Thank you: Drs. Brandon and Jim, Dr. Businelle

• Recently joined Moffitt
• Cancer new, learning everyday
• Doctoral candidate USF IE

• Wondering what is IE?

  • Decision Engineering
  • Data and math. models to improve decision making
  • Inventory allocation
  • vehicle routing
  • optimizing patient flows in ER
  • choosing the best medical option under uncertainty

• Last 4 years: how we age, aging in US

The first reason is summarized in this population pyramid.

Population of US by 5-year age range, by percentage, non-gendered.

• Baby-boomers: born mid-40s through late 60s

• Millennials, Gen X, echo boomers in the 80s-90s.

• Life expectancy has increased by 3 mon/year since 1840^*

  • and while 75% of seniors report being in good to excellent health,
  • a majority are managing chronic health conditions.

Interesting: dependency ratio

Number of working force aged adults to number of dependents.

Work force classically defined as 15-64.

Look at how this ratio changes over time

Ratio of older adults to work-force age adults

So our population growth is occurring along two dimensions:

• Increase in absolute number of older adults
• Increase in the relative number of older adults

Include children in the dependency ratio

• Giant Baby Boomer bump
• Older adults driving trends in dep. ratio

Doesn't take into account:

• More older adults are divorced or live alone than ever before, which in conjunction with demogrph. changes leads to decreased availability of traditional family caregivers

• of 75+ without a spouse will x2 between 2010 and 2030 and without an adult child within 10 miles x6

Early in 2012, just after her 80th birthday my grandmother was diagnosed with Alzheimer’s.

Same time: grandfather hospitalized for heart failure. Also COPD.

They had just re-retired to Florida, 1200 miles from Chicago where my parents were living.

My mom left a full-time position and for a while would fly back and forth.

My grandparents were highly independent, very much wanted to live on their own, but it was very difficult for my mom to monitor them from a distance.

As Alz progressed, tougher to know if they ate, showered, left the house, slept normally, etc.

Two highlights from their story

1. Not uncommon
2. Highlighting the need and value for passive technologies that link caregivers and seniors

My parents would have benefited from a system like the one we installed in "Dorothy's" house

Uses passive, wireless sensors installed throughout the home

Collects basic information about her movements and interactions with objects

Summarized and reported on a web interface that she shared with her daughter

Aging in Place: Overwhelming majority of OA want to stay in current homes, live independently

Increase QoL by mitigating the impact of inconsistent monitoring and delayed health assessments.

Aging in Place: Overwhelming majority of OA want to stay in current homes, live independently

Increase QoL by mitigating the impact of inconsistent monitoring and delayed health assessments.

A major community partner in CREATE Health's work is The Villages...

• Minimum days is 48, newest has 75 days
• Minimum (included) days is just over 181
• Max days is 786.7916667
• Median (> 180) is 446.4166667

Reasons for leaving:

• 2 participants moved (002, and 010)
• 1 participant died
• 1 participant faced medical challenges
• 2 participants did not continue after 6 months
• 1 participant did not like the sensors

Most at once: 11 participants

“Homes [. . . ] can be hazardous for sensors, particularly when hundreds of sensors are deployed over long time durations” Hnat et al. (2011)

Hnat et al. (2011). The hitchhiker’s guide to successful residential sensing deployments. doi:10.1145/2070942.2070966.

Very important to keep track of where sensors are located

This is not something that's carried around with the data

Systems need to be resilient agains changes in devices, locations and human interactions

KL Distance provides a distance measure between probability distributions.

We use KL dist to compare frequencies across b-o-e n-grams (activity profiles)

$D_{KL}(P\parallel Q)={\sum }_i\left(p_i-q_i\right)\log \frac{p_i}{q_i}$

• Having validated that the activity profiles are capable
  of summarizing the activity patterns of an occupant,
  we now turn to the task of behavior change detection

• compare occupant's recent activity to previous behavior
  previous behavior is either a baseline period of normalcy
  or sliding windows

• Given that KL-dist is a scale-less distance metric,
  need to establish bounds guidelines for expected similarity What is normal variation vs. anomaly/change?

• Overall, our goal is to bring abnormalities to the attention of caregiver
  human review and intervention is expected
  and system issues are equally requiring of attention

• Having validated that the activity profiles are capable
  of summarizing the activity patterns of an occupant,
  we now turn to the task of behavior change detection

• compare occupant's recent activity to previous behavior
  previous behavior is either a baseline period of normalcy
  or sliding windows

• Given that KL-dist is a scale-less distance metric,
  need to establish bounds guidelines for expected similarity What is normal variation vs. anomaly/change?

• Overall, our goal is to bring abnormalities to the attention of caregiver
  human review and intervention is expected
  and system issues are equally requiring of attention

• Having validated that the activity profiles are capable
  of summarizing the activity patterns of an occupant,
  we now turn to the task of behavior change detection

• compare occupant's recent activity to previous behavior
  previous behavior is either a baseline period of normalcy
  or sliding windows

• Given that KL-dist is a scale-less distance metric,
  need to establish bounds guidelines for expected similarity What is normal variation vs. anomaly/change?

• Overall, our goal is to bring abnormalities to the attention of caregiver
  human review and intervention is expected
  and system issues are equally requiring of attention