A Lightweight Model for Human Activity Recognition Based on Two-Level Classifier and Compact CNN Model

Coelho, Yves Luduvico; Nguyen, Binh P.; Santos, F. A.; Krishnan, Sridhar; Bastos-Filho, Teodiano Freire

doi:10.1007/978-3-030-70601-2_276

Cited by 2 publications

(1 citation statement)

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“…Lightweight can be achieved by quantifying the network model. For example, the article [25] quantifies the convolutional neural network to make it run on portable devices. In the article [26], the sparse matrix operation is accelerated by compressing data and FPGA hardware equipment, which not only optimizes the calculated data from the perspective of quantification, but also greatly reduces the number of instructions that are not directly related to the operation in the general processor operation through hardware acceleration.…”

Section: Introductionmentioning

confidence: 99%

Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification

2022

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With the development of deep learning and graph deep learning, the network structure is more and more complex, and the parameters in the network model and the computing resources and storage resources required are increasing. The lightweight design and optimization of the network structure is conducive to reducing the required computing resources and storage resources, reducing the requirements of the network model on the computing environment, increasing its scope of application, reducing the consumption of energy in computing, and is conducive to environmental protection. The contribution of this paper is that Geometry-V-Sub is a graph learning structure based on spatial geometry, which can greatly reduce the parameter requirements and only lose a little accuracy. The number of parameters is only 13.05–16.26% of baseline model, and the accuracy of Cora, Citeseer and PubMed is max to 80.4%, 68% and 81.8%, respectively. When the number of parameters is only 12.01% of baseline model, F1 score is max to 98.4.

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Section: Introductionmentioning

confidence: 99%

Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification

2022

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Accelerometer‐based heart rate adjustment for ambulatory stress research

van de Ven,

Gevonden,

Noordzij

et al. 2024

Psychophysiology

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Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes.

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A Lightweight Model for Human Activity Recognition Based on Two-Level Classifier and Compact CNN Model

Cited by 2 publications

References 20 publications

Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification

Geometry-V-Sub: An Efficient Graph Attention Network Struct Based Model for Node Classification

Accelerometer‐based heart rate adjustment for ambulatory stress research

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