Abstract
Fall injury is one of the biggest risks to health and well-being of the elderly especially in independent living because falling accidents may cause instant death. There are many research interests aimed to detect fall incidents. Fall detection is envisioned critical on ICT-assisted healthcare future. In addition, mobile battery is currently another serious problem in which performance feasibility is considered as a standard to verify an effective method. In this paper, the authors study forward fall detection method from mobile phone perspective using accelerometer only without sacrificing accuracy to save energy. Using peak threshold algorithm in axes of mobile accelerometer, transition from activity of daily living (ADL) to forward fall event is recognized. In collected templates, Dynamic Time Warping (DTW) was applied to compute difference among them with new unlabeled samples. Results implemented on mobile phone easily show the feasibility of the method hence contribute significantly to fall detection in healthcare.
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