A dynamical system analysis of the development of spontaneous lower extremity movements in newborn and young infants.
ABSTRACT This study's aim was to evaluate the characteristics of newborn and young infants' spontaneous lower extremity movements by using dynamical systems analysis. Participants were 8 healthy full-term newborn infants (3 boys, 5 girls, mean birth weight and gestational age were 3070.6 g and 39 weeks). A tri-axial accelerometer measured limb movement acceleration in 3-dimensional space. Movement acceleration signals were recorded during 200 s from just below the ankle when the infant was in an active alert state and lying supine (sampling rate 200 Hz). Data were analyzed linearly and nonlinearly. As a result, the optimal embedding dimension showed more than 5 at all times. Time dependent changes started at 6 or 7, and over the next four months decreased to 5 and from 6 months old, increased. The maximal Lyapnov exponent was positive for all segments. The mutual information is at its greatest range at 0 months. Between 3 and 4 months the range in results is narrowest and lowest in value. The mean coefficient of correlation for the x-axis component was negative and y-axis component changed to a positive value between 1 month old and 4 months old. Nonlinear time series analysis suggested that newborn and young infants' spontaneous lower extremity movements are characterized by a nonlinear chaotic dynamics with 5 to 7 embedding dimensions. Developmental changes of an optimal embedding dimension showed a U-shaped phenomenon. In addition, the maximal Lyapnov exponents were positive for all segments (0.79-2.99). Infants' spontaneous movement involves chaotic dynamic systems that are capable of generating voluntary skill movements.
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ABSTRACT: Qualitative assessment of spontaneous motor activity in early infancy is widely used in clinical practice. It enables the description of maturational changes of motor behavior in both healthy infants and infants who are at risk for later neurological impairment. These assessments are, however, time-consuming and are dependent upon professional experience. Therefore, a simple physiological method that describes the complex behavior of spontaneous movements (SMs) in infants would be helpful. In this methodological study, we aimed to determine whether time series of motor acceleration measurements at 40-44 weeks and 50-55 weeks gestational age in healthy infants exhibit fractal-like properties and if this self-affinity of the acceleration signal is sensitive to maturation. Healthy motor state was ensured by General Movement assessment. We assessed statistical persistence in the acceleration time series by calculating the scaling exponent α via detrended fluctuation analysis of the time series. In hand trajectories of SMs in infants we found a mean α value of 1.198 (95 % CI 1.167-1.230) at 40-44 weeks. Alpha changed significantly (p = 0.001) at 50-55 weeks to a mean of 1.102 (1.055-1.149). Complementary multilevel regression analysis confirmed a decreasing trend of α with increasing age. Statistical persistence of fluctuation in hand trajectories of SMs is sensitive to neurological maturation and can be characterized by a simple parameter α in an automated and observer-independent fashion. Future studies including children at risk for neurological impairment should evaluate whether this method could be used as an early clinical screening tool for later neurological compromise.Experimental Brain Research 05/2013; 227(4). DOI:10.1007/s00221-013-3504-6 · 2.17 Impact Factor
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ABSTRACT: Variability has been perceived to be beneficial to movement organization and execution, being essential to selection of movement patterns during motor development, to obtain flexible patterns and adaptability to different task demands. Human movement variability can be measured by linear and nonlinear tools. Recently, nonlinear techniques have been used successfully to give insight into motor skills control in children, and be able to discriminate pathologic and non-pathologic children. For that, this paper is the first to review systematically studies that used nonlinear measures in children. We intend to describe which mathematical tools are utilized to analyze quality and structure of variability, the factors that influence this variability and methodological procedures which are considered for its analysis, and how they are interpreted in child motor development field. A search was performed by one reviewer in relevant databases and the quality appraisal was conducted independently by two reviewers. In all, 27 articles were identified and 20 were selected for the present review. It was detected a large variation in sample characteristics and methodological issues among studies. In fact, the main importance of this review was due to the attempt to define some parameters and standardize some values for typical children and children with disabilities. It is noted that the results from nonlinear techniques depend on the task being analyzed, the age and the type of mathematical technique chosen. The presence of disability is associated to decreases in complexity and nonlinear tools were considered sensible to investigate the effectiveness of practice and intervention in typical children and children with cerebral palsy. Furthermore, future studies should be more careful in standardizing selection, recruitment and explaining missing data. Future reports also should present details of their results and limitations to favor comparisons and helping in formulating new research questions.Research in developmental disabilities 06/2013; 34(9):2810-2830. DOI:10.1016/j.ridd.2013.05.031 · 4.41 Impact Factor
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ABSTRACT: Background Existing motor pattern assessment methods, such as digital cameras and optoelectronic systems, suffer from object obstruction and require complex setups. To overcome these drawbacks, this paper presents a novel approach for biomechanical evaluation of newborn motor skills development. Multi-sensor measurement system comprising pressure mattress and IMUs fixed on trunk and arms is proposed and used as alternative to existing methods. Observed advantages seem appealing for the focused field and in general. Combined use of pressure distribution data and kinematic information is important also for posture assessment, ulcer prevention, and non-invasive sleep pattern analysis of adults. Methods Arm kinematic parameters, such as root-mean-square acceleration, spectral arc length of hand velocity profile, including arm workspace surface area, and travelled hand path are obtained with the multi-sensor measurement system and compared to normative motion capture data for evaluation of adequacy. Two IMUs per arm, only one IMU on upper arm, and only one IMU on forearm sensor placement options are studied to assess influence of system configuration on method precision. Combination of pressure mattress and IMU fixed on the trunk is used to measure trunk position (obtained from mat), rotation (from IMUs) and associated movements on surface (from both). Measurement system is first validated on spontaneous arm and trunk movements of a dedicated baby doll having realistic anthropometric characteristics of newborns. Next, parameters of movements in a healthy infant are obtained with pressure mattress, along with trunk and forearm IMU sensors to verify appropriateness of method and parameters. Results Evaluation results confirm that full sensor set, comprising pressure mattress and two IMUs per arm is a reliable substitution to optoelectronic systems. Motor pattern parameter errors are under 10% and kinematic estimation error is in range of 2 cm. Although, use of only forearm IMU is not providing best possible kinematic precision, the simplicity of use and still acceptable accuracy are convincing for frequent practical use. Measurements demonstrated system high mobility and usability. Conclusions Study results confirm adequacy of the proposed multi-sensor measurement system, indicating its enviable potential for accurate infant trunk posture and arm movement assessment.Journal of NeuroEngineering and Rehabilitation 09/2014; 11(1):133. DOI:10.1186/1743-0003-11-133 · 2.62 Impact Factor