Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
AdventHealth Research Institute Department of Neuroscience, AdventHealth, Orlando, FL, USA.
J Neuroeng Rehabil. 2024 Feb 29;21(1):31. doi: 10.1186/s12984-024-01327-8.
Children and adolescents with neuromotor disorders need regular physical activity to maintain optimal health and functional independence throughout their development. To this end, reliable measures of physical activity are integral to both assessing habitual physical activity and testing the efficacy of the many interventions designed to increase physical activity in these children. Wearable accelerometers have been used for children with neuromotor disorders for decades; however, studies most often use disorder-specific cut points to categorize physical activity intensity, which lack generalizability to a free-living environment. No reviews of accelerometer data processing methods have discussed the novel use of machine learning techniques for monitoring physical activity in children with neuromotor disorders.
In this narrative review, we discuss traditional measures of physical activity (including questionnaires and objective accelerometry measures), the limitations of standard analysis for accelerometry in this unique population, and the potential benefits of applying machine learning approaches. We also provide recommendations for using machine learning approaches to monitor physical activity.
While wearable accelerometers provided a much-needed method to quantify physical activity, standard cut point analyses have limitations in children with neuromotor disorders. Machine learning models are a more robust method of analyzing accelerometer data in pediatric neuromotor disorders and using these methods over disorder-specific cut points is likely to improve accuracy of classifying both type and intensity of physical activity. Notably, there remains a critical need for further development of classifiers for children with more severe motor impairments, preschool aged children, and children in hospital settings.
患有神经运动障碍的儿童和青少年需要定期进行身体活动，以维持其整个发育过程中的最佳健康和功能独立性。为此，可靠的身体活动测量方法对于评估习惯性身体活动和测试旨在增加这些儿童身体活动的许多干预措施的效果都是必不可少的。可穿戴加速度计已用于神经运动障碍儿童数十年；然而，大多数研究经常使用特定于疾病的切点来分类身体活动强度，这缺乏对自由生活环境的普遍性。目前还没有关于加速度计数据处理方法的综述讨论过将机器学习技术用于监测神经运动障碍儿童身体活动的新用途。
在本叙述性综述中，我们讨论了身体活动的传统测量方法（包括问卷和客观加速度计测量）、标准分析在该特殊人群中对加速度计的局限性，以及应用机器学习方法的潜在益处。我们还为使用机器学习方法监测身体活动提供了建议。
虽然可穿戴加速度计为量化身体活动提供了急需的方法，但标准切点分析在神经运动障碍儿童中存在局限性。机器学习模型是分析儿科神经运动障碍中加速度计数据的更强大方法，使用这些方法而不是特定于疾病的切点，很可能会提高身体活动的类型和强度分类的准确性。值得注意的是，仍然迫切需要进一步开发针对运动障碍更严重的儿童、学龄前儿童和住院儿童的分类器。
