用自训练与变分自编码器，提升痴呆患者躁动检测准确率。
Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors
- 结合自训练与变分自编码器，从有限标注数据中学习生理特征表示。
- 在14名参与者数据上，XGBoost分类器达到90.16%准确率。
- 适合医疗监护、智能穿戴设备开发人员参考。
痴呆是一种神经退行性疾病，近年来在老年人群中日益增多，严重影响患者及照护者的生活质量。严重痴呆患者（PwD）在长期护理机构或医院中常出现躁动与攻击行为（AA），不仅造成不适，还可能带来安全风险。现有基于可穿戴传感器与人工智能的监测方案可早期发现AA，实现及时干预，但受限于高质量标注数据稀缺，实际应用效果不佳。本研究利用Empatica E4腕戴设备采集生理数据，构建了一个包含14名参与者、来自加拿大多家医院的三个不同数据集的多样化数据集。针对标注数据有限的问题，提出一种融合自训练与变分自编码器（VAE）的新方法，通过VAE提取特征表示，并借助半监督模块生成标签、分类事件并检测AA。实验表明，该方法显著提升了模型性能，其中XGBoost分类器达到90.16%的最高准确率，验证了其在处理低标注数据场景下的有效性与优越性。
原文摘要 · Abstract (English)
Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality of life for patients and caregivers due to the symptoms arising from it. Agitation and aggression (AA) are some of the symptoms of people with severe dementia (PwD) in long-term care or hospitals. AA not only causes discomfort but also puts the patients or others at potential risk. Existing monitoring solutions utilizing different wearable sensors integrated with Artificial Intelligence (AI) offer a way to detect AA early enough for timely and adequate medical intervention. However, most studies are limited by the availability of accurately labeled datasets, which significantly affects the efficacy of such solutions in real-world scenarios. This study presents a novel comprehensive approach to detect AA in PwD using physiological data from the Empatica E4 wristbands. The research creates a diverse dataset, consisting of three distinct datasets gathered from 14 participants across multiple hospitals in Canada. These datasets have not been extensively explored due to their limited labeling. We propose a novel approach employing self-training and a variational autoencoder (VAE) to detect AA in PwD effectively. The proposed approach aims to learn the representation of the features extracted using the VAE and then uses a semi-supervised block to generate labels, classify events, and detect AA. We demonstrate that combining Self-Training and Variational Autoencoder mechanism significantly improves model performance in classifying AA in PwD. Among the tested techniques, the XGBoost classifier achieved the highest accuracy of 90.16\%. By effectively addressing the challenge of limited labeled data, the proposed system not only learns new labels but also proves its superiority in detecting AA.
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