Investigation of oil palm harvesters' postures using RULA analysis

Deros, Baba Md; Khamis, Nor Kamaliana; Mohamad, Diana; Kabilmiharbi, Nurainaa; Daruis, Dian Darina Indah

doi:10.1109/iecbes.2014.7047504

Cited by 12 publications

(11 citation statements)

References 8 publications

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“…RULA is an ergonomic assessment tool used for detecting the presence of ergonomic risk factors as well as indicate worker's exposure to such risks (McAtamney and Nigel Corlett, 1993), and for analysing the risks associated with Work-Related Upper Limb Disorders (Deros et al, 2015;Godilano et al, 2015). It finds wide application during the ergonomic assessment of workers' postures on manufacturing shop floors (Shah et al, 2016) and in hospitals while lifting patients (Bartnicka, 2015;Ha et al, 2014).…”

Section: Ergonomic Risk Assessment Toolsmentioning

confidence: 99%

“…It finds wide application during the ergonomic assessment of workers' postures on manufacturing shop floors (Shah et al, 2016) and in hospitals while lifting patients (Bartnicka, 2015;Ha et al, 2014). In agriculture, studies concerning oil palm harvests showed that of the six working postures analysed, all required immediate ergonomic intervention (Deros et al, 2015). During bicycle repairing and fastening operations on the shop floor, RULA has been used to assess the risk of developing WMSDs (Daphalapurkar, 2012;Mukhopadhyay et al, 2015), etc.…”

Section: Ergonomic Risk Assessment Toolsmentioning

confidence: 99%

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Ergonomic evaluation on the manufacturing shop floor: A review of hardware and software technologies

Mgbemena

Tiwari

et al. 2020

CIRP Journal of Manufacturing Science and Technology

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The high rate of work-related musculoskeletal disorders (WMSDs) among workers on the shop floor has led to the development of various ergonomic evaluation and risk assessment tools by researchers. This paper presents a summary of existing literature reviews of hardware and software technologies developed for effective ergonomic evaluation and correct risk assessment on manufacturing shop floors. Criteria was set for the review and after comprehensive search on 14 databases, 24 studies met the criteria. Old and modern ergonomic evaluation hardware and software technologies for effective evaluation on shop floors are identified. Most literatures cited the digital human models (DHMs), which can be created on many ergonomic evaluation software tools, to be an effective ergonomic evaluation tool. Gaps are identified with the use of DHMs for ergonomic evaluations. Ultimately, this study is a contribution towards identifying adequate ergonomic evaluation and correct risk assessment tools which when implemented on the Shop floor, can lead to improved productivity, enhanced efficiency and reduced cost.

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Section: Ergonomic Risk Assessment Toolsmentioning

confidence: 99%

Section: Ergonomic Risk Assessment Toolsmentioning

confidence: 99%

Ergonomic evaluation on the manufacturing shop floor: A review of hardware and software technologies

Mgbemena

Tiwari

et al. 2020

CIRP Journal of Manufacturing Science and Technology

View full text Add to dashboard Cite

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“…To achieve this, video capture systems are often used. In [9], a video camera was used to monitor the working postures of palm oil harvesters. It involved taking snapshots of awkward postures with the significant postures captured for onward ergonomic assessment.…”

Section: Literature Review Of Camera-based Data Collection For Industmentioning

confidence: 99%

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

Mgbemena

Oyekan

Tiwari

et al. 2016

Advances in Intelligent Systems and Computing

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Abstract. Manual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator -precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks.

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“…There are some assessment that has been done to investigate the posture and possible risk of the worker when using the palm oil harvesting tools. Rapid Entire Body Assessment REBA [3], Rapid Upper Limb Analysis RULA [4] and Quick Exposure Check QEC [5] are some of the techniques used to determine the possible musculoskeletal risk.…”

Section: Introductionmentioning

confidence: 99%

Simple Arm Muscle Model for Oil Palm Harvesting Process

et al. 2016

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Abstract. Arm are essential in order to perform manual material handling work that normally involves lifting, handling, placing, push and pull, carrying and moving heavy loads. When these work elements are performed over prolonged periods repeatedly, it will expose workers arm to awkward posture and possible ergonomic risk factor. For example, work element that requires repetitions frequently may lead the arm to face physical stress and mental fatigue. The situation can be extremely risky if the worker task requires higher focus or time consumable. These issues are unavoidable in palm oil harvesting process since the workers are still using manual handling when harvesting the fresh fruit bunch (FFB). The worker using a chisel to harvest the young palms and a sickle mounted on a bamboo or aluminum pole to harvest taller palms. When perform this work element combining with heavy physical workload, it may lead to work-related muscle disorders (WSMDs). This study was conducted to identify the force reaction and inverse dynamic analysis during oil palm harvesting process using ergonomics software called Anybody Technology. Inverse dynamic analysis is a technique for figuring strengths and/or moments of power (torques) taking into account the kinematics (movement) of a body and the body's inertial properties.

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Investigation of oil palm harvesters' postures using RULA analysis

Cited by 12 publications

References 8 publications

Ergonomic evaluation on the manufacturing shop floor: A review of hardware and software technologies

Ergonomic evaluation on the manufacturing shop floor: A review of hardware and software technologies

Gesture Detection Towards Real-Time Ergonomic Analysis for Intelligent Automation Assistance

Simple Arm Muscle Model for Oil Palm Harvesting Process

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