mobisys io t_health_arpanpal
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Data-driven Healthcare using Affordable Sensing - Screening, Diagnosis and Therapy
Arpan PalHead, Embedded Systems and Robotics
TCS ResearchTata Consultancy Services, India
2TCS Confidential
Developed Countries
Elderly people - 44.7 M (2013), double by 2060
Invasive and costly diagnosis
One 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 cost
http://www.aoa.acl.gov/Aging_Statistics/index.aspx
Problems of the New Age and the New World
3TCS Confidential
Next Generation Health
Keeping People Healthy
Monitoring
Alert generation
Identifying Disease Onset
Better Diagnostics
Predictive CareTreating Full Fledged Disease
Conventional and New Care Protocols
Reaction To Disease
Prevention of Disease
Disease Treatment
Promote Wellness
Now Future Improvements Across the Spectrum from Diseased to Healthy
4TCS Confidential
Application Use Cases
Accurate, Affordable, Accessible, Adaptive diagnostics & therapeutics
Screening / Diagnosis
Coronary Artery Disease (CAD)
Hypertension
Therapy
Physical rehab for Stroke Patients
Future Plan• Diabetes• Chronic Obstructive
Pulmonary Disease (COPD)• Neurorehab
HeartSense RehabBox
5TCS Confidential
CAD – also a global hazard
IHD [ischemic heart disease, also known as CAD] causes more deaths and disability and incurs greater economic costs than any other illness in the developed world. . . [and it] is likely to become the most common cause of death worldwide by 2020 - Antman et al., 2008
DoctorSpeak“Only Existing conclusive diagnostic procedures is coronary angiogram - invasive, potentially harmful, costly. It is important to diagnose this preventive condition of CAD with affordable non-invasive mass screening.”
Dr. K M Mandana - Consultant, Fortis Hospital, KolkataMCh (Cardiovascular & Thoracic Surgery), Fellow of European Board of Cardiothoracic Surgeons
CAD – Motivation
Need for Early Screening by detecting/predicting early onset of CAD
6TCS Confidential
Singapore–10, USA -14, UK - 9
Hypertension – Motivation
2011 2012 2013 2014
206 197 189 181
India – Maternal Mortality Rate per 100K Live Berths
data.worldbank.org/indicator/SH.STA.MMRT
Causes of maternal deaths 1. Direct Causes: 81%
Severe Bleeding 25%
Sepsis 15%
Unsafe abortions 13%
Eclampsia 12%
Obstructed Labour 8%
Other direct causes * 8%
2. Indirect Causes ** 19%
* Other direct causes: Ectopic pregnancy, embolism, anesthesia related.** Indirect Causes: Malaria, Anemia, Heart Diseases.
Rehana Kausar, “Maternal Mortality in India - Magnitude, Causes and Concerns”, Indian Journal for the Practising Doctor, Vol. 2, No. 2 (2005-05 - 2005-06)http://www.indmedica.com/journals.php/mailt?journalid=3&issueid=58&articleid=722&action=article
Eclampsia - a condition in which one or more convulsions occur in a pregnant woman suffering from high blood pressure, often followed by coma and posing a threat to the health of mother and baby.
Need for Early Screening of Hypertensive Mothers – control of Hypertension by medication significantly reduces complications
Also need for Hypertensive Screening in general
Hypertension – also a global hazardUSA Figures• Hypertension - leading cause of heart disease and stroke• 1/3rd have hypertension, another 1/3rd prehypertension• Only 54% of hypertensive people have it under control• Total cost to Healthcare - US$48.6 billion each year.
