Summary
Background: Caregiving is a complex, stressful activity, which frequently leads to anxiety and
the development of depressive disorders. Recent advances in wearable sensing allows
to monitor relevant physiological data of the caregiver for detecting anxiety spans
and for enacting coping strategies to reduce their anxiety when needed.
Objectives: This work proposes a method to infer anxiety states of caregivers when caring for
people with dementia, by using physiological data.
Methods: A model using Markov chains for detecting internal anxiety states is proposed. The
model is tested with a physiological dataset gathered from a naturalistic enactment
experiment with 10 participants. A visual analysis for observing anxiety states is
employed. The Markov chain model is evaluated by using Inter-beat Interval (IBI) data
to detect 4 internal states: “Relaxed”, “Arousing”, “Anxiety”, and “Relaxing”.
Results: From the visual inspection of interbeat interval data, self-report and observation
labels a total of 823 state segments were identified which contained the following
states: 137 “relaxed”, 91 “arousing”, 410 “anxious”, and 185 “relaxing”. By using
the average IBI value of 60 seconds segments as classification feature, the model
was evaluated with a “leave one-out” cross validation with an average accuracy of
73.03%.
Conclusions: We proposed a Markov chain model for internal anxiety state detection of caregivers
that care for people with dementia. The model was evaluated in a naturalistic enactment
experiment with 10 participants. The resulting accuracy is comparable to previous
results on stress classification.
Keywords
Anxiety - physiological stress - caregivers - dementia
