de la Guia Solaz Manuel, Bourke Alan, Conway Richard, Nelson John, Olaighin Gearoid
Electronics and Computer Engineering Department, University of Limerick, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2423-6. doi: 10.1109/IEMBS.2010.5626244.
The ability to discriminate between falls and activities of daily living (ADL) has been investigated by using tri-axial accelerometer sensors, mounted on the trunk and using simulated falls performed by young healthy subjects under supervised conditions and ADL performed by elderly subjects. In this paper we propose a power-aware real-time fall detection integrated circuit (IC) that can distinguish Falls from ADL by processing the accelerations measured during 240 falls and 240 ADL. In the proposed fixed point custom DSP architecture, a threshold algorithm was implemented to analyze the effectiveness of Programmable Truncated Multiplication regarding power reduction while maintaining a high output accuracy. The presented system runs a real time implementation of the algorithm on a low power architecture that allows up to 23% power savings through its digital blocks when compared to a standard implementation, without any accuracy loss.
通过使用安装在躯干上的三轴加速度计传感器，并利用年轻健康受试者在监督条件下进行的模拟跌倒以及老年受试者进行的日常生活活动（ADL），对区分跌倒和日常生活活动的能力进行了研究。在本文中，我们提出了一种功耗感知型实时跌倒检测集成电路（IC），它可以通过处理在240次跌倒和240次日常生活活动期间测量到的加速度来区分跌倒和日常生活活动。在所提出的定点定制数字信号处理器（DSP）架构中，实现了一种阈值算法，以分析可编程截断乘法在降低功耗同时保持高输出精度方面的有效性。所展示的系统在低功耗架构上运行该算法的实时实现，与标准实现相比，通过其数字模块可实现高达23%的功耗节省，且没有任何精度损失。
