Adherence monitoring of rehabilitation exercise with inertial sensors: A clinical validation study
Adherence monitoring of rehabilitation exercise with inertial sensors: A clinical validation study
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DOI：
10.1016/j.gaitpost.2019.03.008
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发表时间：
2019-05-01
期刊：
影响因子：
2.4
通讯作者：
Rees, Jonathan
中科院分区：
文献类型：
--
作者：
Bavan, Luckshman;Surmacz, Karl;Rees, Jonathan
背景：康复在各种肌肉骨骼疾病的管理中发挥着既定的作用。这种治疗在很大程度上依赖于在家里的自我指导练习，在那里执行的坚持性是未知的。证明治疗的保真度是必要的，以得出结论，在临床和研究环境中的康复干预的疗效。有一个缺乏的工具和方法来实现这一点。研究问题：这项研究的目的是评估使用一个单一的惯性传感器识别和分类肩关节康复activityusing supervised machine learning techniques.Methods的可行性：20例肩关节疼痛患者进行监测，执行五个康复锻炼常规规定他们的条件。加速度计、陀螺仪和磁力计数据是通过安装在臂套上的设备收集的。分析中包括非特异性运动数据。从标记的数据段计算时域和频域特征，并使用ReliefF算法根据其预测重要性进行排名。选择的特征被用来训练四种监督学习算法：决策树，k-近邻，支持向量机和随机森林。结果：支持向量机算法和随机森林算法分别获得了最佳的10倍交叉验证（97.2%）和1个主题外验证（80.5%）的预测准确率。时域特征来自加速度计，磁力计和方向数据流被证明有最高的预测值分类康复activity.Significance：分类模型表现良好，区分患者的运动活动从非特定的运动和识别特定的运动类型，使用惯性传感器数据。家庭康复活动的临床有用的帐户将有助于指导治疗策略和促进方法，以提高患者的参与。今后的工作应侧重于评估这些系统在自然和无人监督环境中的性能。
Background: Rehabilitation has an established role in the management of a wide range of musculoskeletal conditions. Much of this treatment relies on self-directed exercises at home, where adherence of execution is unknown. Demonstrating treatment fidelity is necessary to draw conclusions about the efficacy of rehabilitation interventions in both clinical and research settings. There is a lack of tools and methods to achieve this.Research question: This study aims to evaluate the feasibility of using a single inertial sensor to recognise and classify shoulder rehabilitation activity using supervised machine learning techniques.Methods: Twenty patients with shoulder pain were monitored performing five rehabilitation exercises routinely prescribed for their condition. Accelerometer, gyroscope and magnetometer data were collected via a device mounted onto an arm sleeve. Non-specific motion data was included in the analysis. Time and frequency domain features were calculated from labelled data segments and ranked in terms of their predictive importance using the ReliefF algorithm. Selected features were used to train four supervised learning algorithms: decision tree, k-nearest neighbour, support vector machine and random forests. Performance of algorithms in accurately classifying exercise activity was evaluated with ten-fold cross-validation and leave-one-subject-out-validation methods.Results: Optimal predictive accuracies for ten-fold cross-validation (97.2%) and leave-one-subject-out-validation (80.5%) were achieved by support vector machine and random forests algorithms, respectively. Time domain features derived from accelerometer, magnetometer and orientation data streams were shown to have the highest predictive value for classifying rehabilitation activity.Significance: Classification models performed well in differentiating patient exercise activity from non-specific movement and identifying specific exercise type using inertial sensor data. A clinically useful account of home rehabilitation activity will help guide treatment strategies and facilitate methods to improve patient engagement. Future work should focus on evaluating the performance of such systems in natural and unsupervised settings.