DeepFusion: A Deep Learning Framework for the Fusion of Heterogeneous Sensory Data
DeepFusion: A Deep Learning Framework for the Fusion of Heterogeneous Sensory Data
复制标题
DOI：
10.1145/3323679.3326513
复制
发表时间：
2019-07
期刊：
影响因子：
--
通讯作者：
Hongfei Xue;Wenjun Jiang;Chenglin Miao;Ye Yuan;Fenglong Ma;Xin Ma;Yijiang Wang;Shuochao Yao;Wenyao Xu;Aidong Zhang;Lu Su
中科院分区：
文献类型：
--
作者：
Hongfei Xue;Wenjun Jiang;Chenglin Miao;Ye Yuan;Fenglong Ma;Xin Ma;Yijiang Wang;Shuochao Yao;Wenyao Xu;Aidong Zhang;Lu Su
近年来，人们投入了大量的研究工作来构建智能且用户友好的物联网系统，以使新一代应用程序能够执行复杂的传感和识别任务。在许多此类应用中，通常有多个不同的传感器监视同一对象。这些传感器中的每一个都可以被视为一个信息源，并为我们提供了观察对象的独特“视图”。直观地说，如果我们能够结合多个传感器携带的互补信息，我们将能够提高传感性能。为此，我们提出了 DeepFusion，一个统一的多传感器深度学习框架，用于学习异构传感数据的信息表示。 DeepFusion 可以结合按数据质量加权的不同传感器信息，并整合跨传感器相关性，从而使广泛的物联网应用受益。为了评估所提出的 DeepFusion 模型，我们使用商业化的可穿戴和无线传感设备建立了两个现实世界的人类活动识别测试平台。实验结果表明，DeepFusion 的性能优于最先进的人类活动识别方法。
In recent years, significant research efforts have been spent towards building intelligent and user-friendly IoT systems to enable a new generation of applications capable of performing complex sensing and recognition tasks. In many of such applications, there are usually multiple different sensors monitoring the same object. Each of these sensors can be regarded as an information source and provides us a unique "view" of the observed object. Intuitively, if we can combine the complementary information carried by multiple sensors, we will be able to improve the sensing performance. Towards this end, we propose DeepFusion, a unified multi-sensor deep learning framework, to learn informative representations of heterogeneous sensory data. DeepFusion can combine different sensors' information weighted by the quality of their data and incorporate cross-sensor correlations, and thus can benefit a wide spectrum of IoT applications. To evaluate the proposed DeepFusion model, we set up two real-world human activity recognition testbeds using commercialized wearable and wireless sensing devices. Experiment results show that DeepFusion can outperform the state-of-the-art human activity recognition methods.