HGCNN: Deep Graph Convolutional Network for Sensor-Based Human Activity Recognition

Nian, Aixin; Zhu, Xianqiang; Xu, Xiang; Huang, Xueqin; Wang, Fei; Zhao, Yu

doi:10.1109/bigdia56350.2022.9874212

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The authors classified such graphs using TinyGraphHAR, a powerful GNN model for sensor-based HAR. Further approaches can be found in [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…This paper builds on graph neural networks (GNNs) [ 18 ] because they have previously shown promising results on link prediction tasks [ 18 , 19 , 20 ]. Additionally, GNNs are underrepresented in the HAR domain, with only a few existing papers on GNN-based HAR (e.g., [ 21 , 22 , 23 , 24 ]). This study offers an excellent opportunity to gain a deeper understanding into the application of GNNs within the HAR domain.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

Wieland,

Pankratius

2024

Sensors

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With the continuous development of new wearable devices, sensor-based human activity recognition is enjoying enormous popularity in research and industry. The signals from inertial sensors allow for the detection, classification, and analysis of human activities such as jogging, cycling, or swimming. However, human activity recognition is often limited to basic activities that occur in short, predetermined periods of time (sliding windows). Complex macro-activities, such as multi-step sports exercises or multi-step cooking recipes, are still only considered to a limited extent, while some works have investigated the classification of macro-activities, the automated understanding of how the underlying micro-activities interact remains an open challenge. This study addresses this gap through the application of graph link prediction, a well-known concept in graph theory and graph neural networks (GNNs). To this end, the presented approach transforms micro-activity sequences into micro-activity graphs that are then processed with a GNN. The evaluation on two derived real-world data sets shows that graph link prediction enables the accurate identification of interactions between micro-activities and the precise validation of composite macro-activities based on learned graph embeddings. Furthermore, this work shows that GNNs can benefit from positional encodings in sequence recognition tasks.

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“…The authors classified such graphs using TinyGraphHAR, a powerful GNN model for sensor-based HAR. Further approaches can be found in [ 22 , 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

Wieland,

Pankratius

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Additionally, they utilized a deep convolutional neural network to automatically extract discriminative features from the graph. Nian et al [23] modeled time-series data from sensors as fully connected subgraphs using a sliding window size. They employed spectral graph convolution to extract human motion information from these subgraphs.…”

Section: Related Workmentioning

confidence: 99%

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

Yang,

Liao,

Zhao

et al. 2024

Mathematics

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Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.

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A multi-graph convolutional network based wearable human activity recognition method using multi-sensors

Chen,

Luo,

Peng

et al. 2023

Appl Intell

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HGCNN: Deep Graph Convolutional Network for Sensor-Based Human Activity Recognition

Cited by 4 publications

References 11 publications

Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

Detection and Validation of Macro-Activities in Human Inertial Signals Using Graph Link Prediction

Domain Adaptation for Sensor-Based Human Activity Recognition with a Graph Convolutional Network

A multi-graph convolutional network based wearable human activity recognition method using multi-sensors

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