A proof-of-concept classifier for acoustic signals from the knee joint on a FPAA

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 f...

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Published inIEEE SENSORS 2016 : Orlando, Florida, USA, October 30-November 2, 2016 : 2016 proceedings papers pp. 1 - 3
Main Authors Shah, Sahil, Teague, Caitlin N., Inan, Omer T., Hasler, Jennifer
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2016
Subjects
Acoustics
Detectors
Feature extraction
Field programmable analog arrays
Injuries
Virtual machine monitors
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Summary: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.
DOI:10.1109/ICSENS.2016.7808748