2022
DOI: 10.3390/app12189305
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Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

Abstract: Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these device… Show more

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Cited by 7 publications
(1 citation statement)
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“…In another study [33], the authors explored environmental contexts, such as noise level and illumination, to support raw sensors data using a hybrid CNN-LSTM model. The proposed approach performed fusion of rich contextual data with low-level sensor data to enhance the generalization and recognition accuracy.…”
Section: Hybrid DL Models and Federated Learningmentioning
confidence: 99%
“…In another study [33], the authors explored environmental contexts, such as noise level and illumination, to support raw sensors data using a hybrid CNN-LSTM model. The proposed approach performed fusion of rich contextual data with low-level sensor data to enhance the generalization and recognition accuracy.…”
Section: Hybrid DL Models and Federated Learningmentioning
confidence: 99%