http://www.cdc.gov/dhdsp/data_statistics/fact_sheets/fs_bloodpressure.htm
7TCS Confidential
HeartSense Technology
Systolic Peak
Diastolic Trough
Acquisition and Pre-processing
Feature Engineering and Extraction, Cardiovascular
Models
Classification (includes metadata
like age, gender, height, weight, BMI)
Photoplethysmogram (PPG) Phonocardiogram (PCG)
8TCS Confidential
TCUP - TCS IoT Platform
Ready and DeployedOngoing Field Trials
Photodetector or Camera Microphone
PPG
Cardiovascular Models
PCG
Expert Doctor
Real-time ViewCAD Screening
Event AlertingMaternal Hypertension
Screening
HeartSense Architecture
Advanced Analytics
Cardiovascular Models
9TCS Confidential
CAD – Pilot Study Results
CAD- Own Collected dataset (20 non-CAD + 15
CAD)- 15 healthy adults with no known
history of cardiac problem at TCS Kolkata
- 5 patients but non-CAD subjects from Fortis)
- 15 angio-proven CAD from Fortis- MIMIC-II matched dataset (ICU)
- 56 CAD and 56 non-CADSensitivity = correctly identify CADSpecificity = correctly identify non-CAD
Own Collected Data MIMIC – II Data
Specificity Sensitivity Specificity Sensitivity
PCG 80% 60% - -
PPG 40% 90% 82% 88%
Fusion 70% 80% - -
1000 patient trial in two hospitals in India – Fortis Hospital in Kolkata and JJ Hospital in Mumbai IRB clearance in place, MoU in progress
Future Plan
10TCS Confidential
Blood Pressure – Pilot Study Results
BP98 subjects at 2 hospitals (Narayana Nethralaya, Bangalore and Primary healthcare centre, Jamla, Gujrat)
- SBP (80-210 mmHg) and DBP (60-130 mmHg)
Error(mmHg)
SBP DBP
OMRON1 iBP2 TCS Model OMRON1 iBP2 TCS Model
< 5 61% 24% 29% 52% 26% 41%
< 10 85% 44% 43% 85% 48% 66%
< 15 94% 59% 66% 96% 70% 84%
• Working with Municipality and Government in Nashik, a semi-urban eco-system• Focusing more on classification for Hypertensive screening and not on actual BP measurement – MAP based• Field Trial with Pregnant mothers planned – need for Longitudinal Study
Future Plan
1. El Assaad, Mohamed A., et al. "Validation of the Omron HEM-907 device for blood pressure measurement." American Journal of Hypertension 15.S3 (2002): 87A-87A.2. Plante, Timothy B., et al. "Validation of the Instant Blood Pressure Smartphone App." JAMA internal medicine (2016) – A study by Johns Hopkins University School of Medicine
11TCS Confidential
HeartSense - Achievements so far
Co
ntr
ibu
tio
n • Low cost Mobile Attachment / wearable • Digital stethoscope
• Smartwatch
• Robust Signal Processing• Outlier Rejection
• Motion Artefact Removal
• Robust Pre-processing
• Cloud based screening and alerting• Supervised Learning for
CAD, Hypertension Classification
• Multi-sensor Fusion
IP • Papers• 15+ in major conferences
(ICASSP, EMBC, MobiHoc, Sensys, ICC, BSN ..)
• Patents• 14 patents filed
• Collaborations• Indian Statistical Institute
Kolkata
• Fortis Hospital, Kolkata
• IIT KGP and IISc planned
Aw
ard
s • Awards • Wearable Tech Award, 2016
• Aegis Graham Bell Award, 2015
• CSI Young Innovator Award, 2015
• Best Demo Award at Sensys 2014
Also working on Diabetes, COPD and Stress
12TCS Confidential
Tele-rehabilitation for Stroke Patients - Motivation
Very expensive devices and high maintenance cost
Existing Quantitative Motion Analysis systems (VICON ) costs approx. $150K – very few hospitals in have it even in metro cities – no access in rural locations
Transportation and Queue at Hospital
No adaptability in exercises and Poor Compliance Checking
13TCS Confidential
TCS RehabBox which will be used for balance, gait and joint range of motion analysis using Kinect sensor
Store Raw Data Patient’s Exercise
Parameter
Patient History
Extract Parameters
Doctor’s Portal
Feedback
Video Conf.
RehabBox - Architecture
• Single Limb Standing (SLS) for Balance – Duration, Vibration, Fall Risk Score
• Gait Analysis – Dynamic Balance, Foot Landing, Stride Length
• Range of Motion (ROM) Analysis for Upper Limb – Joint movement compliance
14TCS Confidential
RehabBox –Pilot Study Results
Single Limb Stance - Balance
• 11 subjects (5 chronic stroke-survivors, 3 females, mean age 64.4 ± 8.2 yrs) - able to stand/walk unaided.
History of fall was present in 80% stroke and 16.6% control subjects.
• SLS duration (SLSD) was significantly low in Stroke vs Control (SLSD =9.5±14.5 sec. vs 55.7±13.6 sec.).
• Both SLSD and Vibration Index (VI) are significantly different in patients with fall vs no-fall history
(SLSD=6.7±9.8 second vs 59.9±13.8; VI=0.68±0.34 vs 0.25±0.16
• 8 healthy subjects (age: 21-36 years, weight: 47kg - 78kg & height: 4’6’’ - 5’9’’, athletic and non-athletic)
• Minimum Absolute Error (w.r.t. GAITRite) for stride length 3.84 cm
ROM Analysis – Improvement in Kinect Joint Motion Estimate
Future Plan – Larger Patient Trials at AMRI Kolkata and Kokilaben Hospital, Mumbai
Gait Analysis – Stride Length
15TCS Confidential
RehaBox - Achievements so far
Co
ntr
ibu
tio
n • Image Processing• Improvement in Kinect Accuracy
• Joint movement model based RGBD processing to augment Kinect Skeleton Model
• Analytics• Cloud based therapy planning
and compliance
• Accurate Stride length via Gait Modeling and Analysis
• Creation of a Strength and Stability Score fusing SLS Duration and Vibration Index
• Correlate with Fall PropensityIP • Papers
• 5+ in major conferences (ICASSP, EMBC, BioMed, ESPRM, INREM..)
