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开源硬件生物医学传感器实时处理库。

Real-Time Processing Library for Open-Source Hardware Biomedical Sensors.

机构信息

Departamento de Tecnología Electrónica, ETS, Ingeniería Informática, Universidad de Sevilla, 41012 Sevilla, Spain.

出版信息

Sensors (Basel). 2018 Mar 29;18(4):1033. doi: 10.3390/s18041033.

DOI:10.3390/s18041033
PMID:29596394
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5949041/
Abstract

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive.

摘要

应用程序涉及从传感器获取数据,需要以预设频率率采样,滤除噪声和/或分析某些频率分量。我们提出了一种基于开源硬件平台的新软件架构,允许程序员从输入通道创建数据流,并轻松实现滤波器和频率分析对象。在低成本平台 Arduino Genuino 中分析了不同类别的性能,包括分配的内存大小和执行时间(时钟周期数)。此外,有 11 人参加了一项实验,他们必须完成多项练习并完成可用性测试。低于 250 Hz 的采样率(许多生物医学应用的典型值)使得可以实时实现滤波器、滑动窗口和傅里叶分析。参与者对软件的可用性评分为 70.2 分(满分 100 分),对实施多个信号处理应用程序的易用性评分略高于 4.4 分(满分 5 分)。参与者表示他们有意使用该软件,因为它被认为是有用的,并且非常易于使用。该库的性能表明,它可能适用于实现小型生物医学实时应用程序或用于人体运动监测,即使在像 Arduino Genuino 这样简单的开源硬件设备中也是如此。关于这个库的总体看法是它易于使用和直观。

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本文引用的文献

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Arduino-based noise robust online heart-rate detection.
J Med Eng Technol. 2017 Apr;41(3):170-178. doi: 10.1080/03091902.2016.1271044. Epub 2017 Jan 12.
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Psycho-physiological training approach for amputee rehabilitation.
Biomed Instrum Technol. 2015 Mar-Apr;49(2):138-43. doi: 10.2345/0899-8205-49.2.138.
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Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones.加速信号的探索性数据分析,以选择智能手机实时活动识别的轻量级和准确特征。
Sensors (Basel). 2013 Sep 27;13(10):13099-122. doi: 10.3390/s131013099.
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Acquisition of biomedical signals databases.生物医学信号数据库的采集。
IEEE Eng Med Biol Mag. 2001 May-Jun;20(3):25-32. doi: 10.1109/51.932721.