基于深度学习的慢性疼痛保护行为检测
Chronic-Pain Protective Behavior Detection with Deep Learning
摘要：
在慢性疼痛康复中，物理治疗师根据患者的保护行为表达来调整身体活动，逐渐使他们接触到令人恐惧但无害的基本日常活动。随着康复走出诊所，技术应该自动检测这种行为，以提供类似的支持。以前的工作已经表明了在特定活动中自动保护行为检测（PBD）的可行性。在这篇论文中，我们研究了深度学习在不同活动类型的PBD中的应用，使用了从健康参与者和慢性疼痛患者那里收集的可穿戴运动捕捉和表面肌电图数据。我们通过不断检测活动中的保护行为来解决问题，而不是估计其整体存在。最好的性能达到平均F1得分为0.82，留一个主题交叉验证。当按照活动类型对保护行为进行建模时，表现为弯腰的平均F1得分为0.77，单腿站立为0.81，坐立为0.72，站坐为0.83，前伸为0.67。这一表现达到了与平均专家评级表现的优异一致水平，表明在家中进行个性化慢性疼痛管理的潜力。我们分析了表征我们方法的各种参数，以了解结果如何推广到其他PBD数据集和不同级别的地面实况粒度。
英文摘要：
In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities. As rehabilitation moves outside the clinic, technology should automatically detect such behavior to provide similar support. Previous works have shown the feasibility of automatic protective behavior detection (PBD) within a specific activity. In this paper, we investigate the use of deep learning for PBD across activity types, using wearable motion capture and surface electromyography data collected from healthy participants and people with chronic pain. We approach the problem by continuously detecting protective behavior within an activity rather than estimating its overall presence. The best performance reaches mean F1 score of 0.82 with leave-one-subject-out cross validation. When protective behavior is modelled per activity type, performance is mean F1 score of 0.77 for bend-down, 0.81 for one-leg-stand, 0.72 for sit-to-stand, 0.83 for stand-to-sit, and 0.67 for reach-forward. This performance reaches excellent level of agreement with the average experts' rating performance suggesting potential for personalized chronic pain management at home. We analyze various parameters characterizing our approach to understand how the results could generalize to other PBD datasets and different levels of ground truth granularity.
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。
