Filtering the surface EMG signal: Movement artifact and baseline noise contamination |
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| Authors: | Carlo J De Luca L Donald Gilmore Mikhail Kuznetsov Serge H Roy |
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| Institution: | 1. Delsys Inc., Boston MA, USA;2. NeuroMuscular Research Center, Boston University, 19 Deerfield St, Boston MA, USA;1. SMI, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark;2. School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad, Pakistan;3. Center of Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand;4. Institute of Biomedical Engineering, University of New Brunswick, Canada;5. Faculty of Engineering and Applied Sciences, Riphah International University Islamabad, Pakistan;6. Center for Robotics Research, Department of Informatics, King’s College London, London, United Kingdom;1. Department of Health Science and Technology, Aalborg University, Fredrik bajers vej 7 D3, 9220 Aalborg, Denmark;2. Department of Robotics and Intelligent Machine Engineering, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, H-12, Islamabad, Pakistan;3. Center for Robotics Research, Department of Informatics, King’s College London, 30 Aldwych, WC2B 4BG, London, United Kingdom;1. ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France;2. Ipsen Innovation, Les Ulis, France;3. Department of Neurological Rehabilitation, University Hospital of Toulouse, Hôpital de Rangueil, Toulouse, France;4. Department of Functional Physiological Explorations, University Hospital of Toulouse, Hôpital de Rangueil, Toulouse, France;1. Koç University School of Medicine, Istanbul, Turkey;2. Physical Medicine and Rehabilitation Department, Bagcilar Training & Research Hospital, Istanbul, Turkey;1. Dept. of Electronics and Communication Engineering, Amal Jyothi College of Engineering, Kanjirappally, Kerala, India;2. SENSE, VIT Vellore, Tamil Nadu, India |
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| Abstract: | 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 the movement artifact noise. These noise sources have frequency spectra that contaminate the low-frequency part of the sEMG frequency spectrum. There are many factors which must be taken into consideration when determining the appropriate filter specifications to remove these artifacts; they include the muscle tested and type of contraction, the sensor configuration, and specific noise source. The band-pass determination is always a compromise between (a) reducing noise and artifact contamination, and (b) preserving the desired information from the sEMG signal. This study was designed to investigate the effects of mechanical perturbations and noise that are typically encountered during sEMG recordings in clinical and related applications. The analysis established the relationship between the attenuation rates of the movement artifact and the sEMG signal as a function of the filter band pass. When this relationship is combined with other considerations related to the informational content of the signal, the signal distortion of filters, and the kinds of artifacts evaluated in this study, a Butterworth filter with a corner frequency of 20 Hz and a slope of 12 dB/oct is recommended for general use. The results of this study are relevant to biomechanical and clinical applications where the measurements of body dynamics and kinematics may include artifact sources. |
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