Presentation of Hexoskin Validation for KHealth's Dementia Project The paper is available at: http://www.knoesis.org/library/resource.php?id=2155 Citation for the paper: T. Banerjee, P. Anantharam, W. L. Romine, L. Lawhorne, A. Sheth, 'Evaluating a Potential Commercial Tool for Healthcare Application for People with Dementia' in Proc. of the Intl Conf on Health Informatics and Medical Systems (HIMS), Las Vegas, July 27-30, 2015.

1. Evaluating a Potential Commercial Tool for Healthcare
Application for People with Dementia
Tanvi Banerjee1, Pramod Anantharam1 , William Romine2, Larry Lawhorne3,
Amit Sheth1
1Ohio Center of Excellence in Knowledge-enabled Computing(Kno.e.sis),
Wright State University, USA
2Department of Biological Sciences, Wright State University, USA
3Boonshoft School of Medicine, Wright State University, USA

2. 2
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/
MIT Technology Review, 2012
The Patient of the Future

3. Through analysis of physical,
physiological, and environmental
observations, our cellphones could
act as an early warning system to
detect serious health conditions, and
provide actionable information
canary in a coal mine
Empowering Individuals (who are not Larry Smarr!) for their own health
kHealth: knowledge-enabled healthcare
3

4. AsthmaDementia Heart Failure Liver Cirrhosis
kHealth Application Areas
4

5. 5
1Alzheimer’s Association description of Alzheimer’s statistics, Available online at:
http://www.alz.org/alzheimers_disease_facts_and_figures.asp#quickFacts
2 Dementia related facts, Available online at: http://www.cdc.gov/mentalhealth/basics/mental-illness/dementia.htm
3. K. Vincent, V. A. Velkof, “The next four decades: The older population in the United States: 2010 to 2050.” Washington, D.C.: U.S. Census
Bureau; 2010.
5 million
$150
billion
500,000
17.7
billion
People in the U.S. are
diagnosed with
Alzheimer’s disease1.
Spent on Alzheimer’s
alone in a year2
Cause of death in
Americans annually
In 2013, hours of
unpaid care provided
by friends and
caregivers3
Dementia: Severity of the problem

6. 6
Public level
Signals
Population level
Signals
Monitoring and Predicting Behavior Patterns in Patients with Dementia

7. 7
Clinical Collaborators
Dr. Larry Lawhorne, MD

8. Hexoskin Vest
8
● Heart Rate (HR)
● Breathing Rate (BR)
● Minute Ventilation (MV)
● Cadence
● Activity
http://www.hexoskin.com/blogs/news/13591246-hexoskin-wins-most-innovative-consumer-health-product-award-at-interface-future-of-health

9. Sample Data from a Run Sequence Using the Hexoskin Vest
9

10. • Test for activity states that can use some known information
– Cadence
• Four healthy young subjects completed four activity states
(rest, walk, run, and sprint)
10 mins sit
10 mins walk
10 mins run
1 min sprint
Experimental Design: Controlled Study

11. Activity State Mean Std. Dev
Rest 0.00 0.00
Walk 103.05 25.03
Run 171.95 10.25
Sprint 185.93 22.00
Cadence Validation Across Subjects and Activity States

12. Key Question:
● What is the consistency of cadence measures across subjects and activity
levels?
Key Assumption:
We treat subject and activity state as random effects → attempt to
generalize across all possible subjects and activity states.
Error Analysis: Variance Components Modeling

13. Effect Estimate % Variance
Subject 133.89 1.78
Activity 7199.19 95.51
Subject-by-Activity 153.91 2.04
Error 50.67 0.67
Results from the Generalizability Study

14. • Six subjects (increased age range 27 to 68 to include more
older adults)
• Longer study: wore the vest for a minimum of two hours
• Condition: At least one gait related activity (for cadence)
Experimental Design: Semi-controlled Study

15. MANOVA Lambda F* R Sq.
Subject 1 0.128 28922.56 0.871
Subject 2 0.160 26888.12 0.839
Subject 3 0.181 32369.65 0.818
Subject 4 0.255 3275.61 0.744
Subject 5 0.375 8020.30 0.624
Subject 6 0.242 6354.81 0.757
MANOVA: Trying to Run multiple regressions on HR, BR, A, MV as DV and C
as IV
F critical is 5.1337 at α=.0001

16. Mean Std. Dev SE Tdf=5
P-value
C-BR 0.54 0.20 0.08 6.53 0.001*
C-HR 0.16 0.28 0.12 1.38 0.226
C-MV 0.66 0.15 0.06 10.9 0.000*
C-A 0.85 0.07 0.03 28.9 0.000*
BR-HR 0.18 0.28 0.11 1.56 0.180
BR-MV 0.18 0.21 0.09 2.04 0.097
BR-A 0.52 0.18 0.07 7.06 0.001*
MV-HR 0.31 0.28 0.11 2.75 0.040*
MV-A 0.64 0.18 0.07 8.93 0.000*
HR-A 0.19 0.28 0.11 1.69 0.152
*Significant at alpha = 0.05

17. ● Cadence is a highly precise indicator of activity states for our
cohort
○ Can therefore be used to detect changes in activity patterns across any
individual
● Very little individual-level variation in cadence
○ While expected individual effects exist, they are not likely to confound
detection of activity changes
● HR was the least correlated with the other variables
Conclusions

18. Future Work
Carry out a Large Scale Pilot & Clinical Trial
• kHealth kit is prepared to be deployed with over 20 or more
dementia patients
Formulate Prediction of Patient’s dementia symptoms using
physiological markers from the vest
• Personalization is crucial in such a multispectral condition
Add New Sensors for Monitoring sleep and caregiver stress
• We need these sensors for caregiver stress with dementia
episodes in patients

19. Acknowledgements
Partial support for this research was provided by Wright State
University’s VP of Research under a challenge grant.

20. Thank you 
Thank you, and please visit us at http://knoesis.org
For more information on kHealth, please visit us at http://knoesis.org/projects/khealth
Link to the paper: http://www.knoesis.org/library/resource.php?id=2155