Wearable computing for railway environments: proposal and evaluation of a safety solution

Ferreira, Bruno V.; Serejo, Gerson; Ferreira, Mylena R.; Ferreira, Danilo F.; Cardoso, Leon; Yoshidome, Ewelton; Arruda, Helder; Lira, Wallace; Ferreira, Jair; Carvalho, Eduardo Atem de; Pessin, Gustavo; Souza, Cleidson R. B. de

doi:10.1049/iet-its.2016.0187

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Wearables are capable of delivering exceptional opportunities for dealing with societal challenges. This can be achieved by providing solutions in areas of patient monitoring [2], healthy ageing [3], safety at work [4], emergency management [5], and others. Therefore, major sectors in this market are expected to remain as consumer electronics, defence, and healthcare.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

et al. 2019

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Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.

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Section: Introductionmentioning

confidence: 99%

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

et al. 2019

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“…It resolved the unstructured verbatim and imbalance of maintenance data. Ferreira et al [29] proposed a solution based on wearable components for safety of maintenance personnel on the railway. Durazo-Cardenas et al [30] designed a high-level architecture automatic system for British railway extended infrastructure to realize optimum and intelligent maintenance scheduling.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Failure Features of High-Speed Automatic Train Protection System

Kang

Wang

Chen³

et al. 2021

IEEE Access

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An automatic train protection (ATP) system is the core to ensure operation safety of high-speed railway. However, at present, failure rate change rules of the system are not well understood and the maintenance strategy is not refined. In order to improve the protection capability and maintenance level of high-speed trains, this paper proposes a decision tree machine learning model for failure feature extraction of ATP systems. First, system type, mean operation mileage, mean service time, etc. are selected as ATP failure feature parameters, and cumulative failure rate as a model output label. Second, support vector machine, AdaBoost, artificial neural networks and decision tree model are adopted to train and test practical failure data. Performance analysis shows that decision tree learning model has better generalization ability. Its accuracy of 0.9761 is significantly greater than the other machine learning models, so it is most suitable for failure features analysis. Third, interpretability analysis reveals the quantitative relationship between system failure and features. Finally, an intelligent maintenance system for ATP systems is built, which realize the refined maintenance throughout life cycle.INDEX TERMS Automatic train protection system, intelligent maintenance, failure feature, machine learning, model interpretability, high-speed train.

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“…Another approach to safety was presented by (Ferreira et al 2017) in which wearable devices were placed on worker safety vests and arms. The devices were used as an alarm (visual and sound) to alert maintenance workers near train tracks the proximity of a train.…”

Section: Wearables For Location Of Potential Hazards (Moving Machinery)mentioning

confidence: 99%

Wearable technology for occupational risk assessment: Potential avenues for applications

Pavón¹,

Sigcha²,

Arezes³

et al. 2018

Occupational Safety and Hygiene VI

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This paper analyzes the opportunities offered by wearable technology for occupational risk assessment. A wide range of electronic devices known as "wearables" include sensors that provide interesting features with the potential to be used in workers' monitoring, sending alert signs (e.g., visual, sound, haptic) and training. In this paper, it has been identified the primary applications in which wearables are being used for risk assessment (motion and activity detection, recognition of work-related musculoskeletal disorders, fall detection, evaluation of exposure to different physical agents, evaluation of exposure to chemical agents, and location of potential hazards). Also, it has been identified the opportunities for improvement in the future using the potential of emerging technologies.

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Wearable computing for railway environments: proposal and evaluation of a safety solution

Cited by 4 publications

References 10 publications

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Analysis of Failure Features of High-Speed Automatic Train Protection System

Wearable technology for occupational risk assessment: Potential avenues for applications

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