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 detecting agitation in one-minute windows. However, there are significant limitations in the dataset, such as imbalance problem and potential imprecise labelsas the occurrence of agitation is much rarer in comparison to the normal behaviours. In this paper, we first implemented different undersampling methods to eliminate the imbalance problem, and came to the conclusion that only 20% of normal behaviour data were adequate to train a competitive agitation detection model. Then, we designed a weighted undersampling method to evaluate the manual labeling mechanism given the ambiguous time interval assumption. After that, the postprocessing method of cumulative class re-decision (CCR) was 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 showed that a combination of undersampling and CCR improved F1-score and other metrics to varying degrees with less training time and data.

翻译：摘要：躁动是痴呆症患者最常见的症状之一，可能对患者自身及照护者安全构成风险。开发客观的躁动检测方法对于保障居住在养老机构的痴呆症患者健康与安全具有重要意义。在前期研究中，我们收集了17名参与者600天的多模态可穿戴传感器数据，并开发了基于一分钟时间窗的躁动检测机器学习模型。然而，该数据集存在显著局限性，例如类别不平衡问题以及潜在的不精确标签——因为躁动事件的发生频率远低于正常行为。本文首先实施不同欠采样方法以消除不平衡问题，结果表明仅需20%的正常行为数据即可训练具有竞争力的躁动检测模型。随后，我们设计了一种加权欠采样方法，基于模糊时间区间假设评估人工标注机制。在此基础上，提出基于历史序列信息与躁动连续性特征的累积类别重决策后处理方法，通过优化决策性能推动躁动检测系统的潜在应用。实验结果显示，欠采样与累积类别重决策方案的结合，在减少训练时间和数据量的同时，使F1分数及其他评价指标获得不同程度的提升。