Forthcoming and Online First Articles

International Journal of Human Factors Modelling and Simulation

International Journal of Human Factors Modelling and Simulation (IJHFMS)

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International Journal of Human Factors Modelling and Simulation (2 papers in press)

Regular Issues

  • The assessment of injury risk in the healthcare sector via integrating motion tracking techniques with digital human modeling ergonomic tools   Order a copy of this article
    by Xiaoxu Ji, Justo Hernandez, Emily Schweitzer, Wilson Wang, Davide Piovesan 
    Abstract: An advanced fusion technology has been investigated to evaluate the injury risk in the healthcare sector by integrating a motion tracking system with digital human modeling (DHM) ergonomic tools. The proposed approach greatly overcomes the time-consuming process to create a full-body dynamic simulation where the DHM postures are set manually. This study provides a new method to evaluate the lumbar forces and physical demands for individuals during healthcare operations. In this study, we analyzed the effect of key anthropometric and biomechanics variables on the low back loadings, such as body weight, body height, trunk and hip angular displacements, as well as genders. The relationship of such variables to the lumbar spine forces may provide insights on the occurrence of musculoskeletal disorders during patient handling tasks. The outcomes of this study can directly result in the design of better assistive device and workstation as well as the development of enhanced injury prevention programs.
    Keywords: injury assessment; injury prevention; healthcare; patient handling task; ergonomics; physical demands; Xsens motion tracking; JACK Siemens; digital human modeling; lumbar spine; compressive force; shear force.

  • Deep Spiking Neural Networks for Image Classification   Order a copy of this article
    by Zhuo LI, Lin Meng 
    Abstract: Artificial intelligence (AI) has made great progress with the foundations of computer science. Furthermore, brain-like computing, which simulates the human brain through computers, has been a hot research area. As an important part of brain-like computing, spiking neural networks (SNNs) have received great attention due to their high biological plausibility and low energy consumption. The technique is very suitable for the research and implementation of brain-like computing and provides promising solutions for image classification tasks on resource-constrained mobile devices. However, it typically suffers from severe performance degradation and network architecture complexity. In this paper, we first design a deep spiking neural network with a simple architecture. Then, we propose an improved back-propagation algorithm for the approximate derivation of the spiking neural network. The experimental results show that the proposal achieves recognition accuracy of 98.43%, 96.70%, 98.81% and 93.61% respectively on the image classification datasets MNIST, Fashion-MNIST, Kuzushiji-MNIST and CIFAR-10. The number of parameters and the recognition accuracy show that the proposals have great advantages for image classification. This research has important implications for the implementation of intelligent brain-like computing.
    Keywords: brain-like computing; spiking neural networks; SNNs; image classification; approximate derivation.
    DOI: 10.1504/IJHFMS.2023.10051522