Due to rapid development of sensor technology, human activity recognition (HAR) using wearable inertial sensors has recently become a new research hotspot. Deep learning, especially convolutional neural network (CNN) that can automatically learn intricate activity features have gained a lot of attention in ubiquitous HAR task. Most existing CNNs process sensor input by extracting channel-wise features, and the information from each channel can be separately propagated in a hierarchical way from lower layers to higher layers. As a result, they typically overlook information exchange among channels within the same layer. In this article, we first propose a shallow CNN that considers cross-channel communication in HAR scenario, where all channels in the same layer have a comprehensive interaction to capture more discriminative features of sensor input. One channel can communicate with all other channels by graph neural network to remove redundant information accumulated among channels, which is more beneficial for deploying lightweight deep models. Extensive experiments are conducted on multiple benchmark HAR datasets, namely UCI-HAR, OPPORTUNITY, PAMAP2 and UniMib-SHAR, which indicates that the proposed method enables shallower CNNs to aggregate more useful information, and surpasses baseline deep networks and other competitive methods. The inference speed is evaluated via deploying the HAR systems on an embedded system.

Recently, the state-of-the-art performance in various sensor-based human activity recognition (HAR) tasks has been acquired by deep learning, which can extract automatically features from raw data. In standard convolutional neural networks (CNNs), there is usually the same receptive field (RF) size of artificial neurons within each feature layer. It is well known that the RF size of neurons is able to change adaptively according to the stimulus, which has rarely been exploited in HAR. In this article, a new multibranch CNN is introduced, which utilizes a selective kernel mechanism for HAR. To the best of our knowledge, it is for the first time to adopt an attention idea to perform kernel selection among multiple branches with different RFs in the HAR scenario. We perform extensive experiments on several benchmark HAR datasets, namely, UCI-HAR, UNIMIB SHAR, WISDM, PAMAP2, and OPPORTUNITY, as well as weakly labeled datasets. Ablation experiments show that the selective kernel convolution can adaptively choose an appropriate RF size among multiple branches for classifying numerous human activities. As a result, it can achieve a higher recognition accuracy under a similar computing budget.

Recent years have witnessed significant success of convolutional neural networks (CNNs) in human activity recognition (HAR) using wearable sensors. Nevertheless, prior works have an obvious drawback. An activity sample may contain heterogeneous sensor modalities from different body parts. Moreover, the significance of each modality will change over time. Because a normal convolution filter usually samples activity data at a fixed regular grid, it is hard to capture salient features of activities along different sensor modalities or time intervals. What is the best filter form for activity recognition still remains a challenging task. In this article, to resolve this issue, we present a new deformable convolutional network for recognizing human activities from intricate sensory data. Specifically, the learned offsets and the feature amplitudes are added into standard convolution, which can be modulated to allow more free-form deformation over the sampling grid for sensory data. Comparing previous results, we achieve state-of-the-art recognition accuracies, e.g., 82.91%, 80.02%, 97.35%, and 99.21%, respectively, on several benchmark HAR datasets, including OPPORTUNITY, UNIMIB-SHAR, USC-HAD, and WISDM, hence indicating the advantage of the proposed method. The visual analysis is provided, which shows that the deformation could be conditioned on different input activity samples. The receptive field and the sampling locations can be adjusted in an adaptive manner, which leads to better interpretability for deep model behaviors. Installing PyTorch on a Raspberry Pi 3 B plus system, we evaluate actual run time of the deformable model. The results show that the deformable filter is able to still maintain almost the same inference time, which is very beneficial for activity recognition tasks. Our work can promote further research by leveraging intermodulating information to connect the deformable convolution and attention modules.

Recently, the state-of-the-art performance in various sensor based human activity recognition (HAR) tasks have been acquired by deep learning, which can extract automatically features from raw data. In order to obtain the best accuracy, many static layers have been always used to train deep neural networks, and their weight connectivity in network remains unchanged. Pursuing the best accuracy in mobile platforms with a very limited computational budget at millions of FLOPs is impractical. In this paper, we make use of shallow convolutional neural networks (CNNs) with channel-selectivity for the use of HAR. As we have known, it is for the first time to adopt channel-selectivity CNN for sensor based HAR tasks. We perform extensive experiments on 5 public benchmark HAR datasets consisting of UCI-HAR dataset, OPPORTUNITY dataset, UniMib-SHAR dataset, WISDM dataset, and PAMAP2 dataset. As a result, the channel-selectivity can achieve lower test errors than static layers. The existing performance of deep HAR can be further improved by the CNN with channel-selectivity without any extra cost.

During the past decade, human activity recognition ( HAR ) using wearable sensors has become a new research hot spot due to its extensive use in various application domains such as healthcare, fitness, smart homes, and eldercare. Deep neural networks, especially convolutional neural networks ( CNNs ), have gained a lot of attention in HAR scenario. Despite exceptional performance, CNNs with heavy overhead is not the best option for HAR task due to the limitation of computing resource on embedded devices. As far as we know, there are many invalid filters in CNN that contribute very little to output. Simply pruning these invalid filters could effectively accelerate CNNs , but it inevitably hurts performance. In this article, we first propose a novel CNN for HAR that uses filter activation. In comparison with filter pruning that is motivated for efficient consideration, filter activation aims to activate these invalid filters from an accuracy boosting perspective. We perform extensive experiments on several public HAR datasets, namely, UCI-HAR ( UCI ), OPPORTUNITY ( OPPO ), UniMiB-SHAR ( Uni ), PAMAP2 ( PAM2 ), WISDM ( WIS ), and USC-HAD ( USC ), which show the superiority of the proposed method against existing state-of-the-art ( SOTA ) approaches. Ablation studies are conducted to analyze its internal mechanism. Finally, the inference speed and power consumption are evaluated on an embedded Raspberry Pi Model 3 B plus platform.