From fall-risk assessment to fall detection: inertial sensors in the clinical routine and daily life

Bagalà, Fabio (2012) From fall-risk assessment to fall detection: inertial sensors in the clinical routine and daily life, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Bioingegneria, 24 Ciclo. DOI 10.6092/unibo/amsdottorato/4842.
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Falls are caused by complex interaction between multiple risk factors which may be modified by age, disease and environment. A variety of methods and tools for fall risk assessment have been proposed, but none of which is universally accepted. Existing tools are generally not capable of providing a quantitative predictive assessment of fall risk. The need for objective, cost-effective and clinically applicable methods would enable quantitative assessment of fall risk on a subject-specific basis. Tracking objectively falls risk could provide timely feedback about the effectiveness of administered interventions enabling intervention strategies to be modified or changed if found to be ineffective. Moreover, some of the fundamental factors leading to falls and what actually happens during a fall remain unclear. Objectively documented and measured falls are needed to improve knowledge of fall in order to develop more effective prevention strategies and prolong independent living. In the last decade, several research groups have developed sensor-based automatic or semi-automatic fall risk assessment tools using wearable inertial sensors. This approach may also serve to detect falls. At the moment, i) several fall-risk assessment studies based on inertial sensors, even if promising, lack of a biomechanical model-based approach which could provide accurate and more detailed measurements of interests (e.g., joint moments, forces) and ii) the number of published real-world fall data of older people in a real-world environment is minimal since most authors have used simulations with healthy volunteers as a surrogate for real-world falls. With these limitations in mind, this thesis aims i) to suggest a novel method for the kinematics and dynamics evaluation of functional motor tasks, often used in clinics for the fall-risk evaluation, through a body sensor network and a biomechanical approach and ii) to define the guidelines for a fall detection algorithm based on a real-world fall database availability.

Tipologia del documento
Tesi di dottorato
Bagalà, Fabio
Dottorato di ricerca
Scuola di dottorato
Scienze e ingegneria dell'informazione
Settore disciplinare
Settore concorsuale
Parole chiave
fall-risk, fall detection, inertial sensors, biomechanical models
Data di discussione
20 Aprile 2012

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