استفاده از تکنیک‌های مشابهت رشته‌ای برای تشخیص فعالیت‌های روزانه در خانه‌های مجهز به شبکه حسگر دودویی

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه اراک، اراک، ایران

چکیده

در سیستم‌هایی که افراد در فعالیت‌های روزانه خود به مراقبت ویژه نیاز دارند، الگوریتم‌های تشخیص فعالیت انسانی کاربرد دارند. روش‌های مختلف یادگیری ماشین، از جمله مدل مخفی مارکوف و روش‌های مرتبط به آن، به طور گسترده‌ای برای حل مساله تشخیص فعالیت انسانی استفاده شده‌اند. در کارهای قبلی، روش‌های مبتنی بر مدل مخفی مارکوف از فرض استقلال شرطی برای محاسبه احتمال مشاهدات استفاده شده است. در این تحقیق، به جای فرض استقلال شرطی، یک مدل احتمالی جدید برای فضای رشته‌ها، بر اساس تاب‌خوردگی زمان پویا و فاصله لونشتاین وزنی پیشنهاد شده است. مدل احتمالی پیشنهادی، که با یک مدل مخفی شبه‌مارکف ترکیب شده، روی یکی از مجموعه داده‌های در دسترس اعمال شده است. نتایج حاصله نشان‌ می‌دهد که استفاده از مدل پیشنهادی دقت شناسایی فعالیت‌های روزانه را به میزان قابل توجهی اقزایش می‌دهد. کلیه کدها و داده‌ها مقاله حاضر، از طریق پیوند github.com/ashnik1353 در دسترس هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Using string similarity techniques to detect daily activities in smart homes equipped with a binary sensor network

نویسندگان [English]

  • Ashkan Nikaiin
  • Mohsen Rahmani
Department of Computer Engineering, Faculty of Engineering, Arak University, Arak, Iran
چکیده [English]

Human activity recognition can be employed in systems that provide support to people who need special care in their daily activities. Various machine learning methods, including Hidden Markov Models and their extensions, are widely used to deal with this problem. In the previous works, HMM-based methods use the conditional independent assumption to compute the probability of a segment of observations. In this research, instead of conditional independent assumption, a new probabilistic model for string space, based on Dynamic Time Warping and Weighted Levenstein Distance is proposed. The model, combined with the Hidden semi-Markov Model, is applied to a publicly available dataset. The results show that the use of the proposed model significantly increases the accuracy of identifying daily activities. All the code and data of this paper are available through the link github.com/ashnik1353.

کلیدواژه‌ها [English]

  • Activity recognition
  • Hidden semi Markov model
  • String dissimilarity
  • Weighted Levenstein Distance
  • Dynamic Time Warping
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