Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have made use of such models to recognise human activities in a sensor-based manner, which have achieved good performance. However, sensor-based HAR still faces challenges; in particular, recognising similar activities that only have a different sequentiality and similarly classifying activities with large inter-personal variability. This means that some human activities have large intra-class scatter and small inter-class separation. To deal with this problem, we introduce a margin mechanism to enhance the discriminative power of deep learning networks. We modified four kinds of common neural networks with our margin mechanism to test the effectiveness of our proposed method. The experimental results demonstrate that the margin-based models outperform the unmodified models on the OPPORTUNITY, UniMiB-SHAR, and PAMAP2 datasets. We also extend our research to the problem of open-set human activity recognition and evaluate the proposed method’s performance in recognising new human activities.

Human activity recognition (HAR) using body-worn sensors is an active research area in human-computer interaction and human activity analysis. The traditional methods use hand-crafted features to classify multiple activities, which is both heavily dependent on human domain knowledge and results in shallow feature extraction. Rapid developments in deep learning have caused most researchers to switch to deep learning methods, which extract features from raw data automatically. Most of the existing works on human activity recognition tasks involve multimodal sensor data, and these networks mainly focus on the top representation extracted from bottom-up feedforward process without reusing other features from bottom layers. In this paper, we present a novel hybrid deep learning network for human activity recognition that also employs multimodal sensor data; however, our proposed model is a ConvLSTM pipeline that makes full use of the information in each layer extracted along the temporal domain. Thus, we propose a dense connection module (DCM) to ensure maximum information flow between the network layers. Furthermore, we employ a multilayer feature aggregation module (MFAM) to extract features along the spatial domain, and we aggregate the features obtained from every convolutional layer according to the importance of features in different spatial locations. The output of the MFAM is input into two LSTM layers to further model the temporal dependencies. Finally, a fully connected layer and a softmax function are used to compute the probability of each class. We demonstrate the effectiveness of our proposed model on two benchmark datasets: Opportunity and UniMiB-SHAR. The results illustrate that our designed network outperforms the state-of-the-art models. We also conduct experiments on efficiency, multimodal fusion and different hyperparameters to analyze our proposed network. Finally, we carry out ablation and visualization experiments to reveal the effectiveness of the two proposed modules. INDEX TERMS Human activity recognition, deep learning, dense connection, multilayer feature aggregation, multimodal sensor data.

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