One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

中文翻译：

---

使用统计技术的无线人体局域网的精确传感器故障检测技术

无线人体局域网（WBAN）的主要挑战之一是传感器故障检测。本文报告了一种精确识别故障传感器的方法，该方法应有助于用户识别真实的医疗状况并减少错误警报的发生率，从而提高WBAN提供的服务质量。提出的传感器故障检测（SFD）算法基于Pearson相关系数和简单的统计方法。所提出的方法使用Pearson相关系数来识别强相关的参数，并且所提出的SFD算法可以检测出故障的传感器。我们使用重症监护多参数智能监控数据库中的两个数据集对提出的SFD算法进行了验证，并将结果与​​现有方法进行了比较。该算法的时间复杂度也与现有方法进行了比较。该算法对数据集1和数据集2的检测率较高，误报率较低，准确率分别为97.23％和93.99％。