Dementia is a neurodegenerative condition that combines several diseases and impacts millions around the world and those around them. Although cognitive impairment is profoundly disabling, it is the noncognitive features of dementia, referred to as Neuropsychiatric Symptoms (NPS), that are most closely associated with a diminished quality of life. Agitation and aggression (AA) in people living with dementia (PwD) contribute to distress and increased healthcare demands. Current assessment methods rely on caregiver intervention and reporting of incidents, introducing subjectivity and bias. Artificial Intelligence (AI) and predictive algorithms offer a potential solution for detecting AA episodes in PwD when utilized in real-time. We present a 5-year study system that integrates a multimodal approach, utilizing the EmbracePlus wristband and a video detection system to predict AA in severe dementia patients. We conducted a pilot study with three participants at the Ontario Shores Mental Health Institute to validate the functionality of the system. The system collects and processes raw and digital biomarkers from the EmbracePlus wristband to accurately predict AA. The system also detected pre-agitation patterns at least six minutes before the AA event, which was not previously discovered from the EmbracePlus wristband. Furthermore, the privacy-preserving video system uses a masking tool to hide the features of the people in frames and employs a deep learning model for AA detection. The video system also helps identify the actual start and end time of the agitation events for labeling. The promising results of the preliminary data analysis underscore the ability of the system to predict AA events. The ability of the proposed system to run autonomously in real-time and identify AA and pre-agitation symptoms without external assistance represents a significant milestone in this research field.

翻译：痴呆是一种神经退行性疾病，涵盖多种病症，影响着全球数百万患者及其周围人群。虽然认知障碍具有严重的致残性，但被称为神经精神症状的非认知特征与生活质量下降最为密切相关。痴呆患者的激越与攻击行为会导致痛苦并增加医疗需求。目前的评估方法依赖于护理人员的干预和事件报告，存在主观性和偏差。人工智能和预测算法为实时检测痴呆患者的激越与攻击事件提供了潜在解决方案。我们提出一个为期五年的研究系统，该系统采用多模态方法，结合EmbracePlus腕带和视频检测系统来预测重度痴呆患者的激越与攻击行为。我们在安大略海岸心理健康研究所对三名参与者进行了试点研究以验证系统功能。该系统通过收集和处理来自EmbracePlus腕带的原始与数字生物标志物来准确预测激越与攻击行为。系统还检测到至少早于激越与攻击事件六分钟的前驱激越模式，这是此前使用EmbracePlus腕带未曾发现的发现。此外，隐私保护视频系统采用掩码工具隐藏画面中的人物特征，并运用深度学习模型进行激越与攻击检测。该视频系统还有助于确定激越事件的实际起止时间以进行标注。初步数据分析的良好结果突显了系统预测激越与攻击事件的能力。所提出系统能够自主实时运行，并在无需外部协助的情况下识别激越与攻击行为及前驱症状，这标志着该研究领域的重要里程碑。