Arquitectura de un sistema de medición de bioparámetros integrando señales inerciales-magnéticas y electromiográficas

Este trabajo presenta una arquitectura para la medición e integración de bioparámetros basado en unidades de procesamiento de movimiento inercial-magnético (MPUs) y electromiografía (EMG). Derivado de la arquitectura propuesta, se logró desarrollar un dispositivo llamado Imocap, el cual reúne y utiliza las mejores características de la tecnología MPU + EMG para realizar una medición completa en el segmento de brazo y antebrazo en el cuerpo humano. Se presenta en primer lugar la revisión bibliográfica de los métodos y herramientas para la captura del movimiento biomecánico, seguido de las técnicas y aplicaciones de la recolección de bioparámetros. Finalmente, se muestra la arquitectura y la descripción del sistema Imocap, algunas aplicaciones y discusión. Como trabajo futuro, Imocap tiene como objetivo proporcionar la información necesaria en un sistema de control electrónico para una plataforma de rehabilitación basada en exoesqueletos robóticos

A Review of EMG Techniques for Detection of Gait Disorders

Electromyography (EMG) is a commonly used technique to record myoelectric signals, i.e., motor neuron signals that originate from the central nervous system (CNS) and synergistically activate groups of muscles resulting in movement. EMG patterns underlying movement, recorded using surface or needle electrodes, can be used to detect movement and gait abnormalities. In this review article, we examine EMG signal processing techniques that have been applied for diagnosing gait disorders. These techniques span from traditional statistical tests to complex machine learning algorithms. We particularly emphasize those techniques are promising for clinical applications. This study is pertinent to both medical and engineering research communities and is potentially helpful in advancing diagnostics and designing rehabilitation devices

Research on Lower Limb Motion Recognition Based on Fusion of sEMG and Accelerometer Signals

Since surface electromyograghic (sEMG) signals are non-invasive and capable of reflecting humans’ motion intention, they have been widely used for the motion recognition of upper limbs. However, limited research has been conducted for lower limbs, because the sEMGs of lower limbs are easily affected by body gravity and muscle jitter. In this paper, sEMG signals and accelerometer signals are acquired and fused to recognize the motion patterns of lower limbs. A curve fitting method based on median filtering is proposed to remove accelerometer noise. As for movement onset detection, an sEMG power spectral correlation coefficient method is used to detect the start and end points of active signals. Then, the time-domain features and wavelet coefficients of sEMG signals are extracted, and a dynamic time warping (DTW) distance is used for feature extraction of acceleration signals. At last, five lower limbs’ motions are classified and recognized by using Gaussian kernel-based linear discriminant analysis (LDA) and support vector machine (SVM) respectively. The results prove that the fused feature-based classification outperforms the classification with only sEMG signals or accelerometer signals, and the fused feature can achieve 95% or higher recognition accuracy, demonstrating the validity of the proposed method