用于改善痴呆症患者激越检测的欠采样与累积类别再决策方法
Undersampling and Cumulative Class Re-decision Methods to Improve Detection of Agitation in People with Dementia
AI总结：
针对痴呆症患者激越检测中的类别不平衡与标签不精确问题，本文结合欠采样、加权欠采样及累积类别再决策后处理，以更少数据和训练时间提升了检测性能。
摘要：
激越是痴呆症患者（PwD）最普遍的症状之一，可能将其自身和照护者的安全置于风险之中。开发客观的激越检测方法，对于支持居住在住宿环境中的PwD的健康与安全具有重要意义。在此前的一项研究中，我们收集了17名参与者长达600天的多模态可穿戴传感器数据，并开发了用于在一分钟窗口内检测激越的机器学习模型。然而，该数据集存在显著局限，例如类别不平衡问题以及潜在的标签不精确，因为与正常行为相比，激越的发生要稀少得多。本文中，我们首先实施了不同的欠采样方法以消除不平衡问题，并得出结论：仅需20%的正常行为数据，就足以训练出具有竞争力的激越检测模型。随后，我们在模糊时间区间假设下，设计了一种加权欠采样方法来评估人工标注机制。之后，基于历史序列信息和激越的连续性特征，提出了累积类别再决策（CCR）这一后处理方法，以提升激越检测系统潜在应用中的决策性能。结果表明，欠采样与CCR相结合，在减少训练时间和数据用量的同时，不同程度地提高了F1分数及其他指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
