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Author Sanchez, R.; Villena, M. doi  openurl
  Title Comparative evaluation of wearable devices for measuring elevation gain in mountain physical activities Type Journal Article
  Year 2020 Publication Proceedings Of The Institution Of Mechanical Engineers Part P-Journal Of Sports Engineering And Technology Abbreviated Journal Proc. Inst. Mech. Eng. Part P-J. Sport. Eng. Technol.  
  Volume to appear Issue Pages 8 pp  
  Keywords Mountain running; physical workload; altitude; measuring; validity; GPS; barometric device  
  Abstract The aim of this article is to examine the validity of elevation gain measures in mountain activities, such as hiking and mountain running, using different wearable devices and post-processing procedures. In particular, a total of 202 efforts were recorded and evaluated using three standard devices: GPS watch, GPS watch with barometric altimeter, and smartphone. A benchmark was based on orthorectified aerial photogrammetric survey conducted by the Chilean Air Force. All devices presented considerable elevation gain measuring errors, where the barometric device consistently overestimated elevation gain, while the GPS devices consistently underestimated elevation gain. The incorporation of secondary information in the post-processing can substantially improve the elevation gain measuring accuracy independently of the device and altitude measuring technology, reducing the error from -5% to -1%. These results could help coaches and athletes correct elevation gain estimations using the proposed technique, which would serve as better estimates of physical workload in mountain physical activities.  
  Address [Sanchez, Raimundo; Villena, Marcelo] Adolfo Ibanez Univ, Fac Engn & Sci, Diagonal Las Torres 2640, Santiago 7910000, Chile, Email: raimundo.sanchez@uai.cl  
  Corporate Author Thesis  
  Publisher Sage Publications Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1754-3371 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000534320000001 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1201  
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Author Barrera, J.; Carrasco, R.A.; Moreno, E. doi  openurl
  Title Real-time fleet management decision support system with security constraints Type Journal Article
  Year 2020 Publication TOP Abbreviated Journal TOP  
  Volume to appear Issue Pages 21 pp  
  Keywords Fleet management; Real-time control; Data analytics; GPS tracking; Decision support system; Conflict detection and resolution  
  Abstract Intelligent transportation, and in particular, fleet management, has been a forefront concern for a plethora of industries. This statement is especially true for the production of commodities, where transportation represents a central element for operational continuity. Additionally, in many industries, and in particular those with hazardous environments, fleet control must satisfy a wide range of security restrictions to ensure that risks are kept at bay and accidents are minimum. Furthermore, in these environments, any decision support tool must cope with noisy and incomplete data and give recommendations every few minutes. In this work, a fast and efficient decision support tool is presented to help fleet managers oversee and control ore trucks, in a mining setting. The main objective of this system is to help managers avoid interactions between ore trucks and personnel buses, one of the most critical security constraints in our case study, keeping a minimum security distance between the two at all times. Furthermore, additional algorithms are developed and implemented, so that this approach can work with real-life noisy GPS data. Through the use of historical data, the performance of this decision support system is studied, validating that it works under the real-life conditions presented by the company. The experimental results show that the proposed approach improved truck and road utilization significantly while allowing the fleet manager to control the security distance required by their procedures.  
  Address [Barrera, Javiera; Carrasco, Rodrigo A.; Moreno, Eduardo] Univ Adolfo Ibanez, Fac Engn & Sci, Santiago, Chile, Email: javiera.barrera@uai.cl;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1134-5764 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000534967700001 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1200  
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Author Hughes, S.; Moreno, S.; Yushimito, W.F.; Huerta-Canepa, G. doi  openurl
  Title Evaluation of machine learning methodologies to predict stop delivery times from GPS data Type Journal Article
  Year 2019 Publication Transportation Research Part C-Emerging Technologies Abbreviated Journal Transp. Res. Pt. C-Emerg. Technol.  
  Volume 109 Issue Pages 289-304  
  Keywords Machine learning; Stop delivery time; Classification; Regression; Hazard duration; GPS  
  Abstract In last mile distribution, logistics companies typically arrange and plan their routes based on broad estimates of stop delivery times (i.e., the time spent at each stop to deliver goods to final receivers). If these estimates are not accurate, the level of service is degraded, as the promised time window may not be satisfied. The purpose of this work is to assess the feasibility of machine learning techniques to predict stop delivery times. This is done by testing a wide range of machine learning techniques (including different types of ensembles) to (1) predict the stop delivery time and (2) to determine whether the total stop delivery time will exceed a predefined time threshold (classification approach). For the assessment, all models are trained using information generated from GPS data collected in Medellin, Colombia and compared to hazard duration models. The results are threefold. First, the assessment shows that regression-based machine learning approaches are not better than conventional hazard duration models concerning absolute errors of the prediction of the stop delivery times. Second, when the problem is addressed by a classification scheme in which the prediction is aimed to guide whether a stop time will exceed a predefined time, a basic K-nearest-neighbor model outperforms hazard duration models and other machine learning techniques both in accuracy and F-1 score (harmonic mean between precision and recall). Third, the prediction of the exact duration can be improved by combining the classifiers and prediction models or hazard duration models in a two level scheme (first classification then prediction). However, the improvement depends largely on the correct classification (first level).  
  Address [Hughes, Sebastian; Moreno, Sebastian; Yushimito, Wilfredo F.; Huerta-Canepa, Gonzalo] Univ Adolfo Ibanez, Fac Engn & Sci, Vina Del Mar, Chile, Email: shughes@alumnos.uai.cl;  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0968-090x ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000504780800016 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 1082  
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Author Velazquez, R.; Pissaloux, E.; Rodrigo, P.; Carrasco, M.; Giannoccaro, N.I.; Lay-Ekuakille, A. doi  openurl
  Title An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback Type Journal Article
  Year 2018 Publication Applied Sciences-Basel Abbreviated Journal Appl. Sci.-Basel  
  Volume 8 Issue 4 Pages 15 pp  
  Keywords assistive technology; blind pedestrian; GPS localization; mobile computing; navigational system; tactile display; tactile-foot stimulation; wearable system  
  Abstract This paper presents a novel, wearable navigation system for visually impaired and blind pedestrians that combines a global positioning system (GPS) for user outdoor localization and tactile-foot stimulation for information presentation. Real-time GPS data provided by a smartphone are processed by dedicated navigation software to determine the directions to a destination. Navigational directions are then encoded as vibrations and conveyed to the user via a tactile display that inserts into the shoe. The experimental results showed that users were capable of recognizing with high accuracy the tactile feedback provided to their feet. The preliminary tests conducted in outdoor locations involved two blind users who were guided along 380-420 m predetermined pathways, while sharing the space with other pedestrians and facing typical urban obstacles. The subjects successfully reached the target destinations. The results suggest that the proposed system enhances independent, safe navigation of blind pedestrians and show the potential of tactile-foot stimulation in assistive devices.  
  Address [Velazquez, Ramiro; Rodrigo, Pedro] Univ Panamericana, Fac Engn, Aguascalientes 20290, Mexico, Email: rvelazquez@up.edu.mx;  
  Corporate Author Thesis  
  Publisher Mdpi Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2076-3417 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000434996400105 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 880  
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