Abstract:
Caring for individuals with dementia is frequently
associated with extreme physical and emotional stress, which often
leads to depression. Smart home technology and advances in
machine learning techniques can provide innovative solutions to
reduce caregiver burden. One key service that caregivers provide
is prompting individuals with memory limitations to initiate and
complete daily activities. We hypothesize that sensor technolo-
gies combined with machine learning techniques can automate
the process of providing reminder-based interventions. The first
step toward automated interventions is to detect when an individ-
ual faces difficulty with activities. We propose machine learning
approaches based on one-class classification that learn normal
activity patterns. When we apply these classifiers to activity pat-
terns that were not seen before, the classifiers are able to detect
activity errors, which represent potential prompt situations. We
validate our approaches on smart home sensor data obtained
from older adult participants, some of whom faced difficulties
performing routine activities and thus committed errors.
