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A scalable Bluetooth Low Energy approach to identify occupancy patterns and profiles in office spaces

机译：可扩展的蓝牙低能量方法，用于识别办公空间中的占用模式和配置文件

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摘要

Building occupants are often assumed to follow deterministic schedules in building performance simulation programs. Therefore, to accurately capture the dynamic nature of the occupants' movement patterns, researchers have proposed various indoor localisation technologies to infer occupancy information with varying degrees of accuracy and resolution. Among these technologies, the Bluetooth Low Energy (BLE) technology emerged as a popular alternative due to its availability in smartphone devices, as well as its low cost and power demand. In this study, we proposed a scalable and less intrusive occupancy detection method that leverages existing BLE technologies found in smartphone devices to perform zone-level occupancy localisation, without the need for a mobile application. The proposed method uses a network of BLE beacons for data collection before passing the pre-processed data into a machine learning model to infer the occupants' zone-level location. A supervised ensemble model and a semi-supervised clustering model were proposed and evaluated to identify the best performing model. The feasibility of the proposed method is demonstrated during a five-week case study involving two office spaces in an academic building in Singapore. While the supervised ensemble model produced the best performance in terms of accuracy and macro-average fl-score, the semi-supervised model was able to produce a reasonable performance while using a fraction of the training data (4%) and time needed by the supervised model. By analysing the occupancy information obtained through the best performing model, we further identified a set of occupancy profiles to represent the diverse occupancy patterns observed in the study area.
机译：建立占用者通常在构建性能模拟程序方面遵循确定性计划。因此，为了准确地捕捉乘员运动模式的动态性质，研究人员提出了各种室内定位技术，以推断出具有不同程度的准确性和分辨率的占用信息。在这些技术中，由于其在智能手机设备的可用性以及其低成本和功率需求，蓝牙低能量（BLE）技术作为一种流行的替代品。在这项研究中，我们提出了一种可扩展且富于侵入性的占用检测方法，利用智能手机设备中发现的现有BLE技术，以执行区域级占用定位，而无需移动应用程序。该方法在将预处理的数据传递到机器学习模型中之前，所提出的方法使用用于数据收集的BLE信标网络以推断乘员区域级位置。提出了监督集合模型和半监督聚类模型，并评估以确定最佳性能模型。拟议方法的可行性在涉及在新加坡学术建筑中的两个办公空间的五周案例研究中进行了演示。虽然监督集合模型在准确性和宏观平均飞行方面产生了最佳性能，但半监督模型能够在使用培训数据的一小部分（<4％）和时间的一部分时产生合理的性能监督模型。通过分析通过最佳执行模型获得的占用信息，我们进一步识别了一组占用型材，以表示研究区域中观察到的各种占用模式。

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