Efficient and Cost-Effective Multimodal System for Pain Management in Low Back Pain

项目摘要/摘要 痛苦是人类痛苦中最普遍、最普遍的形式之一。疼痛通常由患者测量 自我报告或临床医生的印象，通过临床访谈或视觉模拟量表。然而，自我- 报告的疼痛很难解释，在某些情况下无法获得[Hadjistavropoulos et 等人，2002年]。为了提高护理标准并推进疼痛评估、监测和干预，我们 提出(1)基于自动面部、头部和身体运动分析的智能技术，以实现可靠和 与急性和慢性下腰痛的五种原因相关的发生和强度的有效评估 (2)告知我们对慢性LBP的心理社会和行为指标的理解，以开发新的 预防慢性腰痛的方法。 在临床评估期间，参与者的面部、头部和身体运动将被记录下来 在伸展、屈曲和旋转运动中同步高清数码摄像机。这个 获得的录像带，在第一次去诊所和治疗后的3次后续访问期间拍摄，将是 用于开发疼痛发生和强度的自动测量。为了调查 为了提高建议的自动措施的泛化能力，我们将明确训练和测试建议的分类器 五种不同类型的急性和慢性LBP。为此，面部、头部和身体的运动将自动进行 使用我们的全自动方法进行追踪。跟踪结果将用于训练端到端的深度学习 基于分类器，自动测量LBP的发生和强度。为了调查……的有效性 建议的分类器，我们将比较自动测量与患者和临床医生评估的视觉 模拟评分，简短的疼痛清单，以及从视频记录中对疼痛强度的连续观察者评级。 Manova将被用来量化个体和它们的组合之间的关系 慢性和急性LBP五种状态的发生和强度的测量 条件。 为了让我们了解LBP是如何演变成慢性形式的，我们将使用生态瞬间 评估(EMA)，以收集视频记录和疼痛之外的行为和背景信息 分数的评估。参与者将在治疗后接受为期6个月的监测，频率为7 每月连续几天(每月1周)，每天4个提示，以识别哪些人进化到 慢性腰痛。将使用EMA测量来调查疼痛强度是否因心理和社会原因而不同 LBP组之间和组内的行为因素以及心理社会和行为因素 与慢性LBP的发展有关。

Project Summary/Abstract Pain is among the most pervasive and universal forms of human distress. Pain typically is measured by patient self-report or clinician impressions, either through clinical interview or the visual analog scale. However, self- reported pain is difficult to interpret and in some circumstances not possible to obtain [Hadjistavropoulos et al., 2002]. To improve the standard of care and advance pain assessment, monitoring, and intervention, we propose (1) a savvy technology based on automatic facial, head, and body movement analysis for a reliable and valid assessment of the occurrence and intensity associated with five causes of acute and chronic low back pain (LBP); (2) inform our understanding of psychosocial and behavioral indicators of chronic LBP to develop new means to prevent chronic LBP. Participants' face, head, and body movement will be recorded during clinical assessment using two synchronized high-definition digital video cameras during extension, flexion, and rotation movements. The obtained video-recordings, taken during a first visit to the clinic and 3 follow-up visits after treatment, will be used for the development of automatic measures of the occurrence and intensity of pain. To investigate the generalizability of the proposed automatic measures, we will explicitly train and test the proposed classifiers on five different types of acute and chronic LBP. To do so, face, head, and body movement will be automatically tracked using our 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 LBP. To investigate the validity of the proposed classifiers, we will compare automated measurement to the patient- and clinician- rated visual analog scale, brief pain inventory, and continuous observer ratings of pain intensity from the video recordings. MANOVA will be used to quantify the relationship between the individual modalities and their combination for the measurement of the occurrence and intensity of the five LBP conditions and for chronic and acute conditions. To inform our understanding of how LBP evolves into a chronic form, we will use Ecological Momentary Assessment (EMA) to collect behavioral and contextual information beyond the video-recordings and pain scores' assessments. Participants will be monitored for 6 months after treatment, at a frequency of 7 consecutive days per month (1 week per month), and 4 prompts per day, to identify those who evolved to chronic LBP. EMA measures will be used to investigate whether pain intensity differs by psychosocial and behavioral factors both between and within LBP groups as well as whether psychosocial and behavioral factors are associated with the development of chronic LBP.