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Removing artifacts from biomedical signals, such as surface electromyography (sEMG), has become a major research topic in biomedical signal processing. In electromyography signals, a source of contamination is the electrophysiological signal of the heart (ECG signals). This contamination influences features extracted from the sEMG, especially during low-activity measurements of the muscles such as...
In this paper we introduce a speech recognition system based on myoelectric signals. The system handles audible and non-audible speech. Major challenges in surface electromyography based speech recognition ensue from repositioning electrodes between recording sessions, environmental temperature changes, and skin tissue properties of the speaker. In order to reduce the impact of these factors, we investigate...
A number of digital signal processing (DSP) techniques are being applied to surface electromyography (SEMG) signals to extract detailed features of the signal. Fast Fourier transform (FFT) is one of the most common methods for analyzing the signal whether it is filtered or not. Another DSP technique is referred to as wavelet analysis, a method that is gaining more use in analyzing SEMG signals. This...
Because of the difficulty in analysis and interpretation of surface electromyography (sEMG), the specific muscle contraction synergy associated with low back pain continues to be debated. Streaming topography is a novel method of continuously visualizing the distribution of sEMG signals during dynamic motion to provide a more comprehensive examination and subsequent insight into the synergy of muscle...
The purpose of this paper is to present preliminary evidence that data mining and artificial intelligence systems may allow one to recognize the presence and severity of motor fluctuations in patients with Parkinson's disease (PD). We hypothesize that movement disorders in late-stage PD present with identifiable and predictable features that can be derived from accelerometer (ACC) and surface electromyographic...
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