The world’s population is aging. By 2030, the number of U.S. adults aged 65+ will be approximately 71 million. Increase in older adult population is resulting in an epidemical growth of diseases related to cognitive impairment, such as dementia. Therefore, innovative healthcare options need to be considered in order to provide quality care to our aging population and to reduce caregiver burden. Smart home technologies can play a pivotal role in disrupting conventional “caregiving”. A primary intervention that is valuable for individuals with cognitive impairment is automated prompts that aid with initiation and completion of daily activity. We postulate that prompt timing can be automated by incorporating contextual information of activities gathered from sensors located in a smart home. To determine the ability of machine learning algorithms to generate appropriate activity-aware prompts, we performed a study in an on-campus smart home with 300 volunteer participants, aged 50+, who are healthy older adults or individuals with mild cognitive impairment. The sensor data collected from the smart home were used to generate various contextual features of an individual’s daily activities. Machine learning algorithms were trained on the data to classify a “prompt” situation from a “no-prompt” situation. However, lack of training samples representing prompt situations raises a fundamental machine learning problem known as imbalanced class distribution. We proposed a probabilistic oversampling technique that helps in better learning of the “prompt” class. While existing approaches achieve 0-40% accuracy on predicting prompt situations correctly, our approach achieved >80% accuracy.

1. 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  barnandas@wsu.edu  cook@eecs.wsu.edu
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 situation60
%