Human Activity Recognition Using Smartphone and Smartwatch

Human activity recognition has many applications in medicine System of research and surveys among people. The recognition of human action has sparked great interest in both industry and academia in recent years. due to the widespread use of sensors such as accelerometers and gyroscopes in products such as smartphones and smartwatches. Activity recognition is currently used in various areas where it is valuable Information about the person's functional abilities and lifestyle is needed. We can do this using multivariate analysis of covariance (MANCOVA). found a statistically significant difference between the data generated by the sensors integrated into smartphones and smartwatches. This is how we show that smartphones and smartwatches Due to where it is worn, the does not record data in the same way. We have implemented several neurons network architectures that classify 15 different manual and non-manual activities. These models include long-term memory (LSTM), convolution neuron Network (CNN) and Convolutional LSTM (ConvLSTM). The models developed worked better with the watch Data from accelerometer. We also saw that the classification accuracy was achieved with the convolutional input (CNN and ConvLSTM) is found to outperform the full LSTM classifier. Additionally, the watch accelerometer CNN model was able to better classify non-wrist-facing watches. tasks compared to manually executed tasks