Feasibility of Wearable Electromyography (EMG) to Assess Construction Workers’ Muscle Fatigue

Conference paper


Due to the labor-intensive nature of construction tasks, a large number of construction workers frequently suffer from excessive muscle fatigue. Workers’ muscle fatigue can adversely affect their productivity, safety, and well-being. Several attempts have been made to assess workers’ fatigue using subjective methods (e.g., fatigue questionnaire). Despite, the success of subjective methods in assessing workers’ fatigue in a long period, these methods have limited utility on construction sites. For instance, these methods interrupt workers’ ongoing tasks. These methods are also subject to high biases. To address these issues, this study aims to examine the feasibility of a wearable Electromyography (EMG) sensor to measure the electrical impulses produced by workers’ muscles as a means to continuously evaluate workers muscle fatigue without interfering with their ongoing tasks. EMG signals were acquired from eight subjects while lifting a concrete block using their upper limbs (i.e., elbow and shoulder muscles). As the first step, filtering methods (e.g., bandpass filter, rolling filter, and Hampel filter) were applied to reduce EMG signal artifacts. After removing signal artifacts, to examine the potential of EMG in measuring workers’ muscle fatigue, various EMG signal metrics were calculated in time domain (e.g., Signal Mean Absolute Value (MAV) and Root Mean Square (RMS)) and frequency domain (e.g., Median Frequency (MDF) and Mean Frequency (MEF)). Subjects’ perceived muscle exertion (Borg CR-10 scale) was used as a baseline to compare the muscle exertion identified by EMG signals. Results show a significant difference in EMG parameters while subjects were exerting different fatigue levels. Results confirm the feasibility of the wearable EMG to evaluate workers’ muscle fatigue as means for assessing their physical stress on construction sites.


Wearable electromyography (EMG) Local muscle fatigue Physical fatigue Wearable biosensors Workers’ productivity Safety Health Signal processing 



The authors would like to acknowledge their industry partners for their considerable help in collecting data. This publication was supported by the Grant of Cooperative Agreement Number, T42OH008455, funded by the Centers for Disease Control and Prevention. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of MichiganAnn ArborUSA

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