HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data

Mohamed, Abduallah; Lejarza, Fernando; Cahail, Stephanie; Claudel, Christian; Thomaz, Edison

doi:10.1109/percomworkshops53856.2022.9767342

Cited by 4 publications

(3 citation statements)

References 28 publications

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“…Several prominent architectures have emerged, including convolutional neural networks (CNNs), long shortterm memory (LSTM), and attention mechanisms. [13][14][15][16][17][18][19][20] have been extensively explored and recognized for various applications. CNN-based activity recognition enables fast and efficient predictions [21,22].…”

Section: Background On Activity Recognition Methods and Datasetsmentioning

confidence: 99%

“…However, there is a potential for missing annotations due to human error or perceived irrelevance. Networks like HAR-GCCN [13], a deep graph CNN model, propose leveraging the inherent chronology of human behavior to learn unknown labels. For example, bathing is expected to follow physical exercise, and this implicit chronology can be utilized with data from chronologically adjacent sensors to learn missing labels.…”

Section: Cnn-based Methodsmentioning

confidence: 99%

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Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

Cavalcante,

Kunst,

Chaves

et al. 2023

Sensors

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The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals’ daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.

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Section: Background On Activity Recognition Methods and Datasetsmentioning

confidence: 99%

Section: Cnn-based Methodsmentioning

confidence: 99%

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

Cavalcante,

Kunst,

Chaves

et al. 2023

Sensors

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show abstract

“…Mondal et al [20] introduced an end-to-end fast graph neural network capable of capturing information not only from individual sensor channels but also from the relationships with other samples in the form of an undirected graph structure. Mohamed et al [21] presented HAR-GCCN, a deep graph convolutional neural network model that simultaneously incorporates correlations between different sensor channels. They also devised a novel training strategy that leverages known activity labels to predict missing activity labels through the HAR-GCCN model.…”

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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HAR-GCNN: Deep Graph CNNs for Human Activity Recognition From Highly Unlabeled Mobile Sensor Data

Cited by 4 publications

References 28 publications

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data

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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