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Detection of Gait Patterns in Challenging Environments
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Author(s): Kate Lynch (Melbourne University, Australia), Daniel T.H. Lai (Victoria University, Canada)and Rezaul Begg (Melbourne University, Australia)
Copyright: 2008
Pages: 9
Source title:
Encyclopedia of Healthcare Information Systems
Source Author(s)/Editor(s): Nilmini Wickramasinghe (Illinois Institute of Technology, USA)and Eliezer Geisler (Illinois Institute of Technology, USA)
DOI: 10.4018/978-1-59904-889-5.ch054
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Abstract
One in three individuals over the age of 65 years (elderly) will fall at least once a year (James, 1993). This probability increases to one in two adults over 80 years (DHA, 2005). Consequently, gait modifications associated with ageing have been linked with increased falls’ probability (Berg, Alessio, Mills, & Tong, 1997; Lord, Sherrington, & Menz, 2001). Despite an increasing research interest in recent times into the aetiology of falls, particularly in the elderly (>65 years), falls continue to be a major public health concern in Australia and worldwide. Fall-related injuries are the leading cause of accidental death in the elderly population, and account for the largest cause of hospitalisation for this population (Lord et al., 2001), with many elderly individuals experiencing physical, social, or functional ramifications following a fall. Consequently, the economic cost of falls to the public health system is escalating, with the total cost of fall injuries reported to be higher than road traffic injuries (DOH, 2004). The majority of falls associated costs include physician consultation, hospital stays, nursing homecare, rehabilitation, medical equipment, home modification and care, community based services, and prescription drugs and administration (DOH, 2004; Lord et al., 2001). Healthcare and related costs associated with falls are expected to double over the next 50 years (Close & Lord, 2006).
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