George Johnathan J, Behrman Andrea L, Roussel Thomas J
Bioengineering Department, University of Louisville, Louisville, KY, USA.
Department of Neurological Surgery, Kentucky Spinal Cord Injury Research Center, University of Louisville, Louisville, KY, USA.
J Rehabil Assist Technol Eng. 2024 Aug 28;11:20556683241278306. doi: 10.1177/20556683241278306. eCollection 2024 Jan-Dec.
Activity-based therapy is effective at improving trunk control in children with spinal cord injury. A prototype sensorized rocking chair was developed and confirmed as an activity that activates trunk muscles. This study uses data collected from the chair to predict muscle use during rocking. The prototype rocking chair included sensors to detect forces, accelerations, as well child and chair movement. Children with spinal cord injury and typically developing children (2-12 years), recruited under an approved IRB protocol, were observed rocking while sensor and electromyography data were collected from arm, leg, and trunk muscles. Features from sensor data were used to predict muscle activation using multiple linear regression, regression learning, and neural network modeling. Correlation analysis examined individual sensor contributions to predictions. Neural network models outperformed regression models. Multiple linear regression predictions significantly correlated ( < 0.05) with targets for four of eleven children with SCI, while decision tree regression predictions correlated for five children. Neural network predictions correlated for all children. Embedded sensors capture useful information about muscle activation, and machine learning techniques can be used to inform therapists. Further work is warranted to refine prediction models and to investigate how well results can be generalized.
基于活动的疗法在改善脊髓损伤儿童的躯干控制方面是有效的。开发了一种原型传感摇椅，并证实其为一种能激活躯干肌肉的活动。本研究使用从该摇椅收集的数据来预测摇晃过程中的肌肉使用情况。该原型摇椅包括用于检测力、加速度以及儿童和摇椅运动的传感器。根据经批准的机构审查委员会方案招募的脊髓损伤儿童和发育正常的儿童（2至12岁），在摇晃时接受观察，同时从手臂、腿部和躯干肌肉收集传感器数据和肌电图数据。利用传感器数据的特征，通过多元线性回归、回归学习和神经网络建模来预测肌肉激活情况。相关性分析检验了各个传感器对预测的贡献。神经网络模型的表现优于回归模型。在11名脊髓损伤儿童中，有4名儿童的多元线性回归预测与目标显著相关（<0.05），而决策树回归预测与5名儿童相关。神经网络预测与所有儿童相关。嵌入式传感器可捕获有关肌肉激活的有用信息，机器学习技术可用于为治疗师提供参考。有必要进一步开展工作以完善预测模型，并研究结果的可推广程度。
