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As the world population ages, falls among the elderly are becoming a significant burden on healthcare. Fall prevention programs provide solutions for alleviating this burden. Such programs can be supported through monitoring of the elderly with tri-axial accelerometer sensors and mobile technology in order to detect falls and ensure individuals receive rapid care. A six-month pilot program was undertaken that involved recording tri-axial accelerometer data from mobile phones designed to be worn and used by independent community-dwelling elderly individuals. Fall data gained through this pilot program has been analysed in order to determine the quality of data recorded and the feasibility of constructing a threshold based fall detection algorithm from this data. Issues are found with the sample rate and range of the recorded data. Despite this, fall detection of acceptable quality is found to be plausible through measurement of changes in posture.

Evaluation of tri-axial accelerometery data of falls for elderly through smart phone