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1 Copyright © 2014 Tata Consultancy Services Limited
Data-driven Healthcare using Affordable Sensing
15th Dec 2015
Dr. Arpan PalPrincipal Scientist and Head, Innovation Labs, KolkataTata Consultancy Services Ltd.
GWS 2015 - WaNIoT
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Problems of the New Age and the New World
Developed Countries
Elderly people - 44.7 M (2013), 98M by 2060Invasive and costly
diagnosisOne size fits all
Diagnostic / Treatment protocols
Some diseases yet to have a cure
Developing Countries
Capacity - not enough doctors per patient
Reachability – specialized primary care not available
Affordability - majority cannot afford to pay
the costhttp://www.aoa.acl.gov/Aging_Statistics/index.aspx
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Diagnosis from Symptoms and Signs is still an Art based on aggregate rules - “Diagnosis is the heart of the medical art”
Data-driven systems allows Diagnosis to be Evidence-based than rule based – allows personalization and
adaptation
From Illness to Wellness and From Rule to Evidence
Need to go towards Wellness Driven ModelsAll stakeholders incentivized to keep patients healthy
http://media.cagle.com/107/2012/09/21/119074_600.jpg
Illness Driven model incentivizes people being sick
“The health care system is really designed to reward you for being unhealthy. If you are a healthy person and work hard to be
healthy, there are no benefits.”- Mike Huckabeehttp://www.brainyquote.com/quotes/keywords/
health_care.html#WmKeI72wL5Wg6wqG.99
http://www.greekmedicine.net/diagnosis/Introduction.html
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Data-driven Systems – Sensing and Analytics to the Help
24x7 remote monitoring of activity, physiology
and pathology
Automated generation of alerts on
anomaly
Personalized Prognosis and risk profiling
Discovery of new
diagnosis protocols
Reachable, Affordable Elderly /
Home Care
Personalized Predictive Maintenance of our Body using Internet-of-Things
Sensing Analytics
Reduce Doctor Load,
Improve Capacity
Towards Wellness Driven
Systems
Repeatable DiagnosticsNew Cures
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Internet-of-Things based Remote Sensing and Analytics System
Mobile phone asmedical gateway
TCS Connected Universe Platform
Web Request
PatientRecords
SocialNetwork
HealthcarePortal
Expert DoctorWearables
Nearables – Mobiles, Camera, 3D, Thermal,
…..
Instruments
• Real-time View• Alerts for Medical Emergency • Analytics for Diagnostics / Prognostics
Rural Remote Healthcare –Villages in Chhattisgarh, GujaratHome Monitoring – Hospital in BangaloreElderly People Monitoring –Pilot at Singapore
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Camera• Heart-rate and Respiratory Rate
• Blood PressureMicrophone• Heart Sound• Heart Rate VariabilityAccelerometer, Magnetometer, Gyroscope• Step Count, Activity• Fall Detection
Physiological Sensing on Mobile - Affordable
Heart Rate, BP, HRV, Respiratory Rate
Heart Sound
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Physiological Sensing on Wearable – 24x7
Heart Rate, BP, HRV , SpO2, Respiratory Rate
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Challenges and Results
Heart Rate• Movement Artefacts• Incorrect Placement / Obstruction of Blood FlowBP• Physical Modeling of Cardiovascular Systems
Activity / Fall Detection• Orientation Correction• False Positives from Normal Activity
Solution AccuracyHeart Rate ~2 bpmHRV (SDNN) 89%BP 80% to 85%Activity Classification (Static, Walking, Brisk Walking, Jogging)
90%
Step Count 95%Fall Detection 99% Detection, 92% False
Alarm Removal
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Physiological Sensing using Nearable (Camera / RF) - Unobtrusive
http://www.extremetech.com/extreme/149623-mit-releases-open-source-software-that-reveals-invisible-motion-and-detail-in-video
Fadel Adib et.al., “Smart Homes that Monitor Breathing and Heart Rate”, CHI 2015, Seoul
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Real-time Alert for Anomalies – when to go and see and doctor
Prognosis for CAD – early filtering help for doctors for prescribing more specific tests
Use case - Early Detection of Coronary Artery Disease (CAD)
By 2020, CAD will be the leading cause of death in Western and Asian countries • 20-30% deaths in industrialized countries, 60% of world heart ailments from
India• CAD is a modern epidemic according to WHO• Current method of 3D Angiography costly, obtrusive and harmful to health
Working with doctors at a Cardiac Specialty Hospital in Kolkata
Blood Pressure, Heart Rate, Blood Oxygen from Wearable / Mobile / Nearable
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Cognitive Computing and AI – the Future
Deep LearningDeep QAhttp://www.cbsnews.com/news/jeopardy-winning-computer-now-using-its-brain-for-science/
