دراسة وتنفيذ نظام لمساعدة كبار السن والأشخاص محدودي الحركة على إصدار الأوامر عن طريق التعرف على حركة اليد

الموضوعات

علوم تطبيقية وتكنولوجية

روابط وملفات

التعريف والنوع

رقم الوثيقة

0d94684f-8247-4b8e-843b-62bcb278e6f9

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

1966381_1.pdf

بيانات النشر

ترجمة العنوان

System Study And Implementation For Assisting The Elderly And Disabled To Give Orders Via Identification Of Hand Movement

ألقاب المؤلفين

[{"name_ar":"حسام كوجك ","title_ar":"اعداد","title_en":"Preparation"},{"name_ar":"عزيز كجو","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"محمد ترياكي","title_ar":"اشراف","title_en":"Supervision"}]

اللغة

Arabic

المصدر والدورية

اسم المصدر

دراسة وتنفيذ نظام لمساعدة كبار السن والأشخاص محدودي الحركة على إصدار الأوامر عن طريق التعرف على حركة اليد

المحتوى والصفحات

عدد الصفحات

127

ترجمة الملخص

In this thesis we present an electronic system for static hand gesture recognition in real time, based on hand edge detection. We use webcam to capture normal hand image in the first step, then we process the image by filtering and applying some morphological operations in order to extract the features of the hand by detecting hand’s edge . We use these features to design our algorithm of gestures recognition. The aim of this search is to provide an easy and efficient way for older and disabled people to communicate with the environment, they can, with our system, by gestures send some voice orders, and control some home equipments such as TV, light, up or down beds and so on. We provide in this system ten different gestures applyied by right hand, which can be chosen by the user himself, and can be implemented immediately without need to wear any gloves or to be connected to any sensors which could bother the user and limit his movement. In this system we designed a new algorithm which depends on hand edge detection, and we proved that hand edge detection can be successful and efficient way for gestures recognition in real time. The accuracy we achieved in our system is 96% in 1.4 second, which can be considered as a good accuracy in short time.

إشراف وإعداد

الإشراف

عزيز كجو, محمد ترياكي

الإعداد

حسام كوجك

الاقتباسات الببليوغرافية