affordable sensing and diagnosis for...
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1 Copyright © 2014 Tata Consultancy Services Limited
Affordable Sensing and Diagnosis for HealthcareAffordable Sensing and Diagnosis for Healthcare
10th Sept2015
Dr. Arpan PalPrincipal Scientist and Head, Innovation Labs, KolkataTata Consultancy Services Ltd.
IBSS 2015
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AgendaAgenda
Problems in Modern HealthcareProblems in Modern Healthcare
Technology Solutions Technology Solutions –– Internet of Things (Sensing and Internet of Things (Sensing and
Analytics)Analytics)
Example Scenarios Example Scenarios –– Coronary Artery Disease Detection Coronary Artery Disease Detection
and Stroke Patient Rehaband Stroke Patient Rehab
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Problems of the New Age and the New WorldProblems of the New Age and the New WorldProblems of the New Age and the New World
http://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 rulebased – allows personalization and adaptation
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 rulebased – allows personalization and adaptation
From Illness to Wellness and From Rule to EvidenceFrom Illness to Wellness and From Rule to EvidenceFrom Illness to Wellness and From Rule to Evidence
Need to go towards Wellness Driven ModelsAll stakeholders incentivized to keep patients healthy
Need to go towards Wellness Driven ModelsAll stakeholders incentivized to keep patients healthy
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 Huckabee
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 Huckabee
http://www.brainyquote.com/quotes/keywords/health_care.html#WmKeI72wL5Wg6wqG.99
http://www.greekmedicine.net/diagnosis/Introduction.html
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https://books.google.co.in/books?id=qW4IKSfM8x8C&pg=RA1-PA551&lpg=RA1-PA551&dq=from+symptoms+to+diagnosis+art+or+science&source=bl&ots=UHqeP9eJev&sig=9E5_SjS_Bj48XHvT71KzbH6Gsyw&hl=en&sa=X&ved=0CEoQ6AEwBmoVChMIt-jptLUxwIVg0mOCh14GQ_a#v=onepage&q=from%20symptoms%20to%20diagnosis%20art%20or%20science&f=false
From Signs and Symptoms to Diagnosis of DiseaseFrom Signs and Symptoms to Diagnosis of DiseaseFrom Signs and Symptoms to Diagnosis of Disease
Affordable SensingAffordable SensingAffordable Sensing
Affordable Sensing
Affordable Affordable SensingSensing Sensor Data
AnalyticsSensor Data Sensor Data
AnalyticsAnalytics
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Internet‐of‐Things based Remote Sensing and Analytics SystemInternetInternet‐‐ofof‐‐Things based Remote Sensing and Analytics SystemThings based Remote Sensing and Analytics System
Mobile phone asmedical gateway
TCS Connected Universe Platform
Web Request
PatientRecords
SocialNetwork
HealthcarePortal
Expert DoctorMobiles Wearables
Nearables – 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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Data‐driven Healthcare ‐ TrendsDataData‐‐driven Healthcare driven Healthcare ‐‐ TrendsTrends
http://hitconsultant.net/2015/03/16/infographic-sxsw-health-tech-trends/
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Physiological Sensing on MobilePhysiological Sensing on MobilePhysiological Sensing on Mobile
Solution Accuracy
Heart Rate in Mobile ~2 bpm
Blood Pressure 92% in healthy subjects, 85% in patients
HRV (SDNN) 89%
Activity Classification (Walking, Brisk Walking, Jogging)
80%
Fall Detection 92% with reduced false alarms
Step Count 95%
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Physiological Sensing on WearablePhysiological Sensing on WearablePhysiological Sensing on Wearable
Heart Rate, BP, Heart Rate, BP, HRVHRV , SpO2, Respiratory Rate, SpO2, Respiratory Rate
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Signal Processing ChallengesSignal Processing ChallengesSignal Processing Challenges
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Mobiles Wearables
Nearables – Camera, 3D, Thermal, …..
