Akbulut A., Aydin M. A. , ZAİM A. H.

ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING, vol.17, no.1, pp.3181-3186, 2017 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 1
  • Publication Date: 2017
  • Journal Indexes: Emerging Sources Citation Index, Scopus, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.3181-3186


This paper presents a mobile healthcare (mHealth) system for estimation of visual impairment that provides easiness by specifying the degree of an eye as orthoscopes. Our proposed system called AcuMob which is an Android based mobile application aimed to be used by patients who have myopia. In the crowd society, our proposed app will be implemented faster than the traditional ophthalmologic examination treatments as an alternative. Because AcuMob can be used in everywhere in any time slot, it is offered in the area where the ophthalmologist is not available. The system is developed with using Xamarin framework and voice commands are used to interact with mobile app. Some preferable letters that are suggested by the ophthalmologists were used in the system. The letter categories are specified according to letters' sizes. In the start-up screen, the biggest letter is demonstrated and if the user responds correct answer, the letter's size is being smaller. However, if the user says wrong answer three times consecutively, eyesight ratio is produced by the system to the user referencing to Snellen Chart's information. This article has aimed at making a prediction about the visual impairment's degree. Thanks to AcuMob, people can get idea about their visual acuity without consulting to an eye medical doctor (MD). For the evaluation of systems' reliability, field tests were performed at Bayrampasa Goz Vakfi Hospital in Istanbul with two ophthalmologist specialists. At the end of trials, the actual diagnosed degrees and the equivalent degree of eyesight ratios according to Snellen Chart's information is compared and the success rates are shown. The system achieved at the 65% of average success rate, which can give users an idea about current condition of their visions.