A new parameter for quantifying the variability of surface electromyographic signals during gait: The occurrence frequency
| Autor: | Laura Burattini, Alessandro Mengarelli, Annachiara Strazza, Sandro Fioretti, Valentina Agostini, Francesco Di Nardo, Marco Knaflitz |
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| Rok vydání: | 2017 |
| Předmět: |
Adult
Male medicine.medical_specialty Speech recognition 0206 medical engineering Biophysics Neuroscience (miscellaneous) Hamstring Muscles Surface EMG Walking 02 engineering and technology Biceps Quadriceps Muscle Age and gender Young Adult 03 medical and health sciences Occurrence frequency Computer-Assisted 0302 clinical medicine Physical medicine and rehabilitation Gait (human) medicine Humans Natural variability Variability Muscle Skeletal Child Gait Retrospective Studies Statistical gait analysis Electromyography Female Healthy Volunteers Signal Processing Computer-Assisted Neurology (clinical) business.industry Healthy subjects Muscle activation Skeletal 020601 biomedical engineering Signal Processing Muscle business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Electromyography and Kinesiology. 36:25-33 |
| ISSN: | 1050-6411 |
| DOI: | 10.1016/j.jelekin.2017.06.006 |
| Popis: | Natural variability of myoelectric activity during walking was recently analyzed considering hundreds of strides. This allowed assessing a parameter seldom considered in classic surface EMG (sEMG) studies: the occurrence frequency, defined as the frequency each muscle activation occurs with, quantified by the number of strides when a muscle is recruited with that specific activation modality. Aim of present study was to propose the occurrence frequency as a new parameter for assessing sEMG-signal variability during walking. Aim was addressed by processing sEMG signals acquired from Gastrocnemius Lateralis, Tibialis Anterior, Rectus Femoris and Biceps femoris in 40 healthy subjects in order to: (1) show that occurrence frequency is not correlated with ON/OFF instants (R mean = 0.11 ± 0.07; P > 0.05) and total time of activation (R mean = 0.15 ± 0.08; P > 0.05); (2) confirm the above results by two handy examples of application (analysis of gender and age) which highlighted that significant (P |
| Databáze: | OpenAIRE |
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