With the rapid development in social media, single-modal emotion recognition is hard to satisfy the demands of the current emotional recognition system. Aiming to optimize the performance of the emotional recognition system, a multimodal emotion recognition model from speech and text was proposed in this paper. Considering the complementarity between different modes, CNN (convolutional neural network) and LSTM (long short-term memory) were combined in a form of binary channels to learn acoustic emotion features; meanwhile, an effective Bi-LSTM (bidirectional long short-term memory) network was resorted to capture the textual features. Furthermore, we applied a deep neural network to learn and classify the fusion features. The final emotional state was determined by the output of both speech and text emotion analysis. Finally, the multimodal fusion experiments were carried out to validate the proposed model on the IEMOCAP database. In comparison with the single modal, the overall recognition accuracy of text increased 6.70%, and that of speech emotion recognition soared 13.85%. Experimental results show that the recognition accuracy of our multimodal is higher than that of the single modal and outperforms other published multimodal models on the test datasets.

/pdf/audio-textual-emotion-recognition-based-on-improved-neural-1rdubuhwek.pdf

Audio-Textual Emotion Recognition Based on Improved Neural Networks

This paper proposes a novel spatial–temporal deep neural network (STDNN) that is applied to multi-view gait recognition. The STDNN comprises a temporal feature network (TFN) and a spatial feature network (SFN). In TFN, a feature sub-network is adopted to extract the low-level edge features of gait silhouettes. These features are input to the spatial–temporal gradient (STG) network that adopts a STG unit and a long short-term memory unit to extract the STG features. In SFN, the spatial features of gait sequences are extracted by multilayer convolutional neural networks from a gait energy image. The SFN is optimized by classification loss and verification loss jointly, which makes inter-class variations larger than intra-class variations. After training, the TFN and the SFN are employed to extract temporal and spatial features, respectively, which are applied to multi-view gait recognition. Finally, the combined predicted probability is adopted to identify individuals by the differences in their gaits. To evaluate the performance of the STDNN, extensive evaluations are carried out based on the CASIA-B, OU-ISIR, and CMU MoBo data sets. The best recognition scores achieved by STDNN are 95.67% under an identical view, 93.64% under a cross-view, and 92.54% under a multi-view. State-of-the-art approaches are compared with the STDNN in various situations. The results show that the STDNN outperforms the other methods and demonstrates the great potential of the STDNN for practical applications in the future.

Multi-View Gait Recognition Based on a Spatial-Temporal Deep Neural Network

Industry 5.0 and its technologies: A systematic literature review upon the human place into IoT- and CPS-based industrial systems

In recent years, skeleton-based human action recognition has attracted substantial attentions. However, owing to the complexity and nonlinearity of human action data, it is still a challenging task to precisely represent skeleton features. Motivated by the effectiveness of Lie Group skeletal representations in extracting human action features and the powerful capability of deep neural networks in feature learning and high-dimensional data processing, we proposed to combine Lie Group features and deep learning for human action recognition. Human skeleton information was firstly used to overcome the interference of external factors such as changes of lighting conditions and body shape. And then, Lie Group was applied to naturally represent the complex and diverse action data. Finally, we took use of convolutional neural networks to learn and classify the Lie Group features. Experiments were performed on three public datasets, and the experimental results show that our methods can achieve higher average recognition accuracy of 93.00% on Florence3D-Action, 93.68% on MSR Action Pairs, and 97.96% on UT Kinect-Action, which outperforms many of the state-of-the-art methods.

Human action recognition using Lie Group features and convolutional neural networks

In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, health behavior, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical events of elderly and patient people is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient populations. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted. In the era of Internet of Medical Things (IoMT), healthcare monitoring has gained a vital role nowadays. Moreover, improving lifestyle, health behavior, and decreasing the chronic diseases are urgently required. However, tracking and monitoring critical events of elderly and patient people is a great challenge. Healthcare services for those people are crucial in order to achieve high safety consideration. Physical human activity recognition using wearable devices is used to monitor and recognize human activities for elderly and patient populations. The main aim of this review study is to highlight the human activity recognition chain, which includes, sensing technologies, preprocessing and segmentation, feature extractions methods, and classification techniques. Challenges and future trends are also highlighted.

