Differences in spinal moments, kinematics and pace during single-task and combined manual material handling jobs

Harari, Yaar; Riemer, Raziel; Bechar, Avital

doi:10.1016/j.apergo.2019.06.002

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…Energy demands for this activity depend on several factors such as the gender, age, posture, burden, walking speed, and individual characteristics [50,51]. Manually load carrying is an essential duty that should be carried out in a lot of industries with plenty of studies related to this task such as [52][53][54]. In agriculture, only three studies were found during the last decade.…”

Section: Load Carryingmentioning

confidence: 99%

A Review on Ergonomics in Agriculture. Part I: Manual Operations

2020

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Background: Agriculture involves several harmful diseases. Among the non-fatal ones, musculoskeletal disorders (MSDs) are the most prevalent, as they have reached epidemic proportions. The main aim of this investigation is to systematically review the major risk factors regarding MSDs as well as evaluate the existing ergonomic interventions. Methods: The search engines of Google Scholar, PubMed, Scopus, and ScienceDirect were used to identify relevant articles during the last decade. The imposed exclusive criteria assured the accuracy and current progress in this field. Results: It was concluded that MSDs affect both developed and developing countries, thus justifying the existing global concern. Overall, the most commonly studied task was harvesting, followed by load carrying, pruning, planting, and other ordinary manual operations. Repetitive movements in awkward postures, such as stooping and kneeling; individual characteristics; as well as improper tool design were observed to contribute to the pathogenesis of MSDs. Furthermore, low back disorders were reported as the main disorder. Conclusions: The present ergonomic interventions seem to attenuate the MSDs to a great extent. However, international reprioritization of the safety and health measures is required in agriculture along with increase of the awareness of the risk factors related to MSDs.

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Section: Load Carryingmentioning

confidence: 99%

A Review on Ergonomics in Agriculture. Part I: Manual Operations

2020

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“…The carrying task occupied the intermediate period (between removing and depositing). We developed a program for analyzing the motion capture signal, which allowed us to identify the start and end points of each task based on a set of quantitative conditions; see Harari et al (2019) for a detailed description. During the experiment, the participants were required to handle boxes of different masses and to work with different shelf heights (see Table 1).…”

Section: Participants and Experimental Designmentioning

confidence: 99%

“…As a result, DHM software is mostly used for the analysis of static postures and not for simulating the entire work process. Recent studies (Harari et al, 2020(Harari et al, , 2019 have shown significant differences between the kinematics for single-task and multiple-task jobs. However, the effect of using predicted motion (based on the single-task approximation) on the calculation of spinal compression forces during sequential MMH tasks has not been investigated.…”

Section: Introductionmentioning

confidence: 99%

Spinal Compression Force During Manual Material Handling - Calculated in Jack^TM Software Using Real Vs Predicted Human Motion

Potash

Harari

Riemer

2020

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Awkward postures have been identified as risk factors for the musculoskeletal system, especially in the construction field [12][13][14][15]. Though different guidelines have been designed for risk assessments, they have not always been definitive due to factors such as high variability, repetitiveness, and a low standardization of tasks [16][17][18][19][20][21][22]. Ergonomics investigations, based on measurements of kinematic and dynamic parameters that are obtained through motion analysis, might be helpful in designing risk management in a real manufacturing scenario.…”

Section: Introductionmentioning

confidence: 99%

“…Hwang et al investigated the lifting squat and stoop techniques by estimating kinematic and kinetic parameters through motion capturing solutions such as an optoelectronic system (OS) and force plates [33]. These two devices are known for their accuracy and reliability on measurements of kinematic variables, but they are difficult to introduce in a workplace setting [11,22,[33][34][35]. Continuous monitoring of a working site could be obtained through the adoption of wearable motion capture systems that are suitable for outdoor motion analysis [2,4,9,10,17,[36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Conforti

Mileti

Prete

et al. 2020

Sensors

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Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.

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Differences in spinal moments, kinematics and pace during single-task and combined manual material handling jobs

Cited by 15 publications

References 38 publications

A Review on Ergonomics in Agriculture. Part I: Manual Operations

A Review on Ergonomics in Agriculture. Part I: Manual Operations

Spinal Compression Force During Manual Material Handling - Calculated in Jack^TM Software Using Real Vs Predicted Human Motion

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

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Differences in spinal moments, kinematics and pace during single-task and combined manual material handling jobs

Cited by 15 publications

References 38 publications

A Review on Ergonomics in Agriculture. Part I: Manual Operations

A Review on Ergonomics in Agriculture. Part I: Manual Operations

Spinal Compression Force During Manual Material Handling - Calculated in JackTM Software Using Real Vs Predicted Human Motion

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

Contact Info

Product

Resources

About

Spinal Compression Force During Manual Material Handling - Calculated in Jack^TM Software Using Real Vs Predicted Human Motion