RSS Center, Korea Electrotechnology Research Institute, Ansan 15588, Korea.
Department of Computer Science, University of Bristol, Bristol BS8 1TR, UK.
Sensors (Basel). 2022 Jan 4;22(1):371. doi: 10.3390/s22010371.
One of the major challenges for blind and visually impaired (BVI) people is traveling safely to cross intersections on foot. Many countries are now generating audible signals at crossings for visually impaired people to help with this problem. However, these accessible pedestrian signals can result in confusion for visually impaired people as they do not know which signal must be interpreted for traveling multiple crosses in complex road architecture. To solve this problem, we propose an assistive system called CAS (Crossing Assistance System) which extends the principle of the BLE (Bluetooth Low Energy) RSSI (Received Signal Strength Indicator) signal for outdoor and indoor location tracking and overcomes the intrinsic limitation of outdoor noise to enable us to locate the user effectively. We installed the system on a real-world intersection and collected a set of data for demonstrating the feasibility of outdoor RSSI tracking in a series of two studies. In the first study, our goal was to show the feasibility of using outdoor RSSI on the localization of four zones. We used a k-nearest neighbors (kNN) method and showed it led to 99.8% accuracy. In the second study, we extended our work to a more complex setup with nine zones, evaluated both the kNN and an additional method, a Support Vector Machine (SVM) with various RSSI features for classification. We found that the SVM performed best using the RSSI average, standard deviation, median, interquartile range (IQR) of the RSSI over a 5 s window. The best method can localize people with 97.7% accuracy. We conclude this paper by discussing how our system can impact navigation for BVI users in outdoor and indoor setups and what are the implications of these findings on the design of both wearable and traffic assistive technology for blind pedestrian navigation.
盲人或视力障碍者（BVI）在步行通过路口时面临的主要挑战之一是安全出行。许多国家现在在交叉路口为视障人士生成可听见的信号，以帮助解决这个问题。然而，这些无障碍行人信号可能会对视障人士造成混淆，因为他们不知道在复杂的道路结构中要解读哪个信号才能通过多个路口。为了解决这个问题，我们提出了一种名为 CAS（交叉协助系统）的辅助系统，该系统扩展了 BLE（蓝牙低能耗）RSSI（接收信号强度指示）信号在户外和室内定位跟踪的原理，并克服了户外噪声的固有限制，从而能够有效地定位用户。我们在真实的十字路口安装了该系统，并收集了一组数据，通过两项研究展示了在户外 RSSI 跟踪方面的可行性。在第一项研究中，我们的目标是展示在四个区域的定位中使用户外 RSSI 的可行性。我们使用 k-最近邻（kNN）方法，结果表明其准确率达到了 99.8%。在第二项研究中，我们将工作扩展到更复杂的九个区域设置，并评估了 kNN 和另一种方法，即支持向量机（SVM），使用各种 RSSI 特征进行分类。我们发现，SVM 使用 RSSI 平均值、标准偏差、中位数、RSSI 的五分位距（IQR）作为分类特征时性能最佳。最佳方法可以将人员的定位准确率提高到 97.7%。我们最后讨论了我们的系统如何影响户外和室内环境中 BVI 用户的导航，并讨论了这些发现对视障行人导航的可穿戴和交通辅助技术设计的影响。
