2.1. Workers’ Ergonomic Risk Assessment
Understanding the nature and preventing exacerbation of musculoskeletal disorders (MSDs) as a result of work-related injuries have been subject to many past studies. MSDs are referred to as the situations involving the nerves, tendons, muscles, and supporting structures of the body are injured. In particular, low back pain, shoulder injuries, and distal upper extremity disorders, including tendonitis, epicondylitis, and carpal tunnel syndrome, can be named as such disorders [
7]. Work-related MSDs, or WMSDs, are among the most reported jobsite injuries that can negatively affect workers’ health, well-being, and productivity [
1,
2,
8]. This has been identified by many previous past studies conducted in collaboration with national and international safety organization. For example, in a research study in 2004, Waters [
7] summarized the findings of two research agendas to increase our understanding of the approaches that can prevent this kind of disorders. The first agenda was based on the data gathered from several hundred practitioners and safety and health specialists representing industry, labor, and academia, and developed by the National Institute for Occupational Safety and Health’s (NIOSH) National Occupational Research Agenda (NORA) MSD team. In the second agenda, which was developed by the National Research Council (NRC) and the Institute of Medicine’s (IOM) National Panel on MSDs and the Workplace, data had been collected from leading researchers in the fields of medicine, information science, and ergonomics. The results clearly showed that work-related injuries and illnesses represent a considerable health problem for the U.S. industrial labor force [
7]. In another study by Pascual and Naqvi [
9], the ergonomics risk assessment methods used by Joint Health and Safety Committees (JHSCs) have been investigated. It is concluded that most JHSC curricula have minimal ergonomics scope; thus, JHSCs rely mostly on injury reports and worker complaints to assess ergonomics risk, and indeed most ergonomics analysis tools available require some ergonomics knowledge.
There are research studies on proactive monitoring of industrial workers’ awkward postures and non-ergonomic motions that date back to the 1990s. Schoenmarklin et al. [
10] monitored the acceleration in the flexion/extension as the best kinematic parameter for evaluation of the low and high incident rates of hand/wrist Cumulative Trauma Disorders (CTDs). However, recent studies focused more on using automated methodologies. Such approaches often take advantage of the advancements in wearable sensors or vision-based monitoring methods to detect such hazardous situations [
11,
12]. Recently, wearable sensing technologies have provided opportunities to gather near real-time data to analyze workers’ safety and health situations [
13]. These approaches are often characterized by features such as being low-cost, easy to use, highly accuracy, and non-intrusive [
14]. In a study aiming at reviewing MSDs in the construction industry, Valero et al. [
15] indicated that the subjectivity and lack of accuracy of visual assessment demand replacing such observations with more accurate and precise posture measurement devices and methods. In some recent studies, in order to identify jobsite workers activity, accelerometers embedded in smart mobile phones have been used [
12,
13,
14,
16,
17,
18,
19]. An artificial neural network (ANN)-based models have been developed for identifying falls and manual material handling activities with a high accuracy using the smartphone installed on workers’ bodies by Akhavian and Behzadan [
16]. Yang et al. [
19] presented a method based on smartphone sensor data acquisition and the concept of labor intensity to evaluate construction workers’ workloads. A sensor application based on the smartphone platform was created to effectively measure labor intense activities so that the application could track construction workers’ movement data unobtrusively.
Recently, IMU sensor-based activity identification models have been explored for a diverse set of applications in the construction industry such as work sampling and fall detection to address practical implementation issues with smartphones [
20,
21,
22,
23]. Furthermore, the IMU sensor-based models have been vastly utilized towards enhancing ergonomic and safety aspects of construction activities [
12,
16,
24,
25]. Yan et al. [
25] designed a motion warning system a real-time motion to detect predefined thresholds of hazardous ergonomic postures and alert the worker’s smartphones. Even though this IMU system recognized movement directions, angles, and rotation, it failed to recognize the real muscle stress and power. In order to validate the system, robust clinical motion data output and sufficient alarm ringing were utilized in both laboratory and field experiments on a construction site located in Hong Kong. The results show that the proposed system assists construction workers to prevent WMSDs without disturbance and interruption during the operations [
25]. Jahanbanifar and Akhavian [
24] modeled body movements and physical activities, including pushing and pulling, in laboratory-scale experiments. The performed force was measured by a work simulator tool, and a smartphone sensor installed on the active arm collected accelerometer data. An ANN was trained with the accelerometer data and the force levels. The testing data results reveal that the trained model can predict the force level with over 87.5% accuracy [
24]. Generally, such methods use IMUs as a wearable tool that detects acceleration produced due to performing a specific activity. The produced data are used to train machine learning algorithms that enable detecting the identified hazardous activities in examples not used during the training of the model. Such models are then evaluated for their generalization, and their performance is enhanced through the use of more training examples and sophisticated prediction algorithms.
Despite the fact that sensors provide detailed information, not all sensors can be utilized in the construction industry due to the dynamic nature of construction activities [
26]. Based on previous research, the ideal sensors for construction applications should have some unique characteristics such as being simple and easy to wear, unobtrusive, affordable, and wireless. Moreover, the sensor should provide valid data and import minimal or no preprocessing for noise cancellation. Thus, identifying a reliable sensor for classifying and monitoring construction activities is crucial, and it can help develop health monitoring systems to prevent WMSDs. In recent studies, attaching sensors using armbands and wristbands have been identified as an affordable, non-invasive, lightweight, and wireless wearable data collection method that is suitable to gather workers’ forearm sEMG and inertial measurement unit (IMU) data [
26].
2.2. Research Motivation
Many researchers have employed these data collection methods for different applications in various fields; nevertheless, the use of sEMG sensors has not been fully investigated for the ergonomic risk evaluation in the context of construction workers’ safety and health. Neck disorders have been the subject of a study where sternocleidomastoid and the upper trapezius muscle activities were monitored using sEMG sensors [
27]. The experiments, however, were intrusive, and the data collection probes were not wearable. In another study, sEMG systems were operated to examine the lower back movements in prefabricated panels erection [
8]. The predictive ability of the developed model was somewhat limited due to limitations, such as using only one motion segment. Matsumura et al. experimented with a wrist-band type sEMG sensor to analyze and recognize sEMG data using a Neural Networks (NN) model. This research was limited only to identify a few wrist movements and did not address the construction industries’ safety requirement [
28]. In a recent study, researchers have assessed construction workers’ fatigue level using sEMG systems [
29]. Results has shown a considerable difference in sEMG parameters while subjects represented different fatigue levels. Results also proved the workability of the wearable sEMG to evaluate workers’ muscle fatigue as a means for assessing their physical stress on construction sites [
29]. Bangaru et al. [
14] evaluated the data quality and reliability of forearm sEMG and IMU data from a wearable sensor for jobsite activity classification. In order to achieve this goal, seven experiments have been conducted. The experiments’ results show that the arm-band sensor data quality is close to the conventional sEMG and IMU sensors, with perfect relative and absolute reliability between trials for all the considered activities.
Despite the potential of wearable sEMG systems to identify different activities risk, the feasibility of a wearable sEMG system to identify heavy weightlifting activities has received very little attention. To address this issue, and in order to identify high-risk lifting activities in jobsites, the present study aimed to propose a machine learning modeling approach where an sEMG-based system is used in a total of 54 experiments to collect data from closely monitored lifting activities. Traditional Machine learning algorithms were implemented to classify the sEMG signal data collected from the lifting activities. The outcomes of this study are expected to identify light classification models that do not require extensive computational burdens and have the potential to be deployed on the edge and in the field (e.g., job sites).