• Patents• 5 patents filed
• Collaborations• AMRI Hospital, Kolkata
• IIT Gandhinagar
Also working on Neurorehab using EEG and GSR
16TCS Confidential
1. Pal, Arpan et. al., "A robust heart rate detection using smart-phone video." In Proceedings of the 3rd ACM MobiHoc workshop on Pervasive wireless healthcare, pp. 43-48. ACM, 2013.
2. Ghose, Avik et. al., "UbiHeld: ubiquitous healthcare monitoring system for elderly and chronic patients." In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pp. 1255-1264. ACM, 2013.
3. Banerjee Rohan et. al., "PhotoECG: Photoplethysmography to Estimate ECG Parameters" In IEEE International Conference on Accoustics, Speech and Signal Processing, 20144. Dutta Choudhury Anirban et al. "Estimating Blood Pressure using Windkessel Model on Photoplethysmogram" in EMBC 2014.5. Visvanathan Aishwarya et al. "Smart Phone Based Blood Pressure Indicator" in MobileHealth workshop of Mobihoc 20146. Visvanathan Aishwarya et al. "Effects of Fingertip Orientation and Flash Location in Smartphone Photoplethysmography“, ICACCI 20147. Banerjee, Rohan et al. "Demo Abstract: HeartSense: Smart Phones to Estimate Blood Pressure from Photoplethysmography" in 11th ACM Conference on Embedded Networked
Sensor Systems (SenSys 2014)8. Dutta Choudhury, Anirban et al. "HeartSense: Estimating Heart rate from Smartphone Photoplethysmogram using Adaptive Filter and Interpolation" in 1st International Conference
on IoT Technologies for HealthCare (HealthyIoT, IoT-360)9. Nasim Ahmed et al. “Feasibility Analysis for Estimation of Blood Pressure and Heart Rate using A Smart Eye Wear”, in WearSys Workshop, Mobisys 201510. Aditi Misra et al. "Novel Peak detection to estimate HRV using Smartphone Audio“ in BSN 201511. "Noise Cleaning and Gaussian Modeling of Smart Phone Photoplethysmogram to improve Blood Pressure Estimation" in ICASSP 201512. Keshaw Dewangan, Arijit Sinharay, Parijat Deshpande, “Design And Simulation Of A Low-cost Digital Stethoscope” in COMSOL 2015 as poster for 30 Oct 201513. Shreyasi Datta et al, “Blood Pressure Estimation from Photoplethysmogram using Latent Parameters” in IEEE ICC 201614. "Rohan Banerjee et al, “Non Invasive Detection of Coronary ArteryDisease using PCG and PPG Signal” in HealthWear 2016 : 1st EAI International Conference on Wearables in
Healthcare"15. Rohan Banerjee et al, "Time-Frequency Analysis of Phonocardiogram for Classifying Heart Disease", Computing in Cardiology 201616. Kingshuk Chakravarty et. al. ,“A Multimodal Therapy Design Toolbox for Gait-Rehabilitation". INEREM 201517. Chakravarty, Kingshuk et. al. ,"Quantification of balance in single limb stance using kinect." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), pp. 854-858. IEEE, 201618. Kingshuk Chakravarty et. al. ,"AN AFFORDABLE GAIT AND POSTURAL BALANCE ANALYSIS SYSTEM USING KINECT FOR REHABILITATION." In BioMed 201619. Sanjana Sinha et. al. ,"Accurate Upper Body Rehabilitation System Using Kinect." Accepted in EMBC 2016 (To Appear in 16-20 Aug 2016)20. Kingshuk Chakravarty et. al. ,"PREDICTING FALL RISK IN STROKE SURVIVORS: A NOVEL MEASUREMENT USING KINECT BASED SYSTEM", Accepted in World Stroke Congress (To
Appear in 26-29 Oct. 2016)
Relevant Publications
17TCS Confidential
Aniruddha SinhaParijat Deshpande
Anirban DuttachaudhuriRohan BanerjeeShreyoshi Dutta
Kingshuk ChakravartyBrojeswar Bhowmick
Sanjana SinhaAvik Ghose
Nasim AhmedAditi Misra
Aiswharya VisvanathanKeshaw Dewangan
Arijit Sinharay
Team
Thank You