Machine Learning-Driven Caregiving for Older Adults with Dementia
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Machine Learning-Driven Caregiving for Older Adults with Dementia Barnan Das and Diane J. Cook School of Electrical Engineering and Computer Science Washington State University casas.wsu.edu [email protected] [email protected] 2009 2030 40mn 72mn 20% Older adults to be 20% of total population of United States by 2030 5 million Motion Phone Wearable Door Object Light 15.5mn caregivers • More than 5 million Americans are living with Alzheimer’s • In 2013, 15.5 mn caregivers were unpaid and mostly family members • 60% of the caregivers report severe emotional stress and often depression Need to disrupt conventional caregiving Smart homes and automated prompting for daily activities can reduce caregiver burden • Study with 400 human participants who performed 8 activities of daily living in an on-campus smart home under naturalistic conditions • Sensor data annotated for activities and errors (potential prompt situations) 400 participants 8 ADLs Statistical features to distinguish prompt and no-prompt situations Machine learning algorithms on the sensor data ∫ ∞0 4% prompt class Imbalanced classes Overlapping classes Over-sample prompt class with Gibbs sampling Under-sample no- prompt samples from overlapping region 0 20 40 60 80 100 Other methods Our approach 85% 40% 1 2 3 4 5 Helfulness Naturalistic not at all somewhat very Average participant reported prompt quality rating Accuracy in predicting prompt situation 60 %
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Transcript of Machine Learning-Driven Caregiving for Older Adults with Dementia
Machine Learning-Driven Caregiving for Older Adults with DementiaBarnan Das and Diane J. Cook
School of Electrical Engineering and Computer Science
Washington State University
casas.wsu.edu [email protected] [email protected]
2009 2030
40mn
72mn
20%
Older adults to be 20% of total
population of United States by 2030
5million
Motion
Phone
Wearable Door
Object
Light
15.5mn
caregivers
• More than 5 million Americans are
living with Alzheimer’s
• In 2013, 15.5 mn caregivers were
unpaid and mostly family members
• 60% of the caregivers report severe
emotional stress and often depression
Need to disrupt conventional caregiving
Smart homes and automated
prompting for daily activities can
reduce caregiver burden
• Study with 400 human participants who
performed 8 activities of daily living in
an on-campus smart home under
naturalistic conditions
• Sensor data annotated for activities and
errors (potential prompt situations)
400participants
8ADLs
Statistical features to distinguish
prompt and no-prompt situations
Machine learning algorithms
on the sensor data∫∞0
4%prompt
class
Imbalanced
classes
Overlapping
classes
Over-sample
prompt class with
Gibbs sampling
Under-sample no-
prompt samples from
overlapping region
0
20
40
60
80
100
Other methods Our approach
85%
40%
1 2 3 4 5
Helfulness
Naturalistic
not at all somewhat very
Average participant reported prompt
quality rating
Accuracy in predicting prompt situation60%
