In the elderly population, falls are amongst the most frequent and dangerous causes of accidental injuries. It is estimated that more than 1 in 3 persons over the age of 65 are victims to falls each year. Proceeding an accidental fall victims are often injured or immobilized. In the elderly population over 72 years of age, approximately 47% of the injured victims are unable to regain upright stance. Without assistance, fallers often remain on the ground. Long-lie is the term used to describe fall-induced immobilization of victims for extended durations of time. In 2004 it was estimated that 20% of fall-induced hospitalizations were associated with Long-lie. In the same statistical report, Long-lie following a fall-induced injury is closely linked to cases of mortality amongst the elderly. An automated system that can reduce duration of fall- induced Long-lie or even frequency of falls will therefore be greatly beneficial. The objective of this project is to engineer a fall-detection system that will i) accurately detect a fall, ii) prevent falls if possible by alerting the user of unstable physical orientations and iii) send out a distress signal to a handheld device or other communication devices wirelessly. The device is to be a compact unit worn around the waist and will communicate wirelessly via Bluetooth.
Nguyen, Binh and Tomkun, Jonathan, "Hybrid System for Fall Detection & Fall Prevention" (2010). EE 4BI6 Electrical Engineering Biomedical Capstones. Paper 33.