ArXiv Preprint
Agitation is one of the most prevalent symptoms in people with dementia (PwD)
that can place themselves and the caregiver's safety at risk. Developing
objective agitation detection approaches is important to support health and
safety of PwD living in a residential setting. In a previous study, we
collected multimodal wearable sensor data from 17 participants for 600 days and
developed machine learning models for predicting agitation in one-minute
windows. However, there are significant limitations in the dataset, such as
imbalance problem and potential imprecise labels as the occurrence of agitation
is much rarer in comparison to the normal behaviours. In this paper, we first
implement different undersampling methods to eliminate the imbalance problem,
and come to the conclusion that only 20% of normal behaviour data are adequate
to train a competitive agitation detection model. Then, we design a weighted
undersampling method to evaluate the manual labeling mechanism given the
ambiguous time interval (ATI) assumption. After that, the postprocessing method
of cumulative class re-decision (CCR) is proposed based on the historical
sequential information and continuity characteristic of agitation, improving
the decision-making performance for the potential application of agitation
detection system. The results show that a combination of undersampling and CCR
improves best F1-score by 26.6% and other metrics to varying degrees with less
training time and data used, and inspires a way to find the potential range of
optimal threshold reference for clinical purpose.
Zhidong Meng, Andrea Iaboni, Bing Ye, Kristine Newman, Alex Mihailidis, Zhihong Deng, Shehroz S. Khan
2023-02-07