http://www.slate.com/blogs/future_tense/2012/06/27/google_computers_learn_to_identify_cats_on_youtube_in_artificial_intelligence_study.html
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CAD Alerting and Prognosis - Architecture
Live Patient Data (Sensing) Stored Medical Records
Knowledge Base
Reasoning
Alert Generation
Healthcare Portals, Medical Books, Article
Diagnostic / Prognostic Support
Relevant Data
Evidence based Learning Text Mining
Knowledge Access
Stream Handling
Anomaly Detection Other Filters
Deductive Abductive Others
EntitiesRules
Relations
Multi-variate
association Rule mining
Deep Learning
Cognitive Computin
g
Available Dataset – MIMIC-II– Waveform for 2500 patients matched with medical records - HR, BP, RR, SpO2– Classified into approx. 700 CAD and 1200 non-CAD patients using ICD-9 codes
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Results
Our Method
Given by MIT
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CAD Detection – from PPG and Heart Sound
PPG Windows extracted from 19 subjects (15 nonCAD and 4 CAD)
CAD Predicted
nonCAD Predicted
CAD Diagnosed
91% 9%
nonCAD Diagnosed
23% 77%
Deep Learning based Work under Progress
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Use Case - Tele-Rehabilitation for Stroke Patients
annual cost in EURO in European economy: - twice the cost of cancer
798 billionpeople worldwide need rehabilitation services
do not receive rehabilitationtreatment after discharge
2/31 billion
RehabWeek conference 2015 by NeuroAtHome (http://www.neuroathome.net/p/home.html)
• Existing Quantitative Gait Analysis systems costs approx. Rs. 35 lakhs & not readily available in the market.
• Expensive maintenance costs
• Difficult for patients to frequently visit hospitals
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Solution Architecture
Left Heel: Line of Progression Right Heel: Line of Progression
Store Raw Data
Patient’s Exercise
Parameter
Patient History
Extract Paramete
rs
Working with doctors at a Neuro-Speciality Hospital in Kolkata
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Results
Stride Length estimated using Kinect data and validated w.r.t GaitRite Definition: distance between the heel-points of 2 consecutive footprints of same foot. In Fig 1.: Stride Length = Distance Between Points A,B
Fig. 1. Mean Absolute Deviation
between our estimated stride-length and GaitRite measurement is about 3.084cm.
Single Limb Standing – duration and Jitter Measurement
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Publications and Awards
1) "A Robust Heart Rate Detection using Smart-phone Video", in MobileHealth workshop of Mobihoc 20132) “UbiHeld - Ubiquitous Healthcare Monitoring System for Elderly and Chronic Patient”, in Recognize2Interact Workshop of
UbiComp 20133) “AcTrak - Unobtrusive Activity Detection and Step Counting using Smartphones”, Mobiquitous 20134) "Demo Abstract: HeartSense – Estimating Blood Pressure and ECG from Photoplethysmograph using Smart Phones", SenSys
2013, Italy, Rome. 11-15 Nov. 20135) “Improved heart rate detection using smart phone” In Proceedings of the 29th Annual ACM Symposium on Applied Computing
(ACM-SAC), 20146) "PhotoECG: Photoplethysmography to Estimate ECG Parameters", ICASSP 20147) "Smart Phone Based Blood Pressure Indicator", in MobileHealth workshop of Mobihoc 2014 11-Aug, 2014, Philadelphia, PA,
USA.8) "Estimating Blood Pressure using Windkessel Model on Photoplethysmogram", 36th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society (EMBC '14), Chicago, Illinois, USA on August 26-30, 2014.9) "Effects of Fingertip Orientation and Flash Location in Smartphone Photoplethysmography", Third International Workshop on
Recent Advances in Medical Informatics (RAMI-2014), ICACCI 24-27 Sept. 2014, Delhi.10)"HeartSense: Estimating Heart rate from Smartphone Photoplethysmogram using Adaptive Filter and Interpolation" in 1st
International Conference on IoT Technologies for HealthCare (HealthyIoT, IoT-360), 201411)"Demo Abstract: HeartSense: Smart Phones to Estimate Blood Pressure from Photoplethysmography" in 11th ACM
Conference on Embedded Networked Sensor Systems (SenSys 2014) – Best Demo Award12)"HeartSense: Photoplethysmography to Estimate Physiological Vitals" in The 4th International Conference on the Internet of
Things, 201413)"Noise Cleaning and Gaussian Modeling of Smart Phone Photoplethysmogram to improve Blood Pressure Estimation“,
Presented in ICASSP 201514)“Novel Peak detection to estimate HRV using Smartphone Audio”, presented in Body Sensor Network (BSN) 2015 15)“Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear”, Wearable workshop in Mobisys
2015Aegis Graham Bell Award for Smart Healthcare
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Conclusion
http://sites.duke.edu/makinghealthinfomaticsmeaningfulatduke/2014/04/
When “I” is replaced by
“We”
even “Illness” becomes
“Wellness”
DoctorsScientists and Engineers
