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 F1-score 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.

翻译：激越行为是痴呆症患者（PwD）最常见的症状之一，可能危及患者自身及护理人员的安全。开发客观的激越行为检测方法对于保障居住式照护场所中PwD的健康与安全至关重要。在前期研究中，我们收集了17名参与者为期600天的多模态可穿戴传感器数据，并构建了基于一分钟时间窗口的激越行为预测机器学习模型。然而，该数据集存在显著局限性，例如类别不平衡问题以及因激越行为发生率远低于正常行为而导致的潜在标签不精确。本文首先采用不同欠采样方法消除不平衡问题，发现仅需20%的正常行为数据即可训练出具有竞争力的激越检测模型；随后基于模糊时间区间（ATI）假设设计加权欠采样方法，用于评估人工标注机制；进而提出基于历史时序信息与激越行为连续性特征的累积类重决策（CCR）后处理方法，改善了激越检测系统潜在应用场景下的决策性能。结果表明，欠采样与CCR的组合方法能在减少训练数据量与训练时间的同时，不同程度提升F1分数及其他评价指标，并为临床应用中寻找最优阈值参考范围提供了新思路。