Disease Detection and Control• Chronic / Elderly Patient @Home• Heart Patient @Home / Hospital• Villagers @ Rural Health center
Care GiverDoctor @ Hospital
Solution ArchitectureSolution ArchitectureSolution Architecture
• Coronary Artery Disease• Stroke Rehab
Instruments
Prevent Disease, Promote Wellness, InclusivePrevent Disease, Promote Wellness, InclusivePrevent Disease, Promote Wellness, Inclusive
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Use case ‐ Early Detection of Coronary Artery Disease (CAD)Use case Use case ‐‐ Early Detection of Coronary Artery Disease (CAD)Early Detection of Coronary Artery Disease (CAD)
y 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 IndiaCAD is a modern epidemic according to WHOCurrent method of 3D Angiography costly, obtrusive and harmful to health
y 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 IndiaCAD is a modern epidemic according to WHOCurrent method of 3D Angiography costly, obtrusive and harmful to health
Working with doctors at a Cardiac Specialty Hospital in KolkataWorking with doctors at a Cardiac Specialty Hospital in Kolkata
Sensor based System Sensor based System –– Blood Pressure, Heart Rate, Blood Oxygen from Wearable / MobileBlood Pressure, Heart Rate, Blood Oxygen from Wearable / Mobile
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Cognitive Computing and AI – the FutureCognitive Computing and AI Cognitive Computing and AI –– the Futurethe Future
Deep LearningDeep LearningDeep QADeep QA
tp://www.cbsnews.com/news/jeopardy-winning-computer-now-using-its-brain-for-ience/
http://www.slate.com/blogs/future_tense/2012/06/27/google_computers_learnidentify_cats_on_youtube_in_artificial_intelligence_study.html
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Alerting and Prognosis for CADAlerting and Prognosis for CADAlerting and Prognosis for CAD
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
vailable 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
Working with doctor at a Cardiac Specialty Hospital in KolkataWorking with doctor at a Cardiac Specialty Hospital in Kolkata
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Use Case ‐ Tele‐Rehabilitation for Stroke PatientsUse Case Use Case ‐‐ TeleTele‐‐Rehabilitation for Stroke PatientsRehabilitation for Stroke Patients
nnual cost in EURO in European conomy: - twice the cost of cancer
798 billion798 billionpeople worldwide needrehabilitation services
do not receive rehabilitationtreatment after discharge
2/32/31 billion1 billion
abWeek conference 2015 by NeuroAtHome (http://www.neuroathome.net/p/home.html)
• Existing Quantitative Gait Analysis systems (Goniometers, markers, VICON system) costs approx. $200K & not readily available in the market. Expensive maintenance costs
• Difficult for patients to frequently visit hospitals
• Existing Quantitative Gait Analysis systems (Goniometers, markers, VICON system) costs approx. $200K & not readily available in the market. Expensive maintenance costs
• Difficult for patients to frequently visit hospitals
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Low cost, affordable for home use
Ease of Access
Fun @ Exercise
Improved Outcome
Affordableand
Reliable
Proposed Kinect based SolutionProposed Kinect based SolutionProposed Kinect based Solution
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Solution ArchitectureSolution ArchitectureSolution Architecture
Left Heel: Line of Progression Right Heel: Line of Progression
Store Raw Data
Patient’s Exercise
Parameter
Patient History
Extract Parameters
Working with doctor at a Neuro-Speciality Hospital in KolkataWorking with doctor at a Neuro-Speciality Hospital in Kolkata
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PublicationsPublicationsPublications
