Dalış Bilgisayarlarının Ergonomik Performanslarının Değerlendirilmesi

Sportif dalıcılar basınç azalmasından ötürü ortaya çıkan vurgun hastalığını önlemek amacı ile derinlik ve süreye bağlı uygun çıkış profilini gösteren dalış bilgisayarı kullanırlar. Bu cihazlar aynı zamanda dalıcının tüpündeki gaz miktarını, bu gazın belirli derinlikte ne kadar yetebileceğini, pusula bilgini gösterebildiği gibi kronometre, algoritma seçimi, dalış kaydı gibi 4., 5 aksesuar özellikler de taşıyabilir. Güvenli ve konforlu dalış için kullanıcı dostu ve etkin bir dalış bilgisayarı seçimi hayati önem taşır. Bu çalışmada, pazardaki sportif dalış bilgisayarlarının ergonomik performanslarının değerlendirilmesi için uzmanlar tarafından belirlenmiş 7 performans ölçütü kullanılarak çok ölçütlü bir karar verme yöntemi uygulanmıştır. Performans ölçütlerinin ikili karşılaştırmaları, çeşitli ülkelerden (Türkiye, İtalya, Belçika, Avustralya, Hollanda, Norveç, İngiltere, Finlandiya ve Almanya) 4 kadın ve 16 erkek dalıcı ile yapılan anketlerle elde edilmiştir. En yüksek pazar segmentinde ve fiyatı birbirine yakın sekiz dalış bilgisayarı analize dahil edilmiş, performans ölçütlerinin ağırlıkları Analitik Hiyerarşi Süreci ile hesaplanmıştır. Bu çalışmada, dalış bilgisayarlarının performanslarının ergonomik açıdan değerlendirilmesi için mevcut popüler dalış dergilerindeki öznel değerlendirmelerin alternatifi nesnel bir yöntem ilk kez kullanılmıştır. Çalışma en üst segment dalış bilgisayarları kapsamış olmakla birlikte, ilerde tüm segmentleri kapsayacak şekilde genişletilmesi ve değerlendirme sonuçları ile vurgun kaza istatistiklerinin karşılaştırılması hedeflenmektedir.

Evaluating the Ergonomic Performance of Dive Computers

Recreational divers use dive computers (DCs) which give information about safe ascent profile in order to prevent decompression illness that may result from decrease in ambient pressure. Dive computer are electronic devices used not only for appropriate ascent from depth but for their extra features such as tank pressure, gas time remaining, compass, chronometer, algorithm selection, recording dives to increase dive safet and comfort. Divers have to select a user-friendly and effective DC for safe and comfortable dive activity. In this study, we implemented a multicriteria decision making method (MCDM) for evaluating the ergonomic performance of recreational DCs on the market based on to seven performance criteria determined by three experts. The pairwise criteria comparisons were acquired from the survey performed on 4 female and 16 male divers from different countries (Turkey, Italy, Belgium, Greece, Australia, Netherlands, Norway, England, Finland and Germany). Ten DCs belonging to the highest market segment based on comparable retail prices were used in this study. The data was used in Analytic Hierarchy Process (AHP) to determine the importance weights of the criteria. This study put forward an objective method for the assessment of DCs in terms of ergonomics. For future studies, MCDM methods can be used in different evaluations of recreational and technical DCs, such as efficiency analysis, taking into account the prices as an input thus enabling cross segmental comparisons as well.

