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- W4321505630 abstract "Human action recognition is a concept that involves acquiring information based on the sequence of movements by the target. This recognition model is used to recognise elderly people's actions to monitor their anomalies and provide appropriate guidance as soon as possible to avoid further disaster. This algorithm involves refining HAR and provides precise decisions to predict any emergencies while handling elderly people. This algorithm uses Dynamic Time Warping as a loss function ensembled with mean absolute error in CNN LSTM neural network. DTW is used in the loss function to get a relationship between two sequences of actions considered as waves. This results in performing better at any speed of action as long as the sequence of movements for action remains the same. The variations in the change of the sequence of movements for action are further countered by mean absolute error to perform better in any variation of an action. Thus, it provides an accuracy of 75% for the taken dataset which is way more than the accuracy produced by DTW or MAE as a separate loss function." @default.
- W4321505630 created "2023-02-23" @default.
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- W4321505630 date "2023-01-01" @default.
- W4321505630 modified "2023-10-16" @default.
- W4321505630 title "Enhanced Human Action Recognition with Ensembled DTW Loss Function in CNN LSTM Architecture" @default.
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- W4321505630 doi "https://doi.org/10.1007/978-981-19-7874-6_36" @default.
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