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  • 1.
    Abbaspour, Sara
    et al.
    Amirkabir University of Technology, Tehran, Iran.
    Fallah, Ali
    Amirkabir University of Technology, Tehran, Iran.
    A Combination Method for Electrocardiogram Rejection from Surface Electromyogram2014Inngår i: Open Biomedical Engineering Journal, ISSN 1874-1207, E-ISSN 1874-1207, Vol. 8, nr 1, s. 13-19Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.

  • 2.
    Khan, Taha
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. Dalarna University.
    Westin, Jerker
    Dalarna University.
    Dougherty, Mark
    Dalarna University.
    Motion Cue Analysis for Parkinsonian Gait Recognition2013Inngår i: Open Biomedical Engineering Journal, ISSN 1874-1207, E-ISSN 1874-1207, Vol. 7, nr 1, s. 1-8Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson’s disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject’s body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

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