D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation

Mourchid, Youssef; Slama, Rim

doi:10.1016/j.compbiomed.2023.107420

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Figure 8 shows the comparison of using different deep learning models (GRU, LSTM, Conv1D) when predicting the jacking forces in this weathered phyllite. It is clearly shown that the GRU model outperformed the other deep learning models with an 𝑅 of 0.82, showing that the model was able to handle limited data [30,60,61]. Figure 9 shows the comparison of the jacking force (JF) prediction performance of GRU with attention and without attention.…”

Section: Feature Visualisation Of Operation Parameters Through Attent...mentioning

confidence: 98%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

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Pipe jacking is a trenchless technology used to install buried pipelines, such as sewer lines in wastewater management systems. Existing mechanistic approaches based on geomaterial strength parameters (i.e., friction angle and apparent cohesion) can provide an estimation of the potential jacking forces during construction. However, extracting intact rock cores for strength characterisation is challenging when dealing with highly weathered ‘soft rocks’ which exhibit RQD values of zero. Such was the case for a pipe jacking drive traversing the highly weathered lithology underlying Kuching City, Malaysia. Furthermore, mechanistic approaches face limitations during construction when jacking forces are dependent on operation parameters, such as jacking speed and lubrication. To address these knowledge gaps, the primary objectives of this study are the development of rock strength parameters based on in situ pressuremeter testing for the purpose of estimating jacking forces. Furthermore, this study investigates the influence of various pipe jacking operation parameters, with a particular focus on their impact on jacking forces in weathered ‘soft rocks’. To achieve this, a novel deep learning model with an attention mechanism is introduced. The proposed methods of rock strength parameters derived from pressuremeter testing and the utilisation of deep learning will help to provide insights into the key factors affecting the development of jacking forces. This paper successfully shows the use of in situ pressuremeter testing in developing Mohr–Coulomb (MC) parameters directly from the site. In addition, the developed deep learning model with an attention mechanism successfully highlights the significance of pipe jacking operation parameters with an accuracy of 88% in predicting the jacking forces.

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Section: Feature Visualisation Of Operation Parameters Through Attent...mentioning

confidence: 98%

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Yeo,

Phangkawira,

Kueh

et al. 2024

Geosciences

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Rehabilitation exercise quality assessment through supervised contrastive learning with hard and soft negatives

Karlov,

Abedi,

Khan

2024

Med Biol Eng Comput

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LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

Sardari,

Sharifzadeh,

Daneshkhah

et al. 2024

Computers in Biology and Medicine

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D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation

Cited by 4 publications

References 32 publications

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Rehabilitation exercise quality assessment through supervised contrastive learning with hard and soft negatives

LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment

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