Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Calambrone, 56128, Pisa, Italy.
Tuscan Ph.D. Programme of Neuroscience, University of Florence, Florence, Italy.
J Neuroeng Rehabil. 2023 May 6;20(1):62. doi: 10.1186/s12984-023-01182-z.
Nowadays, wearable sensors are widely used to quantify physical and motor activity during daily life, and they also represent innovative solutions for healthcare. In the clinical framework, the assessment of motor behaviour is entrusted to clinical scales, but they are dependent on operator experience. Thanks to their intrinsic objectivity, sensor data are extremely useful to provide support to clinicians. Moreover, wearable sensors are user-friendly and compliant to be used in an ecological environment (i.e., at home). This paper aims to propose an innovative approach useful to predict clinical assessment scores of infants' motor activity.
Starting from data acquired by accelerometers placed on infants' wrists and trunk during playtime, we exploit the method of functional data analysis to implement new models combining quantitative data and clinical scales. In particular, acceleration data, transformed into activity indexes and combined with baseline clinical data, represent the input dataset for functional linear models.
Despite the small number of data samples available, results show correlation between clinical outcome and quantitative predictors, indicating that functional linear models could be able to predict the clinical evaluation. Future works will focus on a more refined and robust application of the proposed method, based on the acquisition of more data for validating the presented models.
ClincalTrials.gov; NCT03211533. Registered: July, 7th 2017. ClincalTrials.gov; NCT03234959. Registered: August, 1st 2017.
如今，可穿戴传感器被广泛用于量化日常生活中的身体和运动活动，它们也代表了医疗保健领域的创新解决方案。在临床框架中，运动行为的评估依赖于临床量表，但这些量表依赖于操作者的经验。由于传感器数据具有内在的客观性，因此非常有助于为临床医生提供支持。此外，可穿戴传感器易于使用，并且符合在生态环境（例如在家中）中使用的要求。本文旨在提出一种创新方法，用于预测婴儿运动活动的临床评估分数。
从婴儿在玩耍时手腕和躯干上佩戴的加速度计获取的数据出发，我们利用功能数据分析方法来实现新的模型，这些模型将定量数据和临床量表相结合。具体来说，将加速度数据转换为活动指数，并与基线临床数据相结合，作为功能线性模型的输入数据集。
尽管可用的数据样本数量较少，但结果表明临床结果与定量预测指标之间存在相关性，表明功能线性模型能够预测临床评估结果。未来的工作将集中于更精细和稳健的应用提出的方法，方法是基于采集更多的数据来验证所提出的模型。
ClincalTrials.gov；NCT03211533。注册时间：2017 年 7 月 7 日。ClincalTrials.gov；NCT03234959。注册时间：2017 年 8 月 1 日。
