Falls amongst the elderly is becoming a major problem with over 50% of elderly hospitalizations due to injury from fall related accidents. Healthcare expenses are dramatically rising due to growing elderly population. Many current technologies for gait analysis are laboratory-based and can incur substantial costs for the healthcare sector for treatment of falls. However utilization of alternative commercially available technologies can potentially reduce costs. Accelerometers are one such option, being ambulatory motion sensors for the detection of orientation and movement. Smart mobile devices are considered as noninvasive and increasingly contain accelerometers for detecting device orientation. This study looks at the capabilities of the accelerometer within a smart mobile device, namely the iPhone, for identification of gait events from walking along a flat surface. The results prove that it is possible to extract features from the accelerometer of an iPhone such as step detection, stride time and cadence. I.

Abstract

Long-term chronic conditions pose a significant challenge in modern healthcare systems in terms of a reduction in quality of life and the increased costs associated with their management. The concept of self-management can help address these issues supported through the introduction of home based assistive and rehabilitation telecare technologies. The proposed solution aims to produce a home-based platform that supports a number of chronic diseases such as Chronic Pain, Congestive Heart Failure and Stroke. The long-term vision of this work is to take into consideration supporting further conditions such as Chronic Obstructive Pulmonary Disease. The system aims to promote behavior change by monitoring activity metrics, performing analysis, which ultimately leads to providing feedback to the user in a meaningful and engaging manner.

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