A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices
A Deep Learning Approach for Human Activities Recognition From Multimodal Sensing Devices
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DOI：
10.1109/access.2020.3027979
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Orisatoki, Mobolaji O.
中科院分区：
文献类型：
--
作者：
Ihianle, Isibor Kennedy;Nwajana, Augustine O.;Orisatoki, Mobolaji O.
使用深度学习技术，人类日常生活活动识别的研究得到了显着改善。传统的人类活动识别技术通常使用来自单一感测模态的启发式过程的手工特征。深度学习技术的发展通过从多模态传感设备中自动提取特征来准确识别活动，从而解决了大多数这些问题。在本文中，我们提出了一种使用卷积神经网络（CNN）和双向长短期记忆（BLSTM）组合的深度学习多通道架构。该模型的优点是CNN层执行原始传感器输入的直接映射和抽象表示，以用于不同分辨率的特征提取。BLSTM层充分利用了前向和后向序列，以显著提高用于活动识别的提取特征。我们在两个公开的数据集上评估了所提出的模型。实验结果表明，该模型的性能大大优于我们的基线模型和其他模型使用相同的数据集。它也证明了所提出的模型对多模态传感设备增强人类活动识别的适用性。
Research in the recognition of human activities of daily living has significantly improved using deep learning techniques. Traditional human activity recognition techniques often use handcrafted features from heuristic processes from single sensing modality. The development of deep learning techniques has addressed most of these problems by the automatic feature extraction from multimodal sensing devices to recognise activities accurately. In this paper, we propose a deep learning multi-channel architecture using a combination of convolutional neural network (CNN) and Bidirectional long short-term memory (BLSTM). The advantage of this model is that the CNN layers perform direct mapping and abstract representation of raw sensor inputs for feature extraction at different resolutions. The BLSTM layer takes full advantage of the forward and backward sequences to improve the extracted features for activity recognition significantly. We evaluate the proposed model on two publicly available datasets. The experimental results show that the proposed model performed considerably better than our baseline models and other models using the same datasets. It also demonstrates the suitability of the proposed model on multimodal sensing devices for enhanced human activity recognition.