Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing
Manual physical balance assistance of therapists during gait training of stroke survivors: characteristics and predicting the timing
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DOI：
10.1186/s12984-017-0337-8
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发表时间：
2017-12-02
影响因子：
5.1
通讯作者：
Rietman, Johan S.
中科院分区：
文献类型：
--
作者：
Haarman, Juliet A. M.;Maartens, Erik;Rietman, Johan S.
背景：在步态训练期间，物理治疗师持续监督中风幸存者，并在判断患者无法保持平衡时为他们的骨盆提供物理支撑。本文是第一篇提供有关治疗师在步态训练期间使用的矫正力的定量数据的论文。假设患者 COM 加速度的变化是训练期间治疗平衡辅助的良好预测因子。因此，本文提供了一种基于骶骨加速度数据预测治疗平衡辅助时间的方法。方法：本研究包括 8 名亚急性中风幸存者和 7 名治疗师。患者被要求在传统训练环境中进行直线行走和回转行走。骶骨的加速度由惯性磁测量单元捕获。治疗师施加的平衡辅助矫正力是从位于患者臀部两侧的两个力传感器收集的。表征治疗平衡辅助的措施是力的大小、持续时间、脉冲和辅助发生的解剖平面。根据骶骨的加速度数据，开发了一种算法来预测治疗平衡辅助。为了验证所开发的算法，将算法预测的平衡辅助事件与实际提供的治疗辅助进行比较。结果：该算法能够预测实际的治疗辅助，阳性预测值为 87%，真阳性率为 81%。辅助主要发生在中外侧轴上，治疗师在此平面上提供约患者体重 2% 的矫正力（15.9 N (11)，中位数 (IQR)）。平衡辅助持续时间中位数为 1.1 秒 (0.6)（中位数 (IQR)），中位脉冲为 9.4Ns (8.2)（中位数 (IQR)）。尽管治疗师被专门指示瞄准髂嵴上的力传感器，但在 22% 的矫正中报告了不同的接触位置。结论：本文深入了解了治疗师在步态训练期间对其手动物理协助的行为。提出了代表治疗平衡辅助力特征的定量数据集。此外，还开发了一种算法来预测提供治疗平衡辅助的事件。当使用相同的算法设置对不同的治疗师和患者进行分析时，预测分数仍然很高。定量数据集和开发的算法都可以作为（机器人控制的）平衡支持设备开发的技术输入。
Background: During gait training, physical therapists continuously supervise stroke survivors and provide physical support to their pelvis when they judge that the patient is unable to keep his balance. This paper is the first in providing quantitative data about the corrective forces that therapists use during gait training. It is assumed that changes in the acceleration of a patient's COM are a good predictor for therapeutic balance assistance during the training sessions Therefore, this paper provides a method that predicts the timing of therapeutic balance assistance, based on acceleration data of the sacrum.Methods: Eight sub-acute stroke survivors and seven therapists were included in this study. Patients were asked to perform straight line walking as well as slalom walking in a conventional training setting. Acceleration of the sacrum was captured by an Inertial Magnetic Measurement Unit. Balance-assisting corrective forces applied by the therapist were collected from two force sensors positioned on both sides of the patient's hips. Measures to characterize the therapeutic balance assistance were the amount of force, duration, impulse and the anatomical plane in which the assistance took place. Based on the acceleration data of the sacrum, an algorithm was developed to predict therapeutic balance assistance. To validate the developed algorithm, the predicted events of balance assistance by the algorithm were compared with the actual provided therapeutic assistance.Results: The algorithm was able to predict the actual therapeutic assistance with a Positive Predictive Value of 87% and a True Positive Rate of 81%. Assistance mainly took place over the medio-lateral axis and corrective forces of about 2% of the patient's body weight (15.9 N (11), median (IQR)) were provided by therapists in this plane. Median duration of balance assistance was 1.1 s (0.6) (median (IQR)) and median impulse was 9.4Ns (8.2) (median (IQR)). Although therapists were specifically instructed to aim for the force sensors on the iliac crest, a different contact location was reported in 22% of the corrections.Conclusions: This paper presents insights into the behavior of therapists regarding their manual physical assistance during gait training. A quantitative dataset was presented, representing therapeutic balance-assisting force characteristics. Furthermore, an algorithm was developed that predicts events at which therapeutic balance assistance was provided. Prediction scores remain high when different therapists and patients were analyzed with the same algorithm settings. Both the quantitative dataset and the developed algorithm can serve as technical input in the development of (robotcontrolled) balance supportive devices.