This article employed the Hertz contact stress theory and the finite element method to evaluate the maximum contact pressure and the limit stresses of orange fruit under transportation and storage. The elastic properties of orange fruits subjected to axial and axial contact were measured such that elastic limit force, elastic modulus, Poisson's ratio and bioyield stress were obtained as 18 N, 0.691 MPa, 0.367, 0.009 MPa for axial compression and for radial loading were 15.69 N, 0.645 MPa, 0.123, 0.010 MPa. The Hertz maximum contact pressure was estimated for axial and radial contacts as 0.036 MPa. The estimated limiting yield stress estimated as von Mises stresses for the induced surface stresses of the orange topologies varied from 0.005 MPa-0.03 MPa. Based on the distortion energy theory (DET) the yield strength of orange fruit is recommended as 0.03 MPa while based on the maximum shear stress theory (MSST) is 0.01 MPa for the design of orange transportation and storage system. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Ergonomic risk factors which include force, repetition and awkward postures, can result in Work-Related Musculoskeletal Disorders (WMSDs) among workers. Hence, systems that provide real-time feedback to the worker concerning his current ergonomic behaviours are desirable. This paper presents the design and implementation of a human-machine interface posture assessment feedback system whose conceptual model is developed through a model-driven development perspective using the UML and Interface flow diagrams. The resulting system provides a shop floor with a simple, costeffective and automatic tool for real-time display of worker's postures. Testing the system on volunteer participants reveals that it is easy to use, achieves real-time posture assessment and provides easy-to-understand feedback to workers. This system may be useful for reducing the rate of occurrence of awkward postures, one of the contributing factors to risk of WMSDs among workers.

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.