A reliable adaptive hybrid classifier (hAHC), which combines a posture-based adaptive signal segmentation algorithm with a multi-layer perceptron (MLP) classifier, together with a plurality voting approach, was proposed and evaluated in this study. The hAHC model was evaluated using a real-time posture recognition framework that sought to identify five behaviours (sitting, walking, standing, running, and lying) based on simulated crowd security scenarios. It was compared to a single MLP classifier (sMLP) and a static hybrid classifier (hSHC) from three perspectives (classification precision, recall and F1-score) that used the real-time dataset collected from unfamiliar subjects. Experimental results showed that the hAHC model improved the classification accuracy and robustness slightly more than the hSHC, and significantly more compared to the sMLP (hAHC 82%; hSHC 79%; sMLP 71%). Additionally, the hAHC approach displayed the real-time results as animated figures in an adaptive window, in contrast to the hSHC which used a fixed size-sliding temporal window that as our results demonstrated, was less suitable for presenting real-time results. The main research contribution from this study has been the development of an efficient software-only-based sensor calibration algorithm that can improve accelerometer precision, together with the design of a posture-based adaptive signal segmentation algorithm that cooperated with an adaptive hybrid classifier to improve the performance of real-time posture recognition.

提出并评估了一种可靠的自适应混合分类器（hAHC），该方法将基于姿态的自适应信号分割算法与多层感知器（MLP）分类器相结合，并采用了多种投票方法。该hAHC使用实时姿态识别框架，试图找出基于模拟的人群安全方案5点的行为（坐，行走，站立，跑步，和说谎）模型进行了评价。从三个方面（分类精度，召回率和F1得分），使用从陌生对象收集的实时数据集，将其与单个MLP分类器（sMLP）和静态混合分类器（hSHC）进行了比较。实验结果表明，hAHC模型提高了分级精度和鲁棒性略多于hSHC，更相比显著SMLP（hAHC 82％; hSHC 79％; SMLP 71％）。此外，该hAHC方法显示的实时结果作为动画图中的自适应窗口，在对比的hSHC如我们的结果所示，它使用了固定大小的滑动时间窗口，不适合呈现实时结果。这项研究的主要研究成果是开发了一种可以提高加速度计精度的基于软件的高效传感器校准算法，以及与自适应混合分类器配合使用的基于姿势的自适应信号分割算法的设计，从而可以提高加速度传感器的精度。实时姿势识别的性能。