Activity recognition based on smartphone sensor data using shallow and deep learning techniques: A Comparative Study

Human activity recognition by the use of smartphone-equipped sensors has gotten a lot of interest in current times because of its large variety of applications.In this regard, this study provides a comprehensive comparative analysis of shallow and deep learning models for smartphone-based HARover high granular daily human activities. Moreover, A robust architecture for smartphone-based HAR is also provided, with stages ranging from data collection to data modelling. A total of seven best performing HAR models namely Decision Tree (DT), Random Forest(RF), DeepNeural Networks (DNN), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Gradient Boosting (GB) and Convolutional Neural Networks (CNN) are investigated. This research work is based on a real-world dataset of 95690 data samples collected from the smartphone sensors of 18 different subjects. The comparative study reveals that three models namely DNN, RF, and GB mostly dominated over the other models in terms of five performance metrics namely accuracy, recall, precision, F1-score, and AUC value.