Welcome to the Aging Services Technologies Laboratory

The Aging Services Technologies Laboratory is an interdisciplinary research lab focused on developing innovative technology and systems that improve elderly people’s quality of life.

Our vision is to create the technological foundation for maintaining a high quality of life as people age.

Our mission is to develop technology that will:

Increase quality of lifeDecrease health-care costsBe applicable to our soldiers, veterans and people with disabilities as well

Partnership Opportunities Partnership opportunities are available for companies, institutions and investors.

QuickTime™ and a decompressor

are needed to see this picture.

QoLT Lab QoLT Lab

increasingincreasing youryour QoLQoL

VisionVision

To become a global leader in performing research and innovating technologies in increasing the quality of life with aging.

To become a global leader in performing research and innovating technologies in increasing the quality of life with aging.

QoLT Lab QoLT Lab

increasingincreasing youryour QoLQoL

TeamTeamTeamTeam• Lakshman Tamil, Electrical Engineering

• Architecture, radio and overall management of the project

• Subhash Banerjee, M.D., UTSW Medical Center• Cardiology

• Gopal Gupta, Computer Science• Software

• Larry Amman, Mathematics & Statistics• Statistical analysis & Modeling

• Mehrdad Nourani, Electrical Engineering• Hardware, integration & testing, ASIC/SoC design

• Hlaing Minn, Electrical Engineering• Communication hardware design and modeling

• Vincent Ng, Computer Science• Machine learning

• Lakshman Tamil, Electrical Engineering• Architecture, radio and overall management of the project

• Subhash Banerjee, M.D., UTSW Medical Center• Cardiology

• Gopal Gupta, Computer Science• Software

• Larry Amman, Mathematics & Statistics• Statistical analysis & Modeling

• Mehrdad Nourani, Electrical Engineering• Hardware, integration & testing, ASIC/SoC design

• Hlaing Minn, Electrical Engineering• Communication hardware design and modeling

• Vincent Ng, Computer Science• Machine learning

Generic Body Area Network (BAN)

Generic Body Area Network (BAN)

• Several non-invasive sensors worn on body• Vital signs data collected and passed (via

gateway) to system database• Database stores, processes, analyzes data, and

takes action if required

• Several non-invasive sensors worn on body• Vital signs data collected and passed (via

gateway) to system database• Database stores, processes, analyzes data, and

takes action if required

QoLT Lab QoLT Lab

increasingincreasing youryour QoLQoL

InternetInternet

Gateway

Monitoring center

Doctor’s office

Remote Monitoring of Vital SignsRemote Monitoring of Vital Signs

QoLT Lab QoLT Lab

increasingincreasing youryour QoLQoLApril 19, 2023 QuBIT Lab's Proprietary 6

Symptom Recognition

Pre-Hospital

Delay in Initiation of Therapy

ED Cath LabCall to Medical System

Increasing Loss of Heart Muscle

Treatment Delayed is Treatment DeniedTreatment Delayed is Treatment Denied

Individual with Chest Pain (CP)

Biggest Challenge

5 min < 30 min < 90 min

Current Practice Standard

30 ± 2.3 h

Generic Sensor Node Implementation

Generic Sensor Node Implementation

ECG Sensor Node ImplementationECG Sensor Node Implementation

• Plug-and-Play ECG sensor node for Body Area Network• Connected to PC via USB port

System Backend View of Signal

System Backend View of Signal

ECG Signal ProcessingECG Signal Processing• ECG preprocessing and feature extraction• LabVIEW’s wavelet toolset used

• ECG preprocessing and feature extraction• LabVIEW’s wavelet toolset used

Baseline Wandering Removal

Wide-band Noise

SuppressionECG Records

Denoising

QRS Complexes Extraction

Fiducial Point Extraction

Preprocessed ECG Data • Wavelet peak and valley detector

• Adaptive Thresholding • Search-back algorithm for possible

missed peaks • Valleys right before and after each

peak (R) determine Q and S points

• Wavelet Detrend • Wavelet Analysis sym5 wavelet

To ECG Beat Classification

Original ECG Signal

Denoised ECG Signal and QRS complexes

marked

Preprocessed ECG Data

• Overall accuracy of 99.51% achieved on MIT-BIH Arrhythmia Database

QRS Duration

ST Segment

ST Interval

PR

PR Interval

Mean R-peak Average RR IntervalMean Power Spectral Density Autocorrelation ValueArea under QRS

One Feature Vector for each Heart Beat

Heart Beat Classification Module Using Heart Beat Classification Module Using Support Vector MachineSupport Vector Machine

Classified Heart Beats

Learning Algorithm-- Support Vector Machine

QoLT Lab QoLT Lab

increasingincreasing youryour QoLQoLApril 19, 2023 QuBIT Lab's Proprietary 12

Thank YouThank You