طبقه بندی حالات منتخب صورت از طریق سیگنال الکترومایوگرافی تک کاناله به وسیله شبکه عصبی مصنوعی و درخت تصمیم
الموضوعات :نیلوفر شجاعیان 1 , بابک رضایی افشار 2
1 - گروه مهندسی پزشکی بیوالکتریک دانشگاه آزاد اسلامی واحد تهران مرکزی. تهران
2 - Head of Cognetive science lab ,incubator center of mohaghegh ardebili univrsity.
الکلمات المفتاحية: حالات صورت, الکترومایوگرافی, شبکه عصبی مصنوعی, درخت تصمیم, تحلیل مؤلفه‌های اساسی,
ملخص المقالة :
این مطالعه، به بررسی حالات مختلف صورت از طریق الکترومایوگرافی تک‌کاناله با حداقل پیچیدگی محاسباتی به‌منظور استفاده در کاربردهای واقعی به‌صورت بلادرنگ می‌پردازد. داده‌های الکترومایوگرافی عضلات صورت نشأت گرفته از پتانسیل الکتریکی اجرای حالات مختلف صورت است که به‌ازای حالات مختلف می‌تواند مقادیر متفاوتی را داشته باشد. در این مطالعه، داده‌های الکترومایوگرافی از حالت‌های مختلف عضلات صورت 6 نفر از شرکت‌کنندگان زن با میانگین سنی ۵±۲۵ سال ثبت گردید و پس از اعمال فیلتر باترورث مرتبه چهار، داده‌ها در پنجره‌های ۱۰ ثانیه‌ای تقسیم‌بندی شد و سپس ۱۲ ویژگی در حوزه زمان و فرکانس از آن استخراج گردید. برای کاهش حجم محاسباتی از روش کاهش ویژگی PCA و برای طبقه‌بندی داده‌ها از شبکه عصبی مصنوعی و درخت تصمیم استفاده شد. پس از اعمال فیلترها و استخراج ویژگی، داده‌ها توسط طبقه‌بندهای پرسپترون چندلایه با صحت ۷۳.۵۷ درصدی، درخت تصمیم با صحت ۹۵.۱۶ درصدی، MPL-PCA با صحت ۶۵.۲۰ درصدی و DT-PCA با صحت ۹۶.۳۳درصدی مورد طبقه‌بندی قرار گرفتند. از چالش‌های این مطالعه می‌توان به تعداد کم افراد شرکت‌کننده و تک‌کاناله بودن اشاره کرد. همچنین در مطالعات آینده می‌توان داده‌های عضلات صورت را به‌صورت ترکیبی استفاده کرد.
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