{"title":"Automatic recognition of fear-avoidance behavior in chronic pain physical rehabilitation","authors":"M. Aung, N. Bianchi-Berthouze, P. Watson, Ac De","doi":"10.4108/icst.pervasivehealth.2014.254945","DOIUrl":null,"url":null,"abstract":"Physical activity is beneficial in chronic pain rehabilitation. However, due to psychological anxieties about pain and the percevied risk of injury, physical activity is often avoided by people with chronic pain. This avoidance is expressed through self protective body movement aimed at avoiding strain, particularly in painful areas. The detection of protective behaviour is crucial for effective rehabilitation advice and to enable a more normal lifestyle. Current technology to motivate physical activity in rehabilitation contexts does not address these psychological barriers. In this paper, we investigate the automatic recognition of a specific form of protective behaviour, guarding, common in people with chronic lower back pain. We trained ensembles of decision trees, Random Forests, on posture and velocity based features from motion capture and electromyographic data. Results show overall out of bag F1-classification scores of 0.81 and 0.73 for sitting to standing and one leg stand exercises respectively.","PeriodicalId":120856,"journal":{"name":"Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare","volume":"50 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/icst.pervasivehealth.2014.254945","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38
Abstract
Physical activity is beneficial in chronic pain rehabilitation. However, due to psychological anxieties about pain and the percevied risk of injury, physical activity is often avoided by people with chronic pain. This avoidance is expressed through self protective body movement aimed at avoiding strain, particularly in painful areas. The detection of protective behaviour is crucial for effective rehabilitation advice and to enable a more normal lifestyle. Current technology to motivate physical activity in rehabilitation contexts does not address these psychological barriers. In this paper, we investigate the automatic recognition of a specific form of protective behaviour, guarding, common in people with chronic lower back pain. We trained ensembles of decision trees, Random Forests, on posture and velocity based features from motion capture and electromyographic data. Results show overall out of bag F1-classification scores of 0.81 and 0.73 for sitting to standing and one leg stand exercises respectively.
体力活动对慢性疼痛的康复是有益的。然而,由于对疼痛的心理焦虑和对受伤风险的感知,慢性疼痛患者往往避免进行身体活动。这种回避是通过自我保护的身体运动来表达的,目的是避免紧张,特别是在疼痛的地方。发现保护性行为对于有效的康复建议和实现更正常的生活方式至关重要。目前在康复环境中激励身体活动的技术并没有解决这些心理障碍。在本文中,我们研究了一种特殊形式的保护行为的自动识别,守卫,在慢性腰痛患者中很常见。我们训练决策树的集合,随机森林,基于动作捕捉和肌电图数据的姿势和速度特征。结果表明,坐转站和单腿站立运动的总体out of bag f1分类得分分别为0.81和0.73。