User Locale and Intent Prediction Using Sensors from Smartphone

Smartphone data is becoming increasingly important today as many people own and frequently use smartphones. The smartphone-based data can provide better insight into the user's behavior by providing knowledge related to their daily activities. We can use it in behavior or intent prediction, location prediction, and recommendation of places and activities. We developed an Android application to gather data from the wireless sensors and GPS of the smartphone. Deep learning technology on this complex and diverse behavioral data can help extract useful information. Our research assessed the accuracy and efficiency of four deep learning architectures based on convolutional neural networks and their variants and LSTM. Our study presents a novel deep learning model that integrates residual networks and LSTM. The results of our experiments demonstrate that this model surpasses the traditional approach of combining convolutional networks with LSTM, achieving higher accuracy rates of 90.058% and 90.261% for predicting user locale and user intent, respectively. Overall, this study demonstrates the potential of smartphone-based wireless sensor data and location services for predicting user activity and location. It highlights the importance of utilizing deep learning techniques for effectively processing and analyzing this type of data, which is becoming increasingly prevalent today, and provides a better understanding of how to take advantage of the vast amount of smartphone data to make predictions about human behavior. This can be used in various application areas.