An anticipated increase in the number of people with dementia will lead to an escalation in health and so- cial care spending unless it is altered by a major break- through in treatment or prevention. Behavioral symp- toms associated with dementia (BSD) are some of the most difficult problems faced by caregivers. Several measurement issues have hampered the progress of timely intervention for BSD. Sensor technology may offer a solution to the early detection of BSD that will guide the development of tailored interventions. Simi- larly, a clinical conceptualization of BSD and its mea- surement issues can facilitate the engineering of sensor networks and algorithms for activity recognition. Multi- disciplinary collaboration and the consideration of eth- ical issues will improve the adoption of these technolo- gies in healthcare research.

1. A Case for
Using Sensor Technology to
Monitor Disruptive Behaviors
of Persons with Dementia
Maria Yefimova & Diana Lynn Woods
UCLA School of Nursing
November 3, 2012
2012 AAAI Fall Symposium: AI for Gerontechnology 1

2. Overview
 Behavioral Symptoms of Dementia
 Definition
 Measurement Issues
 Sensor Networks
 Sensor Selection
 Data Processing Algorithms
 Meaningful Output
 Ethics of Technology
 Future Research 2

3. Population of Interest
 35.6 million people worldwide (Wimo & Prince, 2010)
 Affected by dementia - Alzheimer’s disease
most common
 Progressive memory loss
 Need help with daily activities
 “Disruptive behaviors” (BSD)
 Cost of care $604 billion, more than 1% GDP
(Wimo & Prince, 2010)
3

4. Problem of Interest
 “Behavioral Symptoms of Dementia” (BSD)
 Challenging to care providers
 Consume time and effort
 Unsafe, resulting in accidents
 Lead to institutionalization
 Increase cost of care
4

5. Defining BSD
 Restless body movements (tapping, banging,
picking clothes)
 Vocalization (muttering, repetitive questions,
screaming)
 Sleep disturbances (night time awakening)
 Pacing, wandering
 Resisting daily care (showering, dressing)
5

6. Goal of Research on BSD
 Inform clinical practice on prevention and
management through
 Identifying high risk individuals
 Predicting escalation of behavior
 Timing is crucial for interventions
 Developing individualized plans
6

7. Challenges and Opportunities
 Heterogeneous population
 Within-individual variability of behavior and
response
 Data is costly and difficult to analyze
 BSD manifestation
 Behaviors vary in intensity, cluster together
 Non-linear patterns of escalation/de-escalation
Can technology provide new tools to study BSD?
7

8. Faces of Dementia
Alzheimer’s Disease International - http://www.alz.co.uk/
8

9. Deploying Sensor Networks
 Multimodal sensors to capture all aspects of
behavior
 E.g. agitation recognition rate - 59% ultrasonic alone,
73% with pressure, up to 94% with sensor fusion
(Biswas, Jayachandran & Thang, 2006)
Sensors Behavior
Motion Restlessness
Radar Tapping/Banging
GPS tracking Wandering
Acoustic Vocalization
Pressure (bed) Sleep disturbances
Video Daily Activities 9

10. Technical Considerations
 Wearable
 Specific to individual, mobile
 Size and placement must be tolerated
 Environmental
 Unobtrusive and more acceptable
 Static position, tied to specific environment
 Wired versus Wireless
 Power consumption and battery life, cable
management
10

11. Data Processing
Algorithms chosen based on assumptions about
behavior
 Well-defined sequences of events (eg. ADLs)
 Decision trees with classifiers (Maurer et al., 2007),
temporal logic models (Rugnone et al., 2007)
 Sporadic and variable (eg. BSD)
 Fuzzy logic (Fook, 2007), Bayesian models (Biswas,
Jayachandran & Thang, 2006)
Goal: activity recognition and prediction
11

12. Algorithm Considerations
 Obtaining “baseline” to recognize deviations
 Learning data can’t be modeled in lab setting
 Not generalizable across individuals (Algase et al., 2010)
 Inferring meaning
 Timing and micro-context (Biswas et al., 2010)
 Information about surrounding environment
12

13. Meaningful Output
 Output readable by end user (researchers,
clinicians, caregivers)
 Visual representation of data
 Clinically relevant time intervals
 Validation against “ground truth”
 Direct observation by experienced clinicians
 Caution in using existing scales and measures
13

14. Ethical Issues
 Monitoring may be perceived as
“intrusive surveillance” (Price 2007)
 Moderated by perception of technology
 From older adult and caregiver/family
 Consent with the cognitively impaired
 Proxy consent
Preserve privacy and safety of information
14

15. Future Direction
 Currently limited to small samples
 Feasibility studies in labs, with non-elders
 Translating to “real world” setting
 Potential to use in evaluating interventions
 Financial and human capital considerations
 Cost-benefit analysis
15

16. Conclusion
 Collaboration between clinicians and
developers
 Learning a new, common “language”
 Technology as a research tool
 To understand the phenomenon
 To evaluate interventions
Goal: improving health of vulnerable older
adults and reducing healthcare costs
16

17. Questions? Suggestions?
m.yefimova@ucla.edu
Supported by
John A. Hartford Foundation’s
National Hartford Centers of Gerontological
Nursing Excellence
Award Program 17