Background and Objectives: The number of preterm babies is steadily growing world-wide and these neonates are at risk of neuro-motor-cognitive deficits. The observation of spontaneous movements in the first three months of age is known to predict such risk. However, the analysis by specifically trained physiotherapists is not suited for the clinical routine, motivating the development of simple computerized video analysis systems, integrated with a well-structured Biobank to make available for preterm babies a growing service with diagnostic, prognostic and epidemiological purposes.

Methods: MIMAS (Markerless Infant Movement Analysis System) is a simple, low-cost system of video analysis of spontaneous movements of newborns in their natural environment, based on a single standard RGB camera, without markers attached to the body. The original videos are transformed into binarized sequences highlighting the silhouette of the baby, in order to minimize the illumination effects and increase the robustness of the analysis; such sequences are then coded by a large set of parameters (39) related to the spatial and spectral changes of the silhouette. The parameter vectors of each baby were stored in the Biobank together with related clinical information.

Results: The preliminary test of the system was carried out at the Gaslini Pediatric Hospital in Genoa, where 46 preterm (PT) and 21 full-term (FT) babies (as controls) were recorded at birth (T0) and 8-12 weeks thereafter (T1). A simple statistical analysis of the data showed that the coded parameters are sensitive to the degree of maturation of the newborns (comparing T0 with T1, for both PT and FT babies), and to the conditions at birth (PT vs. FT at T0), whereas this difference tends to vanish at T1. Moreover, the coding method seems also able to detect the few ‘abnormal’ preterm babies in the PT populations that were analyzed as specific case studies.

Conclusions: Preliminary results motivate the adoption of this tool in clinical practice allowing for a systematic accumulation of cases in the Biobank, thus for improving the accuracy of data analysis performed by MIMAS and ultimately allowing the adoption of data mining techniques.

背景与目的：早产婴儿的数量在世界范围内稳步增长，这些新生儿处于神经运动认知缺陷的风险中。已知在头三个月内观察到自发运动可预测这种风险。但是，由经过专门培训的物理治疗师进行的分析不适合临床常规操作，从而促使开发简单的计算机视频分析系统，并与结构良好的Biobank集成在一起，从而为早产儿提供具有诊断，预后和流行病学目的的日益增长的服务。

方法： MIMAS（无标记婴儿运动分析系统）是一个简单，低成本的视频分析系统，基于单个标准RGB相机，对自然环境中新生儿的自发运动进行视频分析，而无需将标记物附着在身体上。原始视频被转换为突出婴儿轮廓的二值化序列，以最小化照明效果并提高分析的稳定性；然后，通过与轮廓的空间和光谱变化有关的一大组参数（39）对这些序列进行编码。每个婴儿的参数向量与相关的临床信息一起存储在Biobank中。

结果：系统的初步测试在热那亚的Gaslini儿科医院进行，在出生（T0）和8-12周时记录了46名早产（PT）和21名足月（FT）婴儿（作为对照）之后（T1）。对数据的简单统计分析表明，编码参数对新生儿的成熟程度（PT和FT婴儿的T0与T1进行比较）以及出生条件（PT与FT在T0时）均敏感。 ，而这种差异往往会在T1时消失。此外，编码方法似乎也能够检测到作为特定案例研究进行分析的PT人群中的少数“异常”早产儿。

结论：初步结果促使该工具在临床实践中得到采用，从而可以在生物库中系统地积累病例，从而提高MIMAS进行数据分析的准确性，并最终允许采用数据挖掘技术。