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- W3119690468 abstract "AbstractIn this paper, Long Short Term Memory (LSTM) deep learning model is used to identify human activities using sequential data obtained from cameras, wearable sensors, or other modalities. The proposed method recognizes human activities by optimizing hyper-parameters for the chosen deep learning model. The proposed approach is validated using public domain UTD MHAD dataset and found to be more accurate outperforming the state-of-the-art in terms of accuracy of activity recognition. Datasets categorized as the depth, skeleton, and inertial modalities have been analyzed for all available features. The dependency of the proposed deep learning model on the hyper-parameters is investigated extensively and discussed in detail. Experimental results depicted in this paper demonstrate the fact that the proposed method is a better choice for indoor activity recognition.KeywordsIndoor human activity recognition (IHAR)Multimodal datasetsKinect V1LSTMHyper-parameters" @default.
- W3119690468 created "2021-01-18" @default.
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- W3119690468 date "2021-01-01" @default.
- W3119690468 modified "2023-10-14" @default.
- W3119690468 title "Human Activity Recognition Using Positioning Sensor and Deep Learning Technique" @default.
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- W3119690468 doi "https://doi.org/10.1007/978-981-15-8391-9_34" @default.
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