Reliable and practical fall prediction using artificial neural network

William Engel, W. Ding
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引用次数: 5

Abstract

The growing elder population has inspired remarkable research in the prevention of fall injuries. A reliable technique to predict fall incidence, along with a corresponding mobile phone app, is proposed in this paper. The technique combines the benefits of traditional medical history based paradigm and non-historical paradigm. The app analyzes single leg motion to predict if the carrying individual is about to fall with a desirably practical alert time, not too long like in the medical history based paradigm, not too short like in the non-historical paradigm. Furthermore, this approach utilizes leg motion instead of torso motion to gain considerable longer alert time. This fall prediction technique will be a perfect fit into a real time automated system for fall prevention.
基于人工神经网络的坠落预测可靠实用
不断增长的老年人口在预防跌倒伤害方面激发了非凡的研究。本文提出了一种可靠的预测跌倒发生率的技术,以及相应的手机应用程序。该技术结合了传统的基于病史的范例和非历史范例的优点。该应用通过分析单腿运动来预测携带者是否即将摔倒,并提供一个理想的实用警报时间,不像基于病史的范式那样太长,也不像非历史范式那样太短。此外,这种方法利用腿部运动而不是躯干运动来获得相当长的警报时间。这种秋季预测技术将是一个完美的适合到一个实时自动化系统预防跌倒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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