A hybrid CNN and BLSTM network for human complex activity recognition with multi-feature fusion

2021 ◽  
Author(s):  
Ruohong Huan ◽  
Ziwei Zhan ◽  
Luoqi Ge ◽  
Kaikai Chi ◽  
Peng Chen ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Feature Fusion ◽  
Complex Activity ◽  
Human Complex

scholarly journals HuMAn: Complex Activity Recognition with Multi-Modal Multi-Positional Body Sensing

2019 ◽  
Vol 18 (4) ◽  
pp. 857-870 ◽  
Author(s):  
Pratool Bharti ◽  
Debraj De ◽  
Sriram Chellappan ◽  
Sajal K. Das
Keyword(s):  
Activity Recognition ◽  
Complex Activity ◽  
Human Complex

Human complex activity recognition with sensor data using multiple features

2021 ◽  
pp. 1-1
Author(s):  
Ruohong Huan ◽  
Chengxi Jiang ◽  
Luoqi Ge ◽  
Jia Shu ◽  
Ziwei Zhan ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Sensor Data ◽  
Complex Activity ◽  
Multiple Features ◽  
Human Complex

scholarly journals Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1685
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul
Keyword(s):  
Neural Networks ◽  
Activity Recognition ◽  
Human Activities ◽  
Recognition Performance ◽  
Confusion Matrix ◽  
Experimental Studies ◽  
Industrial Applications ◽  
Sensor Data ◽  
Complex Activity ◽  
Activity Data

Sensor-based human activity recognition (S-HAR) has become an important and high-impact topic of research within human-centered computing. In the last decade, successful applications of S-HAR have been presented through fruitful academic research and industrial applications, including for healthcare monitoring, smart home controlling, and daily sport tracking. However, the growing requirements of many current applications for recognizing complex human activities (CHA) have begun to attract the attention of the HAR research field when compared with simple human activities (SHA). S-HAR has shown that deep learning (DL), a type of machine learning based on complicated artificial neural networks, has a significant degree of recognition efficiency. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two different types of DL methods that have been successfully applied to the S-HAR challenge in recent years. In this paper, we focused on four RNN-based DL models (LSTMs, BiLSTMs, GRUs, and BiGRUs) that performed complex activity recognition tasks. The efficiency of four hybrid DL models that combine convolutional layers with the efficient RNN-based models was also studied. Experimental studies on the UTwente dataset demonstrated that the suggested hybrid RNN-based models achieved a high level of recognition performance along with a variety of performance indicators, including accuracy, F1-score, and confusion matrix. The experimental results show that the hybrid DL model called CNN-BiGRU outperformed the other DL models with a high accuracy of 98.89% when using only complex activity data. Moreover, the CNN-BiGRU model also achieved the highest recognition performance in other scenarios (99.44% by using only simple activity data and 98.78% with a combination of simple and complex activities).

Human interaction recognition method based on parallel multi-feature fusion network

2021 ◽  
Vol 25 (4) ◽  
pp. 809-823
Author(s):  
Qing Ye ◽  
Haoxin Zhong ◽  
Chang Qu ◽  
Yongmei Zhang
Keyword(s):  
Activity Recognition ◽  
Feature Fusion ◽  
Gaussian Model ◽  
Research Direction ◽  
Recognition Algorithm ◽  
Human Interaction ◽  
Amount Of Information ◽  
Interaction Recognition ◽  
Time Periods ◽  
Human Interaction Recognition

Human activity recognition is a key technology in intelligent video surveillance and an important research direction in the field of computer vision. However, the complexity of human interaction features and the differences in motion characteristics at different time periods have always existed. In this paper, a human interaction recognition algorithm based on parallel multi-feature fusion network is proposed. First of all, in view of the different amount of information provided by the different time periods of action, an improved time-phased video down sampling method based on Gaussian model is proposed. Second, the Inception module uses different scale convolution kernels for feature extraction. It can improve network performance and reduce the amount of network parameters at the same time. The ResNet module mitigates degradation problem due to increased depth of neural networks and achieves higher classification accuracy. The amount of information provided in the motion video in different stages of motion time is also different. Therefore, we combine the advantages of the Inception network and ResNet to extract feature information, and then we integrate the extracted features. After the extracted features are merged, the training is continued to realize parallel connection of the multi-feature neural network. In this paper, experiments are carried out on the UT dataset. Compared with the traditional activity recognition algorithm, this method can accomplish the recognition tasks of six kinds of interactive actions in a better way, and its accuracy rate reaches 88.9%.

scholarly journals A Deep Machine Learning Method for Concurrent and Interleaved Human Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5770 ◽  
Author(s):  
Keshav Thapa ◽  
Zubaer Md. Abdullah Al ◽  
Barsha Lamichhane ◽  
Sung-Hyun Yang
Keyword(s):  
Machine Learning ◽  
Random Field ◽  
Activity Recognition ◽  
Human Activity ◽  
Short Term Memory ◽  
Conditional Random Field ◽  
Human Activity Recognition ◽  
Second Phase ◽  
Complex Activity ◽  
Two Phase

Human activity recognition has become an important research topic within the field of pervasive computing, ambient assistive living (AAL), robotics, health-care monitoring, and many more. Techniques for recognizing simple and single activities are typical for now, but recognizing complex activities such as concurrent and interleaving activity is still a major challenging issue. In this paper, we propose a two-phase hybrid deep machine learning approach using bi-directional Long-Short Term Memory (BiLSTM) and Skip-Chain Conditional random field (SCCRF) to recognize the complex activity. BiLSTM is a sequential generative deep learning inherited from Recurrent Neural Network (RNN). SCCRFs is a distinctive feature of conditional random field (CRF) that can represent long term dependencies. In the first phase of the proposed approach, we recognized the concurrent activities using the BiLSTM technique, and in the second phase, SCCRF identifies the interleaved activity. Accuracy of the proposed framework against the counterpart state-of-art methods using the publicly available datasets in a smart home environment is analyzed. Our experiment’s result surpasses the previously proposed approaches with an average accuracy of more than 93%.

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