Abstract : Every year, more than 37 million falls that require medical attention occur. The elderly suffers the greatest number of fatal falls. Therefore, automatic fall detection for the elderly is one of the most important health-care applications as it enables timely medical intervention. The fall detection problem has extensively been studied over the last decade. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable algorithms with feasible computational cost is still an open research challenge. In this paper, a low-cost highly accurate machine learning-based fall detection algorithm is proposed. Particularly, a novel online feature extraction method that efficiently employs the time characteristics of falls is proposed. In addition, a novel design of a machine learning-based system is proposed to achieve the best accuracy/numerical complexity tradeoff. The low computational cost of the proposed algorithm not only enables to embed it in a wearable sensor but also makes the power requirements quite low and hence enhances the autonomy of the wearable device, where the need for battery recharge/replace is minimized. Experimental results on a large open dataset show that the accuracy of the proposed algorithm exceeds 99.9% with a computational cast of less than 500 floating point operations per second.
