gov. gray davis institute for science and innovation · 2018. 11. 12. · endura optimizer . gait...

Calit2

Gov. Gray

Davis

Institute

for Science

and

Innovation

Calit2: Path Forward

The Digital Transformation of

Health • California is a Microcosm of American Health Care

Challenges

– Increasing Chronic Illness as Population Ages

– Skyrocketing Health Care Costs & Tight Budgets

• Digital Transformation of Health Care is Underway

– Explosion of Health Care Data

– Increased Data from Biomedical Devices

– Greater Genomic and Proteomic Understanding

• New Generation of Health Care Will Focus on

Wellness

– Increased Focus on Children’s Health

– Real Time Monitoring for Preventive Intervention and

Behavioral Modification to Enhance Wellness

• Calit2 will Lead Collaborations with Engineers,

Clinicians, Physicians, and Social Science to

Prototype the Infrastructure Necessary to Realize

This Vision

IT in the Doctor’s Office

4

Timeline-Based Visualization of Patient-Provider

Interaction Patterns from Coded Video

Alan Calvitti, PhD,1 Zia Agha, MD, MS,1 Debra Roter, PhD,2 Barbara

Gray, MA,1 Neil Farber, MD,1, Danielle Zuest, MA1

HSRD VA San Diego Healthcare System & Dept. of Med. UCSD.1

Dept. Health Policy and Management, Johns Hopkins School of

Public Health, Baltimore, MD.2

CureTogether -IT for Selfhelp

5

QUALCOMM Life

LONOCLOUD Proprietary & Confidential

LonoCloud Federation of Nodes for Smart and Connected Healthcare

7

Nationwide

Network of

Patient Sensors

Data Collection

& Ingest Lono Nodes

Patient Self-Service

Reporting Portal

Electronic Medical Records

Patient History

Lab Data

Patient Genetic Information

Data Fabric &

Analytics

Processing

Clinical

Researchers

Cloud Monitoring

Dashboard

Data Fabric &

Reporting

Database Gateway

Integration to Fabric

Physicians &

Hospitals

On-Premise Data &

Reporting & Analysis

Lono Edge Node

Operations Center

Policy Makers

Data Fabric &

Reporting

Tom Caldwell, Ingolf Krueger

www.lonocloud.com

LONOCLOUD Proprietary & Confidential

Remote sensors Advanced Monitoring systems provide: 1. Wireless or remote patient monitoring to

share data outside the immediate patient care area.

2. Features: Basic Remote Tracking 3. Face to Face Interaction Patient – Clinician 4. Data sorting vast amounts placed in context of patient conditions

LonoCloud for mHealth – the future is here

LonoCloud

8

Tom Caldwell, Ingolf Krueger

www.lonocloud.com

Sensing Devices and Systems

As worn

Gas exchange

monitors

ECG Holter

monitor

Accelerometers

Suunto: Heart

Rate monitor,

foot pod,

temperature and

altitude

Polar: Heart

Rate, stride

and cadence

sensors and

GPS

GoWear: Calorie

burn, Galvanic

Skin Response,

near and far skin

temperatures,

step sensors

RF ID

Algorithmically Derived Metrics

• Excess Post Exercise Oxygen

Consumption

– Disruption of Homeostasis,

Training Effect

• VO2 max

– Adaptation to endurance

• Polar Running Index

– Race completion times

• Heart-Breath Synchronicity

– Relaxation and Meditation

• ZQ

– Quality of Sleep

• FirstBeat athlete

– Stress and Recovery

Refining a Run

Empty Stomach

Mashed Potatoes

Energy Gels

Endura Optimizer

Gait Analysis: April 17th, 2010

13

Long term trends

A finer look at Yoga

And Music?

16

Ra Ma Da Sa Sa Say So Hung

Monkey Brain

Akaal Hari. Apaal Hari…

Entrainment (N=2)

17

Wavelet coherence

N signals Wavelet coherence

Coherence Network connections

• A3: Shoulder shrugs (2 minutes), LF and HF

Coherence Network connections

• Interlude, LF and HF

Coherence Network connections

• A4: Pressure against nose bridge (3.5 minutes),

• LF and HF

Coherence Network connections

• Interlude, LF and HF

Coherence Network connections

• Act 5: Ek Ong Kar, Sat Nam Siri, Whahay Guru

• 11 min.

• LF and HF

Coherence Network connections

• Interlude, LF and HF

Coherence Network connections

• Act. 7: Ek Ong Kar, Sat Gurprasad, Sat Gurprasad, Ek Ong Kar

• 11 minutes

• LF and HF

Real Time

healthware.ucsd.edu

Estimating (Heart) Age

29

Relations between age and HRV determined by

SDNN index (A), rMSSD (B) and pNN50 (C) in

healthy subjects.

• Solid lines = fitted regression lines and

upper and lower 95% confidence limits.

• Dashed lines = published cutpoints for

increased risk of mortality (SDNN index 30

ms, rMSSD 15 ms, pNN50 0.75%).

"Twenty-Four Hour Time Domain Heart Rate

Variability and Heart Rate: Relations to Age

and Gender Over Nine Decades,"

K Umetani, DH Singer, R McCraty, and M

Atkinson, J. Am. Coll. Cardiol. 1998;31;593-

601

• Yoga and Meditation

– Sensing the heart beat

– Sensing pulsations of

blood flow

– Ento-optic patterns

– Sensing electrical

discharge

Sensations to Sense (Algorithmically?)

• Running

– Patterns of Breath

– Shift in Gait

– Runny Nose

– Retching

– Loss of circulation and

numbness in (left) arm

– Knees buckle

30

SenseWear™ activity monitor (3-axis accelerometry, skin temp, GSR, and more), 23 hrs/day

Polar Team™ heart rate monitor, 12-23 hrs/day

Philips-Respironics Actiwatch™, 12-23 hrs/day

Dexcom 7+™ continuous glucose monitor, 24 hrs/day (research CGM had “blinded” display; this subject also wore his own CGM during the study)

Insulin pump, 24 hrs/day (subjects wore their own pumps, in this case OmniPod™ with site on lower back, pictured)

Zeo™ sleep monitor headband, worn from bedtime to waking

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6/20/2011 19:12 6/21/2011 0:00 6/21/2011 4:48 6/21/2011 9:36 6/21/2011 14:24 6/21/2011 19:12 6/22/2011 0:00

BG

BPM

Bolus

Snack

Meal

METs

DMITRI pilot study: Nate Heintzman

Calit2: At the Cutting Edge

Ramesh Rao,

Director,

UCSD Division

Calit2

Professor of

Electrical and

Computer

Engineering

Jacobs School of

Engineering