Abstract
This paper uses raw sensor data to develop and train the networks for human activity recognition in a variety of applications, ranging from tracking their fitness and the safety monitoring. The models may simply be expanded to be trained with additional data sources for greater accuracy or classification extension for different prediction classes. This research delves into WISDM’s accessible dataset as well as the distinct characteristics of each class for various axes. Furthermore, for the application of human activity identification, the design of a long short-term memory (LSTM) architectural model is defined. In the first 500 epochs of training, an accuracy of over 96 percent and a loss of less than 30% have been achieved.
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Kallepally, P., Rajesh, M. (2023). LSTM-Based Deep Learning Architecture for Recognition of Human Activities. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_14
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DOI: https://doi.org/10.1007/978-981-19-5443-6_14
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