تشخیص وضعیت غیرعادی و تصحیح خطاهای گذرا به‌صورت بی‌درنگ در شبکه‌های حسگر بی‌سیم بدنی

نوع مقاله: مقاله کامل پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی پزشکی، گروه بیوالکتریک، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران

2 استادیار، دانشکده مهندسی برق، دانشگاه صنعتی شریف، تهران

10.22041/ijbme.2017.72877.1265

چکیده

شبکه‌های حسگر بی‌سیم بدنی، مجموعه‌ای از حسگرهای حیاتی برای مشاهدة وضعیت سلامت بیماران از راه دور هستند. تشخیص و تمایز وضعیت‌های غیرعادی، شامل خطای حسگرها یا وضعیت‌های اضطراری، علاوه‌بر رفع نیاز دائمی به متخصص، می‌تواند باعث کاهش نرخ هشدارهای نادرست شود. در این پژوهش، برای تشخیص و تمایز وضعیت‌های غیرعادی، روشی تک‌متغیره، بدون سرپرست و بی‌درنگ با قابلیت پیاده‌سازی سخت‌افزاری آسان، ارائه شده و همچنین روشی جدید برای تصحیح خطاهای گذرا پیشنهاد شده است. روش‌ پیشنهادی، سریع‌تر از روش‌های موجود در پژوهش‌های پیشین عمل می‌کند و دقت آن به‌طور کامل قابل‌مقایسه با روش‌های موجود است. شبیه‌سازی روش پیشنهادی روی مجموعه ‌داده‌های اینترنتی انجام شده است و نتایج حاصل از آن با روش‌های موجود، مقایسه شده‌اند. همچنین برای ارزیابی و اعتبارسنجی نهایی روش پیشنهادی، از داده‌های ثبت‌شده در یک آزمایش واقعی استفاده شده است، که نتایج آن بر عملکرد مناسب روش پیشنهادی در تشخیص وضعیت‌های غیرعادی و تصحیح خطاهای گذرا تأکید می‌کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Real-Time Anomaly Detection and Transient Fault Correction for Wireless Body Area Networks

نویسندگان [English]

  • Mohammad Saeed Zare Dehabadi 1
  • Mehran Jahed 2
1 Ph.D Student, Electric Engineering Department, Sharif University of Technology, Tehran, Iran
2 Assistant Professor, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
چکیده [English]

Wireless Body Area Networks (WBAN) consist of a collection of biosensors utilized to remotely monitor the health status of patients. High accuracy anomaly detection and distinguishing between faults and physiological anomalies play a key role in proper detection of real emergency situations and is cruicial in lowering False Alarm Rate (FAR) cases. In this work, a univariate, unsupervised and real-time anomaly detection algorithm is proposed based on Hampel identifier and its performance is compared with previous and reported methods. Furthermore, a novel prediction method is introduced and utilized in order to correct for transient faults that are quite probable in WBANs, due to inherent noise and artifact of physiological sensors. Proposed method is shown to be faster than reported approaches while providing comparable. Final validation of the proposed method is performed by a real experimental dataset along with intentionally added faults and physiological anomalies. The results illustrate appropriate anomaly detection ability of the proposed approach.

کلیدواژه‌ها [English]

  • WBAN
  • Anomaly detection
  • Hampel identifier
  • Fault correction
  • kNN method

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