Vision-Based Fall Detection Using ST-GCN

Keskes, Oussema; Noumeir, Rita

doi:10.1109/access.2021.3058219

Cited by 52 publications

(37 citation statements)

References 33 publications

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“…TST V2 [11] Fall Detection The fall detection dataset contains depth frames, skeleton joints, acceleration [121] [url]…”

Section: Graph/network Structured Datamentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

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The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; • Computing methodologies → Neural networks.

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“…TST V2 [11] Fall Detection The fall detection dataset contains depth frames, skeleton joints, acceleration [121] [url]…”

Section: Graph/network Structured Datamentioning

confidence: 99%

Graph Neural Networks in IoT: A Survey

Dong¹,

Tang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…There have been multiple action recognition systems using the ST-GCN architecture [5], [16]- [18]. Zheng et al [12] extracted the skeleton from the UCF-101 dataset in a similar manner as Yan et.…”

Section: A Partitioning Strategiesmentioning

confidence: 99%

“…The skeleton information is robust to changes in the illumination of the environment where the action takes place. Also, it is robust to changes in the background [5]. Moreover, the computational cost for training is considerably reduced for skeleton data consisting of only sets of joint cartesian coordinates.…”

Section: Introductionmentioning

confidence: 99%

Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion

Alsawadi¹,

Rio²

2021

Preprint

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There has been a dramatic increase in the volume of videos and their related content uploaded to the internet. Accordingly, the need for efficient algorithms to analyse this vast amount of data has attracted significant research interest. An action recognition system based upon human body motions has been proven to interpret videos' contents accurately. This work aims to recognize activities of daily living using the ST-GCN model, providing a comparison between four different partitioning strategies: spatial configuration partitioning, full distance split, connection split, and index split. To achieve this aim, we present the first implementation of the ST-GCN framework upon the HMDB-51 dataset. We have achieved 48.88% top-1 accuracy by using the connection split partitioning approach. Through experimental simulation, we show that our proposals have achieved the highest accuracy performance on the UCF-101 dataset using the ST-GCN framework than the state-of-the-art approach. Finally, accuracy of 73.25% top-1 is achieved by using the index split partitioning strategy.

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“…An alternative to avoid these limitations is using temporal features, i.e., ST-GCN, which can provide strong feature representation based on ADLs [41]. The use of the body's skeleton information for fall detection problems was tested in different works [42]- [45]. The ST-GCN model uses RGB or RGB-D videos as input, along with a skeleton estimation, and it can generate a robust spatial-temporal representation.…”

Section: Introductionmentioning

confidence: 99%

A Framework for Anomaly Identification Applied on Fall Detection

et al. 2021

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Automatic systems to monitor people and subsequently improve people's lives have been emerging in the last few years, and currently, they are capable of identifying many activities of daily living (ADLs). An important field of research in this context is the monitoring of health risks and the identification of falls. It is estimated that every year, one in three persons older than 65 years will fall, and fall events are associated with high mortality rates among the elderly. We propose an anomaly identification framework to detect falls, which incorporates a spatial-temporal convolutional graph network (ST-GCN) as a feature extractor and uses an encoder process to reconstruct ADLs and identify falls as anomalies. As the publicly available fall datasets are few and generally unbalanced, training a reliable model using approaches that need explicit labeling is challenging. Thus, a focus on learning without external supervision is desirable. Treating a fall as an exception of ADLs allows us to recognize falls as anomalies without explicit labels. Given its modular architecture, our framework can robustly represent visual information and use the encoder's reconstruction error to identify falls as anomalies. We assess our framework's ability to recognize falls by training it with only ADLs. We perform three types of experiments: single dataset training and evaluation that consists of separate 90% of the data to train the model 5% to adjust the model, and the rest to the test. A joint dataset experiment, where we combine two datasets to increase the number of samples our model is trained on, and a cross-dataset evaluation, where we train on one dataset and evaluate using another one. Besides presenting state-of-the-art results on our experiments, particularly on the cross-dataset one, the model also presents a low number of false events, which makes it an ideal candidate for real-world application.

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Vision-Based Fall Detection Using ST-GCN

Cited by 52 publications

References 33 publications

Graph Neural Networks in IoT: A Survey

Graph Neural Networks in IoT: A Survey

Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recogntion

A Framework for Anomaly Identification Applied on Fall Detection

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