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Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: from Exercise to Functional Activity
IEEE Transactions on Affective Computing ( IF 11.2 ) Pub Date : 2020-04-01 , DOI: 10.1109/taffc.2018.2798576
Temitayo A. Olugbade , Nadia Bianchi-Berthouze , Nicolai Marquardt , Amanda C. de C. Williams

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.

中文翻译:

慢性疼痛中运动相关自我效能感的人类观察者和自动评估:从运动到功能性活动

临床医生根据运动相关的自我效能感 (MRSE) 调整慢性疼痛康复的干预措施。这促使我们在这种情况下研究自动 MRSE 估计,以开发能够在没有临床医生的情况下提供适当支持的技术。我们首先探索了临床观察者估计,这表明身体运动行为,而不是面部表情或参与行为,与身体活动期间的 MRSE 估计更相关。根据我们的发现,我们构建了一个系统,该系统可以根据使用可穿戴传感器捕获的身体表情和身体肌肉活动来估计 MRSE。我们的结果(两种体育锻炼类型的 F1 分数为 0.95 和 0.78)提供了自动 MRSE 估计支持慢性疼痛身体康复的可行性证据。我们进一步探索了使用一组减少的低成本传感器自动估计 MRSE,以研究将此类功能嵌入无处不在的可穿戴设备以支持功能活动的可能性。我们对运动和功能活动的评估得出 F1 分数为 0.79。这一结果表明,在无处不在的环境中的日常运作期间,有可能(并呼吁进行更多研究)进行 MRSE 估计。我们讨论了我们的发现对相关领域的影响。这一结果表明,在无处不在的环境中的日常运作期间,有可能(并呼吁进行更多研究)进行 MRSE 估计。我们讨论了我们的发现对相关领域的影响。这一结果表明,在无处不在的环境中的日常运作期间,有可能(并呼吁进行更多研究)进行 MRSE 估计。我们讨论了我们的发现对相关领域的影响。
更新日期:2020-04-01
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