Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system.

中文翻译：

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基于多分类器系统的多传感器融合用于人类活动识别

在医疗保健应用中的多模式传感器已经得到了越来越多的研究，因为它有助于对人类行为，高强度运动管理，能量消耗估算和姿势检测进行自动和全面的监控。最近的研究表明，多传感器融合对于实现鲁棒性，高性能概括性，提供多样性并解决具有单个传感器值可能难以解决的挑战性问题的重要性。这项研究的目的是提出一种创新的多传感器融合框架，以改善人类活动检测性能并降低误识别率。该研究提出了一种多视图集成算法，以集成不同运动传感器的预测值。为此，计算有效的分类算法（例如决策树，Logistic回归和k最近邻用于实现多样化，灵活和动态的人类活动检测系统。为了提供紧凑的特征向量表示，我们研究了混合生物启发式进化搜索算法和基于相关性的特征选择方法，并评估了它们对从各个传感器模态提取的特征向量的影响。此外，我们利用合成过采样少数技术（SMOTE）算法来减少类不平衡的影响并提高性能结果。通过以上方法，本文提供了统一的框架来解决人类活动识别中的主要挑战。使用两个公开可用的数据集获得的性能结果显示，在检测特定活动详细信息方面，与基线方法相比有显着改进，并且错误率降低。我们评估的性能结果显示，与单个传感器和特征级融合相比，准确性，召回率，精度，F量度和检测能力（AUC）提高了3％到24％。提出的多传感器融合的好处是能够利用单个传感器和多个分类器系统的独特特征来提高识别精度。此外，研究表明，混合特征选择方法，基于多样性的多分类器系统有望改善基于移动和可穿戴传感器的人类活动检测和健康监测系统，具有广阔的发展前景。提出的多传感器融合的好处是能够利用单个传感器和多个分类器系统的独特特征来提高识别精度。此外，研究表明，混合特征选择方法，基于多样性的多分类器系统有望改善基于移动和可穿戴传感器的人类活动检测和健康监测系统，具有广阔的发展前景。提出的多传感器融合的好处是能够利用单个传感器和多个分类器系统的独特特征来提高识别精度。此外，研究表明，混合特征选择方法，基于多样性的多分类器系统有望改善基于移动和可穿戴传感器的人类活动检测和健康监测系统，具有广阔的发展前景。