Upper Extremity Muscle Activity During In-Phase and Anti-Phase Continuous Pushing Tasks

Biomechanical Assessments of the Upper Limb for Determining Fatigue, Strain and Effort from the Laboratory to the Industrial Working Place: A Systematic Review

Recent human-centered developments in the industrial field (Industry 5.0) lead companies and stakeholders to ensure the wellbeing of their workers with assessments of upper limb performance in the workplace, with the aim of reducing work-related diseases and improving awareness of the physical status of workers, by assessing motor performance, fatigue, strain and effort. Such approaches are usually developed in laboratories and only at times they are translated to on-field applications; few studies summarized common practices for the assessments. Therefore, our aim is to review the current state-of-the-art approaches used for the assessment of fatigue, strain and effort in working scenarios and to analyze in detail the differences between studies that take place in the laboratory and in the workplace, in order to give insights on future trends and directions. A systematic review of the studies aimed at evaluating the motor performance, fatigue, strain and effort of the upper limb targeting working scenarios is presented. A total of 1375 articles were found in scientific databases and 288 were analyzed. About half of the scientific articles are focused on laboratory pilot studies investigating effort and fatigue in laboratories, while the other half are set in working places. Our results showed that assessing upper limb biomechanics is quite common in the field, but it is mostly performed with instrumental assessments in laboratory studies, while questionnaires and scales are preferred in working places. Future directions may be oriented towards multi-domain approaches able to exploit the potential of combined analyses, exploitation of instrumental approaches in workplace, targeting a wider range of people and implementing more structured trials to translate pilot studies to real practice.

Which subject-related variables contribute to movement variability during a simulated repetitive and standardised occupational task? Recurrence quantification analysis of surface electromyographic signals

Movement variability is a component of human movement. This study applied recurrence quantification analysis (RQA) on electromyographic signals to determine the effects of two types of variables on movement variability during a short, simulated repetitive and standardised occupational clip-fitting task. The electrical activity of six muscles in the dominant upper limb was recorded in 21 participants. Variables related to the task performance (insertion force and movements performed when fitting clips) affected RQA measures: recurrence rate (RR), percentage of determinism (DET) and diagonal line length entropy (ENT). Variables related to participant's characteristics (sex, age, and BMI) affected only DET and ENT. A constrasting variability was observed such as a high-DET value combined with a high-ENT value and inversely. Variables affected mainly the recurrences organisation of the more distal muscles. Even if movement variability is complex, it should be considered by ergonomists and work place designers to better understanding of operators' movements. Practitioner summary: It is essential to consider the complexity of operators' movement variability to understand their activities. Based on intrinsic movement variability knowledge, ergonomists and work place designers will be able to modulate the movement variability by acting on workstation designs and occupational organisation with the aim of preserving operators' health. Abbreviations: RR: recurrence rate; DET: percentage of determinism; ENT: diagonal line length entropy; BMI: body mass index; FDS: flexor digitorum superficialis; EXT: extensor digitorum communis; BIC: biceps brachii; TRI: triceps brachii; DEL: deltoideus anterior; TRA: trapezius pars descendens; F: female; M: male; S: supinated; P: pronated; CM: continuous movement; DM: discontinuous movement.

Feedback Training Improves Compliance with Sternal Precaution Guidelines during Functional Mobility: Implications for Optimizing Recovery in Older Patients after Median Sternotomy

Patients often need to use their arms to assist with functional activities, but after open heart surgery, pushing with the arms is limited to <10 lb (4.5 kg) to help minimize force across the healing sternum. The main purposes of this study were to determine if older patients (>60 years old) (1) accurately estimated upper extremity (UE) weight bearing force of 10 lb or less and (2) if feedback training improved their ability to limit UE force and pectoralis major muscle contraction during functional activities. An instrumented walker was used to measure UE weight bearing force, and electromyography was used to measure pectoralis major muscle activity simultaneously during 4 functional mobility tasks. After baseline testing, healthy older subjects (n = 30) completed a brief session of visual and auditory concurrent feedback training. Results showed that the self-selected UE force was >10 lb for all tasks (20.0-39.7 lb [9.1-18.0 kg]), but after feedback training, it was significantly reduced (10.6-21.3 lb [4.8-9.7 kg]). During most trials (92%), study participants used >12 lb (5.5 kg) of arm weight bearing force. Pectoralis major muscle peak electromyography activity was <23% of maximal voluntary isometric contraction and was reduced (9.8-14.9%) after feedback training. Older patients may not be able to accurately estimate UE arm force used during weight bearing activities, and visual and auditory feedback improves accuracy and also modulation of pectoralis major muscle activation. Results suggest that an instrumented walker and feedback training could be clinically useful for older patients recovering from open heart surgery.

EMG Characterization and Processing in Production Engineering

Electromyography (EMG) signals are biomedical signals that measure electrical currents generated during muscle contraction. These signals are strongly influenced by physiological and anatomical characteristics of the muscles and represent the neuromuscular activities of the human body. The evolution of EMG analysis and acquisition techniques makes this technology more reliable for production engineering applications, overcoming some of its inherent issues. Taking as an example, the fatigue monitoring of workers as well as enriched human–machine interaction (HMI) systems used in collaborative tasks are now possible with this technology. The main objective of this research is to evaluate the current implementation of EMG technology within production engineering, its weaknesses, opportunities, and synergies with other technologies, with the aim of developing more natural and efficient HMI systems that could improve the safety and productivity within production environments.

Influence of Human Parameters on Labor Productivity in the Construction Industry

OBJECTIVE

The objective of this article is to investigate the influence of human parameters on qualitative assessment of labor productivity (LP) in the construction industry.

BACKGROUND

The theories involving workers have identified various parameters on physical efficiency, such as heart rate, relative heart rate, and calorie count, so as to improve the safety and social conditions of construction labor, thereby increasing LP. However, there is no direct emphasis on assessment of LP using human parameters related to physical strength.

METHOD

An exploratory study was conducted on 17 construction workers by observing their task-level LP in real time at a construction site. Human parameters, such as age, body mass index (BMI), handgrip strength, and upper body muscle strength of the laborers, were measured at the construction site. Performance levels of these workers were placed in three categories, and each worker was assigned a typical weightage to each category when correlated to physical strength.

RESULTS

Labor categories among the human parameters that included middle age, normal-range BMI, and strong muscular strength were shown as having higher LP than others. A quantitative parameter called the Human Parameter Index (HPI) was developed based on the performance categories developed for an individual worker. Human parameters revealed a significant relationship with respect to task-level LP.

CONCLUSION AND APPLICATION

The study determines the influence of human parameters on LP in construction. Introduction of human parameters in the construction industry will help in assessment of LP for various labor-intensive activities.