___

  • Albayrak, E., Erensal, Y.C., 2004. Using Analytic Hierarchy Process (AHP) to Improve Human Performance: An Application of Multiple Criteria Decision Making Problem. Journal of Intelligent Manufacturing, 15, 491-503.
  • Azzopardi, E., Sayer, M., 2012. Estimation of depth and temperature in 47 models of diving decompression computer International. Journal of the Society for Underwater Technology, 31, 3–12.
  • Blogg, S. L., Michael, A. L., Møllerløkken, A., 2011. In: Proceedings of the Validation of Dive Computer Workshop. EUBS and Akademika Publishing. Ağustos 24. Gdansk, Poland, 128 p.
  • Rezaei, J., Fahim, P.B.M., Tavasszy, L., 2014. Supplier selection in the airline retail industry using a funnel methodology: Conjunctive screening method and fuzzy AHP. Expert Systems with Applications, 41, 8165-8179.
  • Boycott, A. E., Damant, G. C. C., Haldane, J. S. 1908. The Prevention of Compressed Air Illness. Journal of Hygiene, 8, 342-443.
  • Bruce, R.W., Timothy, R.O., 2001. Reduced gradient bubble model: Diving algorithm, basis and comparisons. In: NAUI Technical Diving Operations, Tampa, Florida, USA, 1-36.
  • Cialoni, D., Pieri. M., Balestra, C., Marroni, A., 2017 Dive Risk Factors, Gas Bubble Formation, and Decompression Illness in Recreational SCUBA Diving: Analysis of DAN Europe DSL Data Base. Frontiers in Psychology, 8, 1587. doi: 10.3389/fpsyg.2017.01587.
  • Choudhary, D., Shankar, R. 2012. A STEEP-fuzzy AHPTOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy, 42, 510-521.
  • Direction of Commander Naval Sea Systems Command. 2016. U.S. Navy Dive Manual, Rev 7. Washington, DC, U.S. Navy. 2016. Chapter 9-65, 495-516.
  • Fast Facts: Recreational Scuba Diving and Snorkeling. The Diving Equipment and Marketing Association (DEMA), 2013
  • Francis, T.J.R., Smith, D.J., eds. 1991. Describing decompression illness, 42nd Workshop of the Undersea and Hyperbaric Medical Society, Bethesda, MD.
  • Gungor, Z., Serhadlioglu, G., Kesen, S.E., 2009. A fuzzy AHP approach to personnel selection problem. Applied Soft Computing, 9, 641—646.
  • Huggings, K.E., 2006. Evaluation of Dive Computer Options for Potential Use in 300 fsw Heliox/Trimix Surface Supplied Scientific Diving. In: Advanced Scientific Diving Workshop; Şubat 23-24; Smithsonian Institution, Washington DC, USA.
  • Keller, H., Bühlmann, A., 1965. Deep diving and short decompression by breathing mixed gases. Journal of Applied Physiology, 20, 1267–70.
  • Lippmann, J., Wellard, M., 2004. Comparing Dive Computers. South Pacific Underwater Medicine Society (SPUMS) Journal, 34, 124-129.
  • Ozen, O., 2008. QFD application for a new dive computer manufacturing. Galatasaray Üniversitesi, Endüstri Mühendisliği Bölümü, Lisans Bitirme Tezi, 63 p, Istanbul, Turkey.
  • Ozyigit, T., Egi, S.M., Denoble, P., Balestra, C., Aydin, S., Vann, R., Marroni, A., 2010. Decompression illness medically reported by hyperbaric treatment facilities: Cluster Analysis of 1929 cases. Aviation, Space, and Environmental Medicine, 81, 1-5.
  • Ozyigit, T., Egi, S.M., 2014. Commercial diver selection using Multiple-Criteria Decision-Making methods. Undersea and Hyperbaric Medicine 41(2014), 565572.
  • Ozyigit, T., Yavuz, C., Pieri, M., Egi, S.M., Egi, B., Altepe, C., Cialioni, D., Marroni, A., 2016. Data Mining on Divers Alert Network DSL Database: Classification of Divers, Advances in Data Mining. Applications and Theoretical Aspects, Volume: 9728 of the series Lecture Notes in Computer Science, Springer International Publishing, (16th Industrial Conference on Data Mining, ICDM 2016), New York, NY, USA, 96109.
  • Saaty, T.L., 1980. Analytic Hierarchy Process. New York: McGraw-Hill, 205
  • Sayer, M.D.J., Azzopardi, E., Sieber, A., 2016. User settings on dive computers: reliability in aiding conservative diving. Diving and Hyperbaric Medicine, 46, 98-110.
  • Taylan, O., Bafail, A.O., Abdulaal, R.M.S., Kabli, M.R., 2014. Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116.
  • Yurdakul, M., 2004. Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model. Robotics and Computer-Integrated Manufacturing, 20, 329-340.