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Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

机译:人类观察者和自动评估慢性疼痛中的运动相关自我疗效:从运动到功能活动

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Clinicians tailor intervention in chronic pain rehabilitation to movement related self-efficacy (MRSE). This motivates us to investigate automatic MRSE estimation in this context towards the development of technology that is able to provide appropriate support in the absence of a clinician. We first explored clinical observer estimation, which showed that body movement behaviours, rather than facial expressions or engagement behaviours, were more pertinent to MRSE estimation during physical activity instances. Based on our findings, we built a system that estimates MRSE from bodily expressions and bodily muscle activity captured using wearable sensors. Our results (F1 scores of 0.95 and 0.78 in two physical exercise types) provide evidence of the feasibility of automatic MRSE estimation to support chronic pain physical rehabilitation. We further explored automatic estimation of MRSE with a reduced set of low-cost sensors to investigate the possibility of embedding such capabilities in ubiquitous wearable devices to support functional activity. Our evaluation for both exercise and functional activity resulted in F1 score of 0.79. This result suggests the possibility of (and calls for more studies on) MRSE estimation during everyday functioning in ubiquitous settings. We provide a discussion of the implication of our findings for relevant areas.
机译:临床医生在慢性疼痛康复中定制干预以移动相关的自我效能(MRSE)。这使我们能够调查这种背景下的自动MRSE估计,以便在缺乏临床医生的情况下提供适当的支持。我们首先探索了临床观察者估计,这表明身体运动行为,而不是面部表情或接触行为,与体力活动实例中的MRSE估计更有关。基于我们的研究结果,我们建立了一个系统,它从使用可穿戴传感器捕获的身体表达和身体肌肉活动的MRSE估计MRSE。我们的结果(两种物理锻炼类型为0.95和0.78的F1分数)提供了自动MRSE估计可行性以支持慢性疼痛的身体康复的证据。我们进一步探索了MRSE的自动估计,具有减少的低成本传感器,以研究嵌入普遍可穿戴设备中的这种能力以支持功能活动的可能性。我们对运动和功能活动的评估导致F1得分为0.79。该结果表明(并呼吁更多研究)MRSE估计在普遍存在的环境中的日常运作期间。我们讨论了相关领域的调查结果的含义。

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