การตรวจจับการล้มตามลำดับความรุนแรงสำหรับผู้สูงอายุโดยใช้สัญญาณ WiFi
Severity-Based Fall Detection for Elderly Using WiFi Sensing
Abstract
อุบัติเหตุการล้มของผู้สูงอายุมักเกิดขึ้นในห้องน้ำหรือห้องนอนซึ่งต้องการความเป็นส่วนตัว ไม่สะดวกต่อการติดตั้งกล้องวงจรปิด เพื่อลดการติดตั้งอุปกรณ์เพิ่มเติมและไม่รุกล้ำความเป็นส่วนตัว บทความนี้ได้ตรวจจับและจำแนกความรุนแรงการล้มของผู้สูงอายุโดยใช้อุปกรณ์ปล่อยสัญญาณวายฟายที่มีอยู่ภายในบ้านทั่วไป ซึ่งพิจารณาจำแนกความรุนแรงของการล้มเป็น 3 ระดับ คือ เล็กน้อย ปานกลาง และรุนแรง โดยเก็บข้อมูล Channel State Information (CSI) เมื่อเกิดเหตุการณ์ลื่นล้มตั้งแต่ก่อนล้มไปจนถึงหลังล้ม 27 เหตุการณ์ จำนวนเหตุการณ์ละ 600 ชุดข้อมูล สถานที่ใช้ทดลองคือห้องสตูดิโอที่ประกอบด้วยห้องนอนพร้อมห้องน้ำ ทดลองภายใต้ 3 สถานการณ์คือ Line-of-Sight (LoS), Non-Line-of-Sight (NLoS) และ NLoS Through the Wall พบว่า มีความแม่นยำอยู่ที่ 98.2%, 97.6% และ98.3% ตามลำดับ โดยใช้เทคนิคการจำแนกแบบ Support Vector Machine (SVM) การพิจารณาการล้มตามลำดับความรุนแรงสามารถนำไปพัฒนาเพื่อติดตั้งระบบแจ้งเตือนและการรักษาที่เหมาะสมหลังเกิดเหตุการณ์ล้มของผู้สูงอายุในอนาคตต่อไป
Elderly falls often occur in bathrooms or bedrooms, which require privacy and inconvenient to install CCTV to reduce the installation of additional equipment and not invade your privacy. This paper detects and categorizes the fall severity of the elderly by using WiFi signal devices that are common in homes. The severity of the fall was classified into three levels: mild, moderate, and severe. It collects Channel State Information (CSI) on 27 slip events from pre-fall to post-fall 600 datasets per event. The experimental site was a studio with a bedroom and bathroom. It was tested under three scenarios: Line-of-Sight (LoS), Non-Line-of-Sight (NLoS), and NLoS Through the Wall. Accuracy was 98.2%, 97.6%, and 98.3%, respectively, using a Support Vector Machine (SVM) classification technique. Determination of falls in order of severity could be developed to implement alert and treatment systems appropriate after the event of a fall of the elderly in the future.
Keywords
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DOI: 10.14416/j.kmutnb.2022.10.009
ISSN: 2985-2145
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