Automated Monitoring of Physical Fatigue Using Jerk

Abstract

Construction workers are commonly subjected to ergonomic risks due to manual material handling that requires high levels of energy input over long work hours. Fatigue in musculature is associated with decline in postural stability, motor performance, and altered normal motion patterns, leading to heightened risks of work-related musculoskeletal disorders. Physical fatigue has been previously demonstrated to be a good indicator of injury risks, thus, monitoring and detecting muscle fatigue during strenuous work may be advantageous in mitigating these risks. Currently, few researchers have investigated how physical fatigue and exertion can be continuously monitored for practical use outside laboratory settings. Exercise-induced fatigue has been shown to impact motor control; thus, it can be measured using jerk, the time derivative of acceleration. This paper investigates the application of a machine learning approach, Support Vector Machine (SVM), to automatically recognize jerk changes due to physical exertion. We hypothesized that physical exertion and fatigue will influence motions and thus, can be classified based on jerk values. The motion data of six expert masons were collected using IMU sensors during two bricklaying tasks. The pelvis, upper arms, and thighs jerk values were used to classify inter-and intra-subject rested and exerted states. Our results show that on average, intra-subject classification achieved an accuracy of 94% for a five-course wall building experiment and 80% for a first-course experiment, leading us to conclude that jerk changes due to physical exertion can be detected using wearable sensors and SVMs.

Calculating and Analyzing Angular Head Jerk in Augmented and Virtual Reality: Effect of AR Cue Design on Angular Jerk

“…With these measures in place, the task can be changed (e.g., removing a heavy lifting task) to adapt to current user abilities. Other work involving jerk has been used to determine levels of fatigue in construction workers [15], skill level of surgeons [10], and to identify the aggressiveness of drivers in a naturalistic setting [16]. It should be noted though that these studies typically use purpose-built IMUs to analyze the movement patterns and thus would require workers to wear special equipment to obtain these jerk analytics.…”

Jerk within the Context of Science and Engineering—A Systematic Review

“…Jerk in human tolerance [202,203]; 8. Jerk in kinesiology [139][140][141][142][143][144][145][146][147]; 9. Jerk in measurement [148][149][150][151]; 10.…”

“…Jerk has been used in different kinesiologies in different fields of clinical studies in sport science. Below, those works which studied jerk in kinesiology for different applications [139][140][141][142][143][144][145][146][147] have been captured (Table 12).…”

“…Recently, jerk has gained interest in non-clinical trials. For instance, in a recent study by Zhang [145][146][147], jerk was used as a measure to detect fatigue in workers.…”

Inertial Motion Capture-Based Whole-Body Inverse Dynamics

“…The fidelity of motion kinematics obtained from IMC systems has been previously verified [7][8][9][10][11][12][13]. Currently available IMC systems have been used in different domains for various applications, including activity recognition [14][15][16] and activity assessments [17][18][19]. Marta et al [17] developed a wearable biofeedback suit equipped with 10 IMUs.…”