Learning Temporal and Bodily Attention in Protective Movement Behavior Detection
文献类型：会议论文
| 作者 | Wang, Chongyang3; Peng, Min4 ; Olugbade, Temitayo A.3; Lane, Nicholas D.1; De Williams, Amanda C.C.2; Bianchi-Berthouze, Nadia3
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| 出版日期 | 2019 |
| 会议日期 | September 3, 2019 - September 6, 2019 |
| 会议地点 | Cambridge, United kingdom |
| DOI | 10.1109/ACIIW.2019.8925084 |
| 页码 | 324-330 |
| 英文摘要 | For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e.g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention. Advances in deep learning (DL) can enable the development of such intervention. Using the EmoPain MoCap dataset, we investigate how attention-based DL architectures can be used to improve the detection of protective behavior by capturing the most informative temporal and body configurational cues characterizing specific movements and the strategies used to perform them. We propose an end-to-end deep learning architecture named BodyAttentionNet (BANet). BANet is designed to learn temporal and bodily parts that are more informative to the detection of protective behavior. The approach addresses the variety of ways people execute a movement (including healthy people) independently of the type of movement analyzed. Through extensive comparison experiments with other state-of-the-art machine learning techniques used with motion capture data, we show statistically significant improvements achieved by using these attention mechanisms. In addition, the BANet architecture requires a much lower number of parameters than the state of the art for comparable if not higher performances. © 2019 IEEE. |
| 会议录 | 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019
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| 语种 | 英语 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9787]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 作者单位 | 1.University of Oxford, Department of Computer Science, Oxford, United Kingdom; 2.University College London, Department of Clinical Health, London, United Kingdom 3.UCL Interaction Centre, University College London, London, United Kingdom; 4.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, Chongqing, China; |
| 推荐引用方式 GB/T 7714 | Wang, Chongyang,Peng, Min,Olugbade, Temitayo A.,et al. Learning Temporal and Bodily Attention in Protective Movement Behavior Detection[C]. 见:. Cambridge, United kingdom. September 3, 2019 - September 6, 2019. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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