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The time-dependent shift in the spectral content of the surface myoelectric signal to lower frequencies has proven to be a useful tool for assessing localized muscle fatigue. Unfortunately, the technique has been restricted to constant-force, isometric contractions because of limitations in the processing methods used to obtain spectral estimates. A novel(More)
Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited(More)
This paper describes an in vitro method for comparing surface-detected electromyographic median frequency (MF) and conduction velocity (CV) parameters with histochemical measurements of muscle fiber type composition and cross-sectional area (CSA). Electromyographic signals were recorded during electrically elicited tetanic contractions from rat soleus,(More)
A new approach to estimating the frequency compression of the surface EMG signal during cyclical dynamic exercise is described. The basic properties of the method are first developed using simulated EMG signals. Spectral compression is measured by defining the instantaneous median frequency from time-frequency representations of the signal derived from a(More)
The purpose of this article is to provide an overview of research to develop surface electromyographic (EMG) measurements for classification of paraspinal muscle impairments in persons with low back pain (LBP). The process of developing laboratory and clinically based protocols is described. Results of studies to evaluate the reliability of these(More)
We present a dynamic neural network (DNN) solution for detecting time-varying occurrences of tremor and dyskinesia at 1 s resolution from time series data acquired from surface electromyographic (sEMG) sensors and tri-axial accelerometers worn by patients with Parkinson's disease (PD). The networks were trained and tested on separate datasets, each(More)
Automatic tracking of movement disorders in patients with Parkinson's disease (PD) is dependent on the ability of machine learning algorithms to resolve the complex and unpredictable characteristics of wearable sensor data. The challenge reflects the variety of movement disorders that fluctuate throughout the day which can be confounded by voluntary(More)
The surface electromyographic (sEMG) signal that originates in the muscle is inevitably contaminated by various noise signals or artifacts that originate at the skin-electrode interface, in the electronics that amplifies the signals, and in external sources. Modern technology is substantially immune to some of these noises, but not to the baseline noise and(More)
H+ accumulation at the sarcolemma is believed to play a key role in determining the electrophysiological correlates of fatigue. This paper describes an in vitro method to externally manipulate muscle pH while measuring the resultant effect on surface-detected median frequency (MDF) and conduction velocity (CV) parameters. Hamster muscle diaphragm strips (n(More)
Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in(More)