On Fall Detection Using Smartphone Sensors

S. Biswas, Tanima Bhattacharya, Ramesh Saha
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引用次数: 5

Abstract

In today’s world there is rapid growth of technology in medical field. Body Sensor Networks are being used hugely for remote monitoring and support for people in need e.g. elderly, children, patients etc. Besides monitoring significant physiological parameters, posture and fall detection related to health monitoring has gained immense popularity. This work focuses on fall detection using accelerometer data as almost all people are nowadays carrying smartphones. A challenge lies in this field i.e. detecting sudden fall of an elderly or a patient because this needs immediate support. Delay can cause havoc to the person in need. This work basically aims to identify fall uniquely. An environment where the proposed algorithm can be deployed is proposed. Accuracy calculation of proposed technique is also given in support.
关于使用智能手机传感器进行跌倒检测
当今世界,医学领域的技术发展迅速。身体传感器网络被广泛用于远程监测和支持有需要的人,如老人、儿童、病人等。除了监测重要的生理参数外,与健康监测相关的姿势和跌倒检测也得到了广泛的普及。这项工作的重点是使用加速度计数据进行跌倒检测,因为现在几乎所有人都携带智能手机。这一领域存在一个挑战,即检测老年人或患者的突然跌倒,因为这需要立即支持。拖延会对有需要的人造成严重破坏。这项工作基本上是为了独特地识别秋天。提出了一种可部署该算法的环境。并给出了该方法的精度计算。
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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