The importance of trunk motion in wearable based infant spontaneous movement analysis

The characteristics of spontaneous movements in infants are essential for the early detection of neurological pathologies, with the Prechtl method being a widely recognized approach. While the Prechtl method is effective in predicting motor risks, its reliance on the evaluator's expertise limits its scalability, particularly in low-income areas. In such contexts, the use of inertial sensors combined with automated analysis presents a promising accessible alternative; however, more research is necessary to get results comparable to those of the Precht method. This research aims to determine the more important metrics of trunk and limbs to assess spontaneous movement in healthy infants during the first semester of life as the basis of a sensor-based alternative. It was a cross-sectional study with 116 separate subjects divided into 3 groups: 0 M Group (N = 43), 3 M Group (N = 44), and 6 M (N = 29). Participants' movements were recorded using 6 wireless inertial sensors (4 limbs, thorax, and pelvis). Parameters from the acceleration signal were estimated in relation to velocity, cross-correlation, kurtosis, skewness, area, and periodicity. The different stages (0 M,3 M, and 6 M) have different profiles of accelerometric parameters. Trunk and limb parameters can differentiate between 0 of 3 months (13/25 trunk and 17/36 limb parameters) and between 0 and 6 months (10/25 trunk and 20/36 limb). Mainly, trunk parameters can differentiate between 3 and 6 months (9/25 trunk vs. 3/36 limb). Additionally, only 2 trunk parameters (kurtosis and periodicity) can differentiate the 3 stages. Wearable devices can effectively detect significant differences in spontaneous movements during the first six months of life, particularly trunk-related data. The extremities could be insufficient to distinguish movements between 3 and 6 months. On the other hand, two key parameters-kurtosis of thorax velocity and periodicity of trunk velocity-successfully differentiate between the three age groups analyzed.