Human activity recognition (HAR) is an emerging area of study and research field that explores the development of automated systems to identify and categorize human activities using data collected from various sensors. In the field of Human Activity Recognition (HAR), binary sensors offer a distinct approach by providing simpler on/off readings to indicate the presence of events such as door openings or light switch activations. Compared to other sensors used for HAR, binary sensors have several advantages, including lower cost, low power consumption, ease of installation, and privacy preservation. For instance, they can be effectively used in smart homes to detect when someone enters or leaves a room without user input. This study presents a systematic review of the state-of-the-art methods and techniques for HAR using binary sensors. We comprehensively consider five crucial aspects: data collection methods, preprocessing techniques, feature extraction and fusion strategies, classification algorithms, and evaluation metrics. Furthermore, we identify the gaps and limitations of the existing studies and provide directions for future research. This comprehensive and up-to-date review can serve as a valuable reference for researchers and practitioners in the field of HAR using binary sensors.

中文翻译：

---

使用二进制传感器进行人体活动识别的系统评价


人类活动识别 （HAR） 是一个新兴的研究领域，它探索开发自动化系统，以使用从各种传感器收集的数据来识别和分类人类活动。在人类活动识别 （HAR） 领域，二进制传感器提供了一种独特的方法，它提供更简单的开/关读数来指示是否存在事件，例如开门或电灯开关激活。与用于 HAR 的其他传感器相比，二进制传感器具有多项优势，包括成本更低、功耗低、易于安装和隐私保护。例如，它们可以有效地用于智能家居，以检测是否有人在没有用户输入的情况下进入或离开房间。本研究系统回顾了使用二进制传感器进行 HAR 的最新方法和技术。我们综合考虑了五个关键方面：数据收集方法、预处理技术、特征提取和融合策略、分类算法和评估指标。此外，我们确定了现有研究的差距和局限性，并为未来的研究提供了方向。这篇全面且最新的综述可以为使用二进制传感器的 HAR 领域的研究人员和从业者提供有价值的参考。