Improving the railway’s understanding of accident causation through an integrated approach to human factors analysis and technical safety data recording

This paper describes a safety data recording and analysis system that has been developed to capture safety occurrences including precursors using high-definition forward-facing video from train cabs and data from other train-borne systems. The paper describes the data processing model and how events detected through data analysis are related to an underlying socio-technical model of accident causation. The integrated approach to safety data recording and analysis insures systemic factors that condition, influence or potentially contribute to an occurrence are captured both for safety occurrences and precursor events, providing a rich tapestry of antecedent causal factors that can significantly improve learning around accident causation. This can ultimately provide benefit to railways through the development of targeted and more effective countermeasures, better risk models and more effective use and prioritization of safety funds. Level crossing occurrences are a key focus in this paper with data analysis scenarios describing causal factors around near-miss occurrences. The paper concludes with a discussion on how the system can also be applied to other types of railway safety occurrences.

Human action recognition from video sequences

This PhD research has proposed new machine learning techniques to improve human action recognition based on local features. Several novel video representation and classification techniques have been proposed to increase the performance with lower computational complexity. The major contributions are the construction of new feature representation techniques, based on advanced machine learning techniques such as multiple instance dictionary learning, Latent Dirichlet Allocation (LDA) and Sparse coding. A Binary-tree based classification technique was also proposed to deal with large amounts of action categories. These techniques are not only improving the classification accuracy with constrained computational resources but are also robust to challenging environmental conditions. These developed techniques can be easily extended to a wide range of video applications to provide near real-time performance.

Motion Analysis for Short and Long Jump

Motion analysis is very essential in sport activities to enhance the performance of an athlete and to ensure the correctness of regimes. Expensive methods of motion analysis involving the use of sophisticated technology has led to limited application of motion analysis in sports. Towards this, in this paper we have integrated a low-cost method for motion analysis using three axis accelerometer, three axis magnetometer and microcontroller which are very accurate and easy to use. Seventeen male subjects performed two experiments, standing short jumps and long jumps over a wide range of take-off angles. During take-off and landing the acceleration and angles at different joints of the body are recorded using accelerometers and magnetometers, and the data is captured using Lab VIEW software. Optimum take-off angle in these jumps are calculated using the recorded data, to identify the optimum projection angle that maximizes the distance achieved in a jump. The results obtained for optimum take off angle in short jump and long jump is in agreement with those obtained using other methodologies and theoretical calculations assuming jump to be a projectile motion. The impact force (acceleration) is also analysed and is found to progressively decrease from foot to neck.

Does training policy help to attract, retain and develop valuable human resources? Analysis from the Mondragon case

This is an electronic version of the accepted paper in the journal:Advances in the Economic Analysis of Participatory and Labor-Managed Firms. Volumen. 12

Healthcare human reliability analysis - By heart

As part of the investigations into a surgical incident involving the accidental retention inside a patient's venous system of a guide wire for central venous catheterisation (CVC), the Human Error Assessment and Reduction Technique (HEART) was used to examine the potential for further occurrences. It was found to be time-efficient and to yield plausible probabilities of human error, although its use in healthcare has challenges, suggesting adaptation would be beneficial.

Healthcare human reliability analysis – by HEART

This book presents the proceedings of the international conference on Contemporary Ergonomics and Human Factors 2013.