Automatic Multimodal Assessment of Pain in Dementia

项目摘要/摘要 慢性和急性疼痛情况随着年龄的增长而增加，并与年龄相关的疾病，包括阿尔茨海默氏症 疾病和类似的痴呆症，造成痛苦，加剧生活质量的下降，并可能导致 进一步的发病率和增加的死亡率。对疼痛的理解和治疗尤其具有挑战性 在言语交流能力可能受损的人群中，其中有 阿尔茨海默病和相关的痴呆症特别重要，如果仅仅是因为老龄化的增加 人口。大量研究表明，基于非语言的疼痛指标的方法会产生效果 痴呆患者疼痛的可靠有效测量。其中一些研究表明，患者 事实上，痴呆症患者可能对疼痛高度反应，从而导致对评估技术的改进的追求 疼痛在痴呆症人群中更加重要。评估非语言指标的现有技术 疼痛有几个缺点，这些缺点限制了它们的应用。特别是，它们需要人类观察者，是 依赖于专业培训，可能很费力，而且不能连续应用。最新进展 计算机视觉和机器学习有可能克服其中的一些缺点。我们的“父母” 建议“项目旨在开发先进的、自动化的计算机视觉和机器学习模型 在一项对腰痛患者的纵向队列研究中评估疼痛的非语言方面。本副刊 一项提案寻求延长我们在“家长津贴”方面的工作，其目标是“建立一个全自动、多式联运的 (面部、头部和身体运动)系统，通过视频测量下腰痛的发生和强度“。 在这个补充项目中，这些目标将扩展到患有痴呆症的老年人。我们将完善 在下腰痛人群中发现的疼痛评估原则，推广和评估其应用 在长期护理机构中患有痴呆症的老年人样本中。 参与者，患有痴呆症的长期护理居民，在基线状态下被录像，就像他们 一动不动地躺在床上或检查台上，然后在他们接受标准化的运动方案时 旨在识别疼痛部位。参与者的面部、头部和身体的运动将被用于开发 对疼痛的发生和强度进行自动测量。要做到这一点，面部、头部和身体的运动将是 使用全自动方法自动跟踪。跟踪结果将用于端到端深度训练- 基于学习的分类器自动测量老年人疼痛的发生和强度 痴呆症。为了调查所提出的分类器的有效性，我们将比较自动测量的 对疼痛强度进行可靠、客观的疼痛强度编码。马诺瓦将被用来量化关系 在单个模式和它们的组合之间，用于测量发生和强度 老年痴呆症患者的疼痛。

Project Summary/Abstract Chronic and acute pain conditions increase with aging and with age-associated conditions, including Alzheimer's disease and similar dementias, causing suffering, exacerbating diminished quality of life, and likely leading to further morbidity and increased mortality. Understanding and treatment of pain is particularly challenging among populations whose ability to communicate verbally may be impaired, among which patients with Alzheimer's disease and related dementias are of particular importance if only because of the increased aging population. A substantial body of research suggests that methods based on nonverbal indicators of pain yield reliable and valid measurement of pain in patient with dementias. Some of this work has indicated that patients with dementia may in fact be hyper-reactive to pain, rendering the pursuit of improved techniques for assessing pain in the dementia population even more important. Existing techniques for assessing nonverbal indicators of pain have several disadvantages which limit their utility. In particular, they require human observers, are dependent on specialty training, can be laborious, and cannot be applied on a continuous basis. Advances in computer vision and machine learning have the potential to overcome some of these shortcomings. Our “parent proposal” project aims to develop advanced, automated computer-vision and machine-learning models for assessing nonverbal aspects of pain in a longitudinal cohort study of people with low back pain. This supplement proposal seeks to extend our work on the “parent grant”, which aims to “build a fully automatic, multimodal (face, head, and body movement) system to measure the occurrence and intensity of low back pain from video”. In this supplement project, these aims will be extended to older adults with dementia. We will refine the principles discovered for pain assessment in the low back pain population, extend, and evaluate their application in a sample of older adults with dementias in extended-care facilities. Participants, long-term care residents with dementia, were video-recorded during a baseline state as they were lying still on a bed or examination table and then as they underwent a standardized protocol of movements designed to identify painful areas. Participants' face, head, and body movement will be used for the development of automatic measures of the occurrence and intensity of pain. To do so, face, head, and body movement will be automatically tracked using fully- automatic methods. The tracking results will be used to train end-to-end deep- leaning based classifiers to automatically measure the occurrence and intensity of pain in older adults with dementia. To investigate the validity of the proposed classifiers, we will compare automated measurement of pain intensity to reliable and objective pain intensity coding. MANOVA will be used to quantify the relationship between the individual modalities and their combination for the measurement of the occurrence and intensity of pain in older adults with dementia.