A proof-of-concept classifier for acoustic signals from the knee joint on a FPAA
A proof-of-concept classifier for acoustic signals from the knee joint on a FPAA
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FPAA 上膝关节声信号的概念验证分类器
DOI：
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发表时间：
2016
期刊：
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通讯作者：
J. Hasler
中科院分区：
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作者：
Sahil Shah;Caitlin N. Teague;O. Inan;J. Hasler
提出了一种基于可重构现场可编程模拟阵列（FPAA）的膝关节声信号评估的低功耗模拟分类器。使用压电（接触）麦克风测量膝关节声音，并使用前端模拟滤波器进行处理。一个单层的神经网络组成的向量矩阵乘法（VMM）和赢家通吃（WTA）用于分类。一个简单的分类检测前交叉韧带损伤在这里实现。这里使用来自单个受试者的健康和受伤膝盖的测量值作为输入。FPAA采用350 nm CMOS工艺制造。采用12个并行滤波器进行特征提取，采用12×2 VMM-WTA作为分类器。编译后的系统、前端和分类器在2.5V电源下的功耗为15.29μW。
A proof-of-concept low-power analog classifier for assessing acoustic signals from the knee joint on a reconfigurable Field Programmable Analog Array (FPAA) is presented in this paper. Knee joint sounds are measured using piezoelectric (contact) microphones and processed using the front end analog filters. A single layer of neural network composed of Vector Matrix Multiplication (VMM) and Winner-Take All (WTA) is used for the classification. A simple classifier detecting an anterior cruciate ligament injury is implemented here. Measurement from a single subject's healthy and injured knees are used here as an input. The FPAA is fabricated in a 350nm CMOS process. A bank of 12 parallel filters is used for feature extraction and a 12×2 VMM-WTA is used as a classifier. The compiled system, front-end and the classifier, consumes a power of 15.29μW with a power supply of 2.5 V.