1. Energy-efficient Long Term Physiological Monitoring
GeM-REM
Energy-efficient Long Term Physiological Monitoring
Ayan Banerjee, Sandeep Gupta
Arizona State University
Sidharth Nabar, Radha Poovendran
University of Washington, Seattle
Clinical Collaborator: Dr. Barbara Cochrane, UW School of Nursing

2. Long Term Monitoring Solution
Sensor
Platform
ECG Leads
Wireless Link
Base Station
Physician
1. Low Energy
2. Small Batteries
3. Low Memory
4. Simple processing
1. Low Bandwidth Usage
2. Robust to Wireless Errors
Storage Efficient
Data Annotation
High Fidelity Data
Quick Review
Keep alive
Sensor Module
Model
Sensed ECG
Compare
Match?
Model
Base Station Module
Raw ECG samples
Align
Output ECG
Raw signal updates
Feature updates
Wireless Link

3. Example Operation 42:1 93% Reduction in data transmission
Periodic Signal Update
Low Battery
Lifetime (3 hrs)[1]
High Storage (7.2 GB per month)
Bandwidth Usage 82 kbps
Scan ______ samples per day
High Fidelity Data, transmitted at 82 kbps
3 lead ECG
1.2
0.4
-0.4
200
400
600
800
1000
1200
1400
Baseline and expected variations (feature updates)
Arrhythmia probability of occurrence = [2] (raw signal update)
Baseline and expected variations (feature updates)
Diagnostically Equivalent Data at kbps
High Fidelity Data at 82 kbps
Diagnostically Equivalent Data at kbps
Prolonged Battery
Life (126 hrs)
Low Storage (175 MB per month)
Low Bandwidth Usage 1.95 kbps
Annotated ECG
GeM-REM
Reduction in data transmission
Diagnostic Feature Accuracy
42:1
93%
[1] Munshi et al. "Wireless ECG plaster for body sensor network," Medical Devices and Biosensors, ISSS-MDBS 2008
[2] Martinelli et al. “Probability of occurrence of life-threatening ventricular arrhythmias in Chagas' disease versus non-Chagas' disease.” PubMed

4. Implementation Details
Generative Model:
Model learned using Matlab curve fitting tool
Learning Accuracy = 2.13%
Devices:
Shimmer ECG
Google donated Nexus
Computation Energy Overhead = 0.8 mW
Code Size = 5KB in RAM and 22 KB in ROM
Application Size = 123 KB
Demo Video: