RESUMEN
Several EMG-based approaches to muscle fatigue assessment have recently been proposed in the literature. In this work, two multivariate fatigue indices developed by the authors: a generalized mapping index (GMI) and the first component of principal component analysis (PCA) were compared to three univariate indices: Dimitrov's normalized spectral moments (NSM), Gonzalez-Izal's waveletbased indices (WI), and Talebinejad's fractal-based Hurst Exponent (HE). Nine healthy participants completed two repetitions of fatigue tests during isometric, cyclic and random fatiguing contractions of the biceps brachii. The fatigue assessments were evaluated in terms of a modified sensitivity to variability ratio yielding the following scores (mean±std.dev.): PCA: (12.6±5.6), GMI: (11.5±5.4), NSM: (10.3±5.4), WI: (8.9±4.6), HE: (8.0±3.3). It was shown that PCA statistically outperformed WI and HE (p<0.01) and that GMI outperformed HE (p<0.02). There was no statistical difference among NSM, WI and HE (p>0.2). It was found that taking the natural logarithm of NSM and WI, although reducing the parameters' sensitivity to fatigue, increased SVR scores by reducing variability.
Asunto(s)
Algoritmos , Electromiografía/métodos , Contracción Muscular/fisiología , Fatiga Muscular/fisiología , Músculo Esquelético/fisiología , Adulto , Interpretación Estadística de Datos , Femenino , Humanos , Masculino , Análisis de Componente Principal , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Análisis de OndículasRESUMEN
A novel approach to fatigue assessment during dynamic contractions was proposed which projected multiple surface myoelectric parameters onto the vector connecting the temporal start and end points in feature-space in order to extract the long-term trend information. The proposed end to end (ETE) projection was compared to traditional principal component analysis (PCA) as well as neural-network implementations of linear (LPCA) and non-linear PCA (NLPCA). Nine healthy participants completed two repetitions of fatigue tests during isometric, cyclic and random fatiguing contractions of the biceps brachii. The fatigue assessments were evaluated in terms of a modified sensitivity to variability ratio (SVR) and each method used a set of time-domain and frequency-domain features which maximized the SVR. It was shown that there was no statistical difference among ETE, PCA and LPCA (p>0.99) and that all three outperformed NLPCA (p<0.0022). Future work will include a broader comparison of these methods to other new and established fatigue indices.
Asunto(s)
Contracción Muscular/fisiología , Fatiga Muscular/fisiología , Análisis de Componente Principal , Adulto , Análisis de Varianza , Electromiografía , Femenino , Humanos , Masculino , Redes Neurales de la ComputaciónRESUMEN
The repeatability of a spectral surface electromyography-based fatigue assessment strategy was evaluated. Variability of two fatigue-trend tracking parameters was used as an indicator for repeatability. The parameters were the natural logarithm of the slope of linear mean frequency decline lnMF(S) and the percent drop in mean frequency MF(D). The coefficient of variation CoV was used as the metric for repeatability, representing the ratio of the standard deviation to the mean of repeated measures from the same individual. Five weekly fatigue tests on the right biceps brachii were conducted on 11 participants with a fatiguing regime comprising of alternating static and cyclic segments, collecting seven channels of differential EMG. The resulting 95% confidence intervals of the CoV were: 15.38-24.87% (Static lnMF(S)), 12.21-23.36% (Cyclic lnMF(S)), 13.18-21.85% (Static MF(D)), and 12.37-24.39% (Cyclic MF(D)). There was no statistically significant difference in repeatability between any combination of parameter and types of motion.
Asunto(s)
Electromiografía/métodos , Fatiga Muscular/fisiología , Adulto , Análisis de Varianza , Femenino , Humanos , Masculino , Reproducibilidad de los ResultadosRESUMEN
The mapping index (MI) is a fatigue assessment index that uses multiple time-domain myoelectric features to train an artificial neural network (ANN) to track the progression of fatigue. This work showed that mapping functions trained using data from independent subjects and contraction conditions to yield a generalized mapping index (GMI) can assess fatigue as well as functions trained with subject and contraction-specific data to yield MI. Surface myoelectric signals were collected from nine healthy participants during isometric, cyclic and random fatiguing contractions. Two datasets were collected: one for tuning the functions and the other for testing. The performance of fatigue indices was evaluated using a newly proposed piece-wise linear signal to noise ratio. ANN based indices were compared to normalized spectral moments (NSM) and mean frequency (MF). GMI performed as well as MI and outperformed NSM and MF demonstrating that subject and contraction-specific baseline data is not needed in order to train a mapping function which can effectively assess fatigue.
Asunto(s)
Algoritmos , Electromiografía/métodos , Contracción Muscular/fisiología , Fatiga Muscular/fisiología , Músculo Esquelético/fisiología , Examen Físico/métodos , Adulto , Femenino , Humanos , Masculino , Análisis Multivariante , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
A novel approach to muscle fatigue assessment is proposed. A function is used to map multiple myoelectric parameters representing segments of myoelectric data to a fatigue estimate for that segment. An artificial neural network is used to tune the mapping function and time-domain features are used as inputs. Two fatigue tests were conducted on five participants in each of static, cyclic and random conditions. The function was tuned with one data set and tested on the other. Performance was evaluated based on a signal to noise metric which compared variability due to fatigue factors with variability due to nonfatiguing factors. Signal to noise ratios for the mapping function ranged from 7.89 under random conditions to 9.69 under static conditions compared to 3.34-6.74 for mean frequency and 2.12-2.63 for instantaneous mean frequency indicating that the mapping function tracks the myoelectric manifestations of fatigue better than either mean frequency or instantaneous mean frequency under all three contraction conditions.