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Muscular load characterization during isometric shoulder abductions with varying force
Iridiastadi H.a, Nussbaum M.A.b, van Dieen J.H.c
a Department of Industrial Engineering, Institut Teknologi Bandung, Indonesia
b Department of Industrial and Systems Engineering, Virginia Tech, United States
c Faculty of Human Movement Sciences, Vrije Universiteit Amsterdam, Netherlands
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]This study sought to characterize muscle loading and fatigue during static shoulder abductions with varying force. In a supine posture, participants maintained fixed shoulder abductions against a time-varying external resistance, generated by a dynamometer-spring mechanism. Patterns (cumulative distribution) of the external resistance were varied by selecting different 10th and 90th percentiles of the distribution. Dynamometer angular velocities were also varied, to reflect different rates of cyclic muscle contraction. The degree of local fatigue development was assessed by common measures, including endurance time, strength reduction, and perceived discomfort. Myoelectric (EMG) signals were continuously obtained from the middle deltoid muscle throughout experimental exercise (60 min max). Changes in EMG root-mean-square (RMS) and spectral measures (derived from 1-s windows at peaks in the cyclic contractions) were used as manifestations of muscle fatigue. For each minute, the RMS signal was further reduced using two methods, the cumulative probability distribution of EMG (CPDE) and exposure variation analysis (EVA). The former resulted in three percentile values (10th, 50th, and 90th), whereas the latter method resulted in 10 different measures (grouped by EMG activity level and duration). A main finding of the study was the applicability of several common fatigue indicators for these cyclic, repetitive exertions. Overall, the use of CPDE and EVA to characterize task differences and predict muscle fatigue was found to have limited value. © 2007 Elsevier Ltd. All rights reserved.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]CPDE,Electromyography,EVA,Exposure assessment,Localized muscle fatigue[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1016/j.jelekin.2007.01.011[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]