Comparación de la normalización lineal de la escala de tiempos con el registro funcional continuo en movimientos cíclicos del cuello

La normalización de la escala de tiempos es un paso necesario para aplicar las técnicas de análisis de datos funcionales al estudio de los movimientos humanos. La técnica estándar es la normalización lineal que, a pesar de su sencillez, puede ser ineficaz para reducir la variabilidad en la duración de los eventos [1]. Una alternativa es el registro continuo que supone ajustar de forma no lineal la escala de tiempos [2]. En este caso, la información temporal se mantiene en las funciones warping, que relacionan el tiempo modificado frente al promedio. No obstante, este procedimiento es complejo y computacionalmente costoso. Además, se ha señalado que en movimientos cíclicos como el de masticación apenas hay diferencias entre métodos [3]. En este trabajo se comparan ambos métodos en el caso del movimiento cíclico de flexo-extensión del cuello, analizando las funciones ángulo y velocidad angular. A partir de una base de datos con 437 ciclos completos extensión-flexión de cuello, se han aplicado ambos tipos de normalización de la escala temporal. Se han analizado las diferencias en las curvas medias y las desviaciones típicas funcionales, cuyos valores medios e intervalos de confianza se han establecido mediante un proceso de bootstrapping. Los resultados muestran que apenas hay diferencias en las curvas medias obtenidas por ambos procedimientos, aunque sí en las desviaciones típicas funcionales, que son algo menores en el caso del registro no lineal. Por otra parte, los resultados obtenidos con el registro no lineal son diferentes cuando se usan las curvas de posición o las de velocidad como referencia para el reescalado. Estos resultados sugieren que el registro no lineal, aunque puede ser útil para el análisis de señales no periódicas o donde no haya que analizar a la vez funciones y sus derivadas, no ofrece ventajas importantes frente a la normalización lineal en el caso de los movimientos cíclicos. REFERENCIAS [1] Page, A., & Epifanio, I. (2007). A simple model to analyze the effectiveness of linear time normalization to reduce variability in human movement analysis. Gait & posture, 25(1), 153-156. [2] Page, A., et al. (2006). Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data. Journal of biomechanics, 39(13), 2526-2534. [3] Crane, E. et al.. (2010). Effect of registration on cyclical kinematic data. Journal of biomechanics, 43(12), 2444-2447.

Movement Analysis in Orthopedics and Trauma Surgery – Measurement Systems and Clinical Applications

Background Technical development lead to an enhancement of clinical movement analysis in the last few decades and expanded its research and clinical applications. Since the mid 20th century, human movement analysis has made its way into clinical practice, e.g. in treating poliomyelitis and infantile cerebral palsy. Today, it has a wide range of applications in various clinical areas. The aim of this narrative review is to illustrate the variety of camera-based systems for human movement analysis and their clinical applications, specifically in the field of orthopaedics and trauma surgery (O/U). Benefits and limitations of each system are shown. Future development and necessary improvements are discussed. Material and Methods A selective literature review was undertaken with the databases PubMed and Google Scholar using keywords related to clinical human movement analysis in the field of orthopaedics and trauma surgery. Furthermore standard book references were included. Results Common video camera systems (VS) are used for basic visual movement analysis. Instrumented movement analysis systems include marker-based systems (MBS), markerless optical systems (MLS) and rasterstereographic analysis systems (VRS). VS, MBS and MLS have clinical use for dynamic examination of patients with various disorders in movement and gait. Among such are e.g. neuro-orthopaedic disorders, muscular insufficiencies, degenerative and post-trauma deficiencies with e.g. resultant pathologic leg axis. Besides the measurement of kinematic data by MBS and MLS, the combination with kinetic measurements to detect abnormal loading patterns as well as the combination with electromyography (EMG) to detect abnormal muscle function is a great advantage. Validity and reliability of kinematic measurements depend on the camera systems (MBS, MLS), the applied marker models, the joints of interest and the observed movement plane. Movements in the sagittal plane of the hip and knee joint, pelvic rotation and tilt as well as hip abduction are generally measured with high reliability. In the frontal and transverse planes of the knee and ankle joint substantial angular variabilities were noted due to the small range of motion of the joints in these planes. Soft tissue artefacts and marker placement are the biggest sources of errors. So far MLS did not improve these limitations. MBS are most accurate and remain the gold-standard in clinical and scientific movement analysis. VRS is used clinically for static 3D-analysis of the trunk posture and spine deformities. Current systems allow the dynamic measurement and visualisation of trunk and spine movement in 3D during gait and running. Planar x-ray-imaging (Cobbʼs angle) and to some extent cross sectional imaging with CT-scan or MRI are commonly used for the evaluation of patients with spinal deformities. VRS offers functional 3D data of trunk and spine deformities without radiation exposure, thus allowing safer clinical monitoring of the mainly infantile and adolescent patients. The accuracy, validity and reliability of measurements of different VRS-systems for the clinical use has been proven by several studies. Conclusion The instrumented movement analysis is an additional tool that aids clinical practitioners of O/U in the dynamic assessment of pathologic movement and loading patterns. In conjunction with common radiologic imaging it aids in the planning of type and extent of corrective surgical interventions. In the field of orthopaedics and trauma surgery movement analysis can help as an additional diagnostic tool to develop therapeutic strategies and evaluate clinical outcomes.