/pdf/a-review-of-physical-human-activity-recognition-chain-using-4q4rqokrw2.pdf

A Review of Physical Human Activity Recognition Chain Using Sensors

Human action recognition has been widely used in various fields of computer vision, pattern recognition, and human–computer interaction and has attracted substantial attention. Combining deep learning and depth information, this paper proposed a method of human action recognition based on improved convolutional neural networks (CNN). First, we use the depth motion maps to extract the depth sequence features and obtain three projected maps corresponding to front, side, and the top views. On this basis, an improved CNN is constructed to realize the recognition of human action, which uses three-dimensional (3-D) input and two-dimensional process identification to speed up the computation and reduce the complexity of recognition process. We evaluate our approach on two public 3-D action datasets: MSR Action3D dataset and UT-Kinect dataset, and our private CTP Action3D dataset built using Kinect to collect data. The experimental results show that the proposed methods of human action recognition achieve higher average recognition rate of 91.3% on MSR Action3D dataset, 97.98% on UT-Kinect dataset, and the average recognition rate is 93.8% on our CTP Action3D dataset. Furthermore, the trained model on one depth video sequence dataset can be easily generalized to different datasets without changing network parameters.

Robust human action recognition based on depth motion maps and improved convolutional neural network

Text multi-label learning method based on label-aware attention and semantic dependency

Event extraction is one of the key tasks in information extraction tasks, and it has been widely used in various fields in recent years. In the field of disciplinary inspection and supervision, disciplinary inspection case text has the characteristics of multiple types, multiple professional terms, and strong correlation between event types and event arguments. In the face of huge data, relying on manual analysis by manpower has seriously affected the efficiency of disciplinary inspection. However, there is no corpus in the field of discipline inspection currently available. This article uses BIO annotation to construct a corpus of discipline inspection to lay the foundation for subsequent work. Proposed BERT-BiGRU-CRF event joint extraction model. Use the BERT model to train the discipline inspection corpus, and Combine BiGRU network and CRF network to realize event type recognition and argument extraction. The experimental results show that the model can effectively extract event information in the discipline inspection field. At the same time, in order to better facilitate the work of disciplinary inspection personnel, a disciplinary inspection and supervision event extraction system is constructed to systematically extract all event information contained in the case.

A Method of Extracting Discipline Inspection Cases Based on Deep Learning

Owing to the fragile to external environment, current human activity recognition is still challenging in the area of computer vision. In particular, the recognition of subtle actions is a difficult task to distinguish from each other. In this paper, we proposed the methods for human action recognition based on improved sparse Gaussian process latent variable model (GPLVM) and hidden conditional random field (HCRF). We first fused the skeletal information and the motion characteristics of human body to extract human action features from a set of action sequences. And then, we proposed an improved sparse GPLVM algorithm for feature dimensionality reduction to satisfy the visualization and computation complexity for human actions recognition. Through sparse approximation with information vector machine and the dynamic process of human actions, the improved sparse GPLVM allows the action features to be represented by the low dimensional trajectory in manifold space. Furthermore, HCRF was applied to recognize the characteristics of human actions derived from manifold spaces and finally to classify human actions. The proposed methods are tested and evaluated on public action database. The experimental results show that our methods can achieve better performance of feature dimensionality reduction and visualization and obtain the average recognition accuracy of 93.68%.

Human Action Recognition Using Improved Sparse Gaussian Process Latent Variable Model and Hidden Conditional Random Filed

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Robust human action recognition based on depth motion maps and improved convolutional neural network

Text multi-label learning method based on label-aware attention and semantic dependency

A Method of Extracting Discipline Inspection Cases Based on Deep Learning

Human Action Recognition Using Improved Sparse Gaussian Process Latent Variable Model and Hidden Conditional Random Filed