, Detectors, Signal processing
Official Date:	August 2019
Dates:	Date Event August 2019 Published
Institution:	University of Warwick
Theses Department:	School of Engineering
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Pecchia, Leandro ; James, Christopher
Sponsors:	Consejo Nacional de Ciencia y Tecnología (Mexico) ; Tecnologico de Monterrey
Format of File:	pdf
Extent:	xviii, 211 leaves: illustrations, charts, plates
Language:	eng

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