Jerk as an indicator of physical exertion and fatigue

Zhang, Lichen; Diraneyya, Mohsen Mutasem; Ryu, JuHyeong; Haas, Carl T.; Abdel‐Rahman, Eihab

doi:10.1016/j.autcon.2019.04.016

Cited by 63 publications

(36 citation statements)

References 43 publications

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“…The Perception Neuron V2 inertial motion capture system (NOITOM Ltd, China) was setup in the 18-neuron mode as validated in [22,23], and previously used within [24,25]. Only the upper body portion of the motion capture system was used within this study and consisted of 11 ‘physical’ (Table 1) and 4 ‘virtual’ neurons.…”

Section: Methodsmentioning

confidence: 99%

The impact of patient body mass index on surgeon posture during simulated laparoscopy

Sers

Forrester

Zecca

et al. 2020

Preprint

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Laparoscopy is a cornerstone of modern surgical care. Despite clear advantages for the patients, it has been associated with inducing upper body musculoskeletal disorders amongst surgeons due to the propensity of non-neutral postures. Furthermore, there is a perception that patients with obesity exacerbate these factors. Therefore, novice, intermediate and expert surgeon upper body posture was objectively quantified using inertial measurement units and the LUBA ergonomic framework was used to assess the subsequent postural data during laparoscopic training on patient models that simulated BMI’s of 20, 30, 40 and 50 kg/m2. In all experience groups, the posture of the upper body significantly worsened during simulated surgery on the BMI 50 kg/m2 model as compared to on the baseline BMI model of 20 kg/m2. These findings suggest that performing laparoscopic surgery on patients with severe obesity increases the prevalence of non-neutral upper body posture and may further increase the risk of musculoskeletal disorders in surgeons.

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Section: Methodsmentioning

confidence: 99%

The impact of patient body mass index on surgeon posture during simulated laparoscopy

Sers

Forrester

Zecca

et al. 2020

Preprint

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“…Joseph C. Grieco et al [14] also observed the different paths of the CoP in children with the angelic syndrome and normal children by using pressure plates, and they found that the concerned children's jerk of center of pressure (CoPj) is higher than normal children. Lichen Zhang et al [33] also demonstrated through the bricklaying experiment that skilled workers have the lowest jerk value, stable movement, and high motion control capacity. By contrast, third-year apprentices had the higher jerk values, indicating poor motion control.…”

Section: B Foot Analysismentioning

confidence: 99%

The Influence of Treadmill on Postural Control

et al. 2020

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When people walk on a treadmill, significant differences exist in postural control if compared to walking on the ground. In this paper, a method combining linear and nonlinear analysis is proposed to evaluate the walking postural control ability. In this method, the movements of the upper body and the feet are analyzed separately, because these parts have more capacity to reflect the characteristics of people's gait. On this basis, the linear and nonlinear indicators of each part are analyzed, and the postural control ability is evaluated. The method is validated by three healthy subjects walking on both the treadmill and the ground. In treadmill walking case, the results show a smaller upper trunk acceleration (UTa) in the anterior-posterior (AP) direction. In the foot movement analysis, there are significant differences in the performance indexes related to the center of pressure (CoP) when people are walking on the treadmill and the ground. When walking on the treadmill, the velocity, acceleration, and jerk of the CoP in the AP direction are smaller than those in the ground walking case. Besides, when people walk on treadmills at uniform speeds, their feet accelerate is less in the vertical direction, and they have shorter stride length (SL) and longer stride time (ST). These results suggest that people have better postural control when walking on a treadmill. This method can be used as a means of assessing people's behavior when walking on various situations by evaluating their control posture ability.

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“…To investigate the feasibility of using continuous motion data as an input to train SVM, we first analyzed data from our previous study [26]. Each participant was instructed to complete a pre-built lead wall shown in Figure 2(a), using forty-five concrete masonry units (CMUs) from the second to the sixth course.…”

Section: Wall Building Experimentsmentioning

confidence: 99%

“…In our previous work [26], we found that jerk may be used as an indicator of loss of motor control caused by physical exertion. However, the tasks were manually separated to ensure that jerk values were compared between the same action types, for examples, the motion data collected during each lifting action (pick uptransport -lay down) were segmented out from other motions such as spreading mortar.…”

Section: Introductionmentioning

confidence: 99%

Automated Monitoring of Physical Fatigue Using Jerk

Zhang¹,

Diraneyya²,

Ryu³

et al. 2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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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.

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Jerk as an indicator of physical exertion and fatigue

Cited by 63 publications

References 43 publications

The impact of patient body mass index on surgeon posture during simulated laparoscopy

The impact of patient body mass index on surgeon posture during simulated laparoscopy

The Influence of Treadmill on Postural Control

Automated Monitoring of Physical Fatigue Using Jerk

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