Arpan Pal, Aishwarya Visvanathan, Aniruddha Sinha, Anirban Duttachoudhury, Tanushyam Chattopadhyay, "A Robust Heart Rate Detection usSmart‐phone Video", in MobileHealth workshop of Mobihoc 2013Avik Ghose, Amit Agrawal, Priyanka Sinha, Anirban Duttachoudhury, Chirabrata Bhaumik, Aniruddha Sinha, “UbiHeld ‐ Ubiquitous HealthcMonitoring System for Elderly and Chronic Patient”, in Recognize2Interact Workshop of UbiComp 2013Avik Ghose, Vivek Chandel, Anirban Duttachoudhury, Chirabrata Bhaumik, “AcTrak ‐ Unobtrusive Activity Detection and Step Counting usSmartphones”, Mobiquitous 2013Anirban Duttachoudhury, Aishwarya Visvanathan, Rohan Banerjee, Aniruddha Sinha, Arpan Pal, Chirabatra Bhaumik, Anurag Kumar, "DeAbstract: HeartSense – Estimating Blood Pressure and ECG from Photoplethysmograph using Smart Phones", SenSys 2013, Italy, Rome. 11‐15 N2013Arpan Pal, Aishwarya Visvanathan, Anirban Dutta Choudhury, and Aniruddha Sinha. 2014. Improved heart rate detection using smart phoneProceedings of the 29th Annual ACM Symposium on Applied Computing (ACM‐SAC), pp. 8‐13Rohan Banerjee, Aniruddha Sinha, Anirban Dutta Choudhury, Aishwarya Visvanathan, "PhotoECG: Photoplethysmography to Estimate EParameters", ICASSP 2014Aishwarya Visvanathan, Rohan Banerjee, Anirban Duttachoudhury, Aniruddha Sinha, "Smart Phone Based Blood Pressure Indicator", in MobileHeaworkshop of Mobihoc 2014 11‐Aug, 2014, Philadelphia, PA, USA.Rohan Banerjee, Anirban Duttachoudhury, Aniruddha Sinha, "Estimating Blood Pressure using Windkessel Model on Photoplethysmogram", 3Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '14), Chicago, Illinois, USA on August 26‐30, 2014.Aishwarya Visvanathan, Rohan Banerjee, Aditi Misra, Anirban Dutta Choudhury and Arpan Pal, "Effects of Fingertip Orientation and Flash LocationSmartphone Photoplethysmography", Third International Workshop on Recent Advances in Medical Informatics (RAMI‐2014), ICACCI 24‐27 Se2014, Delhi.
0) "HeartSense: Estimating Heart rate from Smartphone Photoplethysmogram using Adaptive Filter and Interpolation" in 1st International Confereon IoT Technologies for HealthCare (HealthyIoT, IoT‐360), 2014
1) Banerjee, Rohan et al. "Demo Abstract: HeartSense: Smart Phones to Estimate Blood Pressure from Photoplethysmography" in 11th AConference on Embedded Networked Sensor Systems (SenSys 2014) – Best Demo Award
2) Banerjee, Rohan et al. "HeartSense: Photoplethysmography to Estimate Physiological Vitals" in The 4th International Conference on the InternetThings, 2014
3) Banerjee, Rohan et al. "Noise Cleaning and Gaussian Modeling of Smart Phone Photoplethysmogram to improve Blood Pressure EstimatioPresented in ICASSP 2015
4)MISRA Aditi et al., “Novel Peak detection to estimate HRV using Smartphone Audio”, presented in Body Sensor Network (BSN) 2015 5) Nasim Ahmed et al. “Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear”, Wearable workshop in Mobi2015
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PatentsPatentsPatents
1) Vivek Chandel, Anirban Dutta Choudhury “ACCELERATION‐BASED STEP ACTIVITY DETECTION AND CLASSIFICATION ON MOBILE DEVICES” IndiaComplete Specification 1875/MUM/2013 on 3‐April‐2013
2) Arpan Pal, Aniruddha Sinha, Aishwarya Visvanathan, Anirban Dutta Choudhury, Tanushyam Chattopadhyay, "MEASUREMENT OF PHYSIOLOGICAPARAMETERS", Indian Complete specification 2489/MUM/2013 on 26‐July 2013
3) VISVANATHAN, Aishwarya; SINHA, Aniruddha; PAL, Arpan; BANERJEE, Rohan, "MONITORING PHYSIOLOGICAL PARAMETERS", Indian Complespecification 3152/MUM/2013 on 3‐Oct 2013
4) VISVANATHAN, Aishwarya; SINHA, Aniruddha; PAL, Arpan; BANERJEE, Rohan; DUTTA CHOUDHURY, Anirban, "MONITORING PHYSIOLOGICAPARAMETERS", Indian Complete Specification 1540/MUM/2014 on 2‐May 2014
5) ROHAN BANERJEE, Anirban Dutta Choudhury, ANIRUDDHA SINHA, "MEASURING BLOOD PRESSURE", Indian complete Application N2593/MUM/2014, Filing Date : 11‐Aug 2014
6) VISVANATHAN, Aishwarya et al., “Region of Interest based PPG enhancement of smartphone video”, Provisional Specification 3396/MUM/20147) BANERJEE, Rohan et al., Noise Cleaning of Smart Phone PPG signal to estimate Blood Pressure”, Indian complete Application No. 1684/MUM/2018) MISRA Aditi et al., “A System and Method for estimating heart rate and heart rate variability using heart sounds collected from smartphon
microphones using envelope detection”, Indian Patent Application No. 1544/MUM/2015
