ارائه یک مدل فراابتکاری برای تشخیص ریزحرکات انسان مبتنی بر حس‌گرهای اینرسی

نویسندگان

دانشکده فنی و مهندسی - دانشگاه رازی

چکیده

پیشرفت اخیر حس‌گرهای اینرسی مبتنی بر فناوری سیستم‌های میکرو الکترومکانیکی امکان طراحی دستگاه‌های پوشیدنی را برای تشخیص خودکار حرکات انسان فراهم نموده است. رهگیری حرکات طبیعی بدن در تعامل با محیط، نیازمند پردازش سیگنال‌های اینرسی در سطح جزئی است. این رهگیری به کمک تشخیص حرکات کوتاه و پیوسته انسان انجام می‌شود. در این مقاله روشی برای تشخیص ریزحرکات پیوسته انسان برمبنای پردازش سیگنال‌های اینرسی معرفی شده است. در این روش، ابتدا با استفاده از الگوریتم‌های ناوبری اینرسی، سیگنال شتاب خطی و جاذبه زمین محاسبه می‌شود؛ سپس با ترکیب این دو سیگنال، ویژگی‌های متمایزکننده استخراج می‌شود. نوآوری این مقاله برای تشخیص ریزحرکات پیوسته معرفی یک مدل طبقه‌بندی جدید تحت عنوان مدل‌های شرطی است. هر مدل متعلق به یک کلاس ریزحرکت است که عملیاتی را به‌منظور تشکیل یک مجموعه عبارت منطقی و طبقه‌بندی نمونه‌های آن کلاس خاص انجام می‌دهد. در روش معرفی شده، به‌منظور پیدا کردن عبارت‌های منطقی بهینه برای هر مدل، از بهینه‌سازی ازدحام ذرات (Particle Swarm Optimization) استفاده شده است. به‌منظور ارزیابی، روش پیشنهادی برای تشخیص ریزحرکات پیوسته نماز مورد آزمایش قرار گرفته است. اجرای الگوریتم بر روی جامعه آماری از نمازگزاران و مقایسه آن با مدل‌های مرسوم طبقه‌بندی ریزحرکات پیوسته، نشان از درستی و تشخیص بالای روش پیشنهادی دارد.

کلیدواژه‌ها


عنوان مقاله [English]

A Meta-heuristic Model for Human Micro Movements Recognition Based on Inertial Sensors

نویسندگان [English]

  • M. Sepahvand
  • F. Abdali-Mohammadi
Faculty of Engineering, Razi University, Kermanshah, Iran
چکیده [English]

Current developments in inertial sensors based on Microelectromechanical Systems technology allows us to design wearable devices for human movements automatic detection. Detection of human movements in natural environments needs detailed inertial signals processing. These activities are detected using human short movements detection. In this paper a method is proposed for continuous human tiny movements based on inertial signal processing. In the proposed method, at first linear acceleration and earth gravity signals are calculated using inertial navigation algorithms. Then discriminant features are extracted using a combination of these to signals. Innovation of this paper is introducing a new classification algorithm for continuous tiny movements recognition named Conditional Models. Each model belongs to a class of micro movement which performs some operations for generating a logical expression set and classifying the samples in that class. The proposed method uses the Particle Swarm Optimization to finding the optimized logical expression for each model. In order to evaluating, this method is tested on prayer micro movements recognition. Running the algorithm on the population of prayers and comparing with well-known micro movements classification models demonstrates the accuracy and high recognition of the proposed method.

کلیدواژه‌ها [English]

  • particle swarm optimization
  • inertial sensors
  • micro movements
  • classification
  • conditional models
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