The document discusses research being conducted at Xerox Research Center India on non-contact video-based detection of vital signs and diseases. This includes using video to monitor respiration, heart rate, temperature and detect cardiac arrhythmias. The research aims to provide decision support for clinicians for diagnosis, prediction and treatment options. Examples are provided of data collection on cancer patients and a proposed emergency room decision support system for stroke prediction.

1. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Healthcare Research Focus at Xerox Research
Center India
• Non-contact video-based detection of body vitals and diseases
• Decision support for clinicians - diagnosis, prediction, treatment options for
diseases
Acknowledgements: Lalit K Mestha, Xerox Fellow; Manipal Hospital

2. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
How Deep Can We See?
Mean optical path length for 500 - 650nm = ~1-2mm
Mean optical path length for NIR (700 – 2400nm) = ~6-10mm
Blood pool in a normal skin is at ~0.3 – 2mm deep
Epidermis: 0.06– 0.5mm
Dermis: 1-4mm
Hypodermis: 1 – 6mm
Thickness of skin layersMean Optical Path Lengths & Penetration Depth
Ref: JHGM Klaessens and RM Verdaasdonk, “Non-invasive skin oxygenation imaging using a multi-spectral camera system: Effectiveness of
various concentration algorithms applied on human skin”, Proc. Of SPIE Vol. 7174 717408-1, 2009

3. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Video based Detection – in progress
1. Non-contact vitals (continuous monitoring)
– Heart rate
– Respiration rate
– Temperature
– Oxygen saturation (functional & fractional)
– Pulmonary volumes with 3D
– Heart rate variability
– Blood pressure
– Glucose
2. Disease diagnostics
– Cancer screening (melanoma, breast cancer, ..)
– Cardiac arrhythmias (atrial fibrillation, premature atrial contraction etc)
– Strokes, diabetics etc.,
– Abnormalities in a comprehensive metabolic panel (CMP) etc
– Sleep apnea
– SIDS
3. Biometrics
– Vein pattern & cardiovascular based biometrics
4. Food volume & nutrition content detection

4. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Monitoring Respiration, Temperature
Research Problems
Algorithms for
continuous
monitoring & Data
Fusion with
multiple cameras
Respiration signal
Temperature signal

5. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Health: Cardiac Arrhythmia (A-fib) Detection
Xerox Camera
Work process:
1. Patient with A-fib enrolled for the study
2. Videos were taken of the entire process of electrical
cardio version
3. Holter monitor installed
4. Patient - briefed about the benefits of the study
5. Patient – undergoes electrical cardio version
6. Patient – cleared of A-fib
Performed video shoot of A-fib patients interacting with Doctors,
nurses and patients
461 462 463 464 465 466 467 468
-3
-2
-1
0
1
2
3
Time (in secs)
NormalizedAmplitude
Raw ECG v/s PPG extracted using video camera
ECG lead 2
PPG (from video)
Irregular beat to beat intervals

6. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
A-fib Detection – During A-fib (Patient 1)
409 410 411 412 413 414 415 416 417
-2
-1
0
1
2
3
4
494
495
496
497 498
499 500
501
502
503
504
505 506
507
508
50
time (in secs)
NormalizedAmplitude
ECG vs PPG
ECG (lead 2)
PPG (video)
peaks (ECG)
peaks (PPG)

7. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
A-fib Detection–Normal Sinus Rhythm
(Patient 2)
843 844 845 846 847 848 849 850 851 852 853
-2
-1
0
1
2
3
900
901
902
903
904
905 906 907
908 909
910 911 912
time (in secs)
NormalizedAmplitude
ECG vs PPG
ECG (lead 2)
PPG (video)
peaks (ECG)
peaks (PPG)

8. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Sample Data Collection on Cancer Patients
• Patient 2: Male Patient
Mammogram Images Thermal Images
Right breast Left breast Left breast
Confirmed malignant lesion
(2.5 x 1.3 cm in the left breast)
Craniocaudal and mediolateral oblique views
~45 deg right from sagittal plane
Along sagittal plane

9. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Sample Data Collection on Cancer Patients
• Patient 3: Female Patient
Mammogram Images Thermal Image
Right breast Left breast
No suspicious region foundNo cancer found. Radiology report suggested
Fibroadenoma – a benign lesion
~45 deg right from sagittal plane

10. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Sample Data Collection on Cancer Patients
• Patient 4: Female Patient
Mammogram Image
Left breast Malignant lesion
Sonomammogram Image
~10 deg right from sagittal plane
Cancer is spread to
lymph nodes
~45 deg right from sagittal plane

11. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Healthcare Research Focus
• Non-contact video-based detection of body vitals and diseases
• Decision support for clinicians - diagnosis, prediction, treatment options for
diseases

12. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Emergency Room Decision Support:
Stroke Prediction
Stroke happens when blood flow to a
part of the brain stops, often causing
permanent damage
• 4.4 million deaths each year.
• ~70% of the strokes are first-ever
strokes
Tools for Clinical decision support, Preventive
healthcare
Open Innovation Project with Indian hospitals
• Data collection initiated
• Initial statistical model for stroke severity

13. 4th International Conference on Transforming Healthcare with IT 6th – 7th Sep. 2013 Hyderabad, India
Open Healthcare Analytics System
Analytics
Data & Process Analytics
• Risk Models
• Diagnostics
• Treatment planning
• Preventive Healthcare
Data
Providers,
Others
Clinical/patient
data
Process data
Gene banks
ML researchers
Universities
Crowd Experts
Abstracted
Data Views
Analytical
Models
External
Partners
Data Integration and
Privacy Management
Reporting
& Visualization
Adapters: integrate with EHR/HIS
solutions
Open APIs
Medical Literature
Websites
Social networks