Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC

Sanjeev Tannirkulam Chandrasekaran, Sumukh Prashant Bhanushali, Imon Banerjee, Arindam Sanyal

Research output: Contribution to journalArticlepeer-review

Abstract

This work presents a mixed-signal, reservoir-computing neural network (RC-NN) for at-home, real-time health monitoring using intelligent wearable device. The proposed technique is demonstrated on stress detection from electrocardiogram (ECG) signal, and heart diseases detection using a fusion artificial intelligence (AI) model that combines demographic and physiological information. The RC-NN uses a static, random reservoir layer with short-term memory to nonlinearly project input data to high-dimensional plane, and allow easy separation using linear AI model at the output layer. The RC-NN is designed in 65nm CMOS process, and detects stress and heart-diseases with mean accuracies of 92.8% and 86.8% respectively, while consuming 10.97nJ/inference and 2.57nJ/inference respectively.

Original languageEnglish (US)
Pages (from-to)829-839
Number of pages11
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
Volume11
Issue number4
DOIs
StatePublished - Dec 1 2021

Keywords

  • cardiac diseases prediction
  • data fusion and medical wearable
  • health monitoring
  • Machine learning
  • reservoir computing
  • stress detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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