Human Activity Recognition using LSTM with depth data
Keywords:
HAR, deep learning, CNN, neural networkAbstract
For many academics, HAR is a hot topic. They can do this with ease because to a number of cutting-edge technologies, including deep learning, which is useful in a number of contexts. While most of the current body of work has focused on wearable sensor data, it is not always practical to get such data. Publicly accessible video datasets are mined for human activity detection in the proposed study using deep learning techniques including CNNand long short-term memory. CNN extracts relevant characteristics from input data, whereas LSTM eliminates and rejects superfluous data to increase performance. The confusion matrix's precision and recall are used to evaluate the suggested technique. Accuracy is high across the board, as shown by the fact that the diagonals of the confusion matrices for all actions are near to 1.
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