Improving Human Activity Recognition using ML and Wearable Sensors

The Internet of Things (IoT) generates massive amounts of data everywhere through sensors of every kind which are disseminated in a variety of objects. This data contains incredibly valuable information useful for multiple applications. Knowing the context in which it was generated is extremely important and constitutes one of the first steps in extracting the knowledge it contains. Thereby, Context-Aware Learning (CAL) has become an important area of research as machine learning (ML) is a fast and ever-evolving technology. Wearable devices, ranging from accelerometers (ACC), frequently used, to magnetic field sensors, are used to monitor and recognize human activities (HA). Beyond ML Algorithms (MLA), accurate Human Activities Recognition (HAR) or context identification, depends not only on the kinds of sensors used but also on their location. In this paper, we study the impact of three types of sensors: ACC, gyroscope (GYR), and magnetometer (MAG); and their locations on the performance of MLA for HAR. Our results show that magnetic field sensors, less frequently used in the literature, placed at a specific location, provide the best performance in terms of HAR. Using a publicly available dataset, PAMAP2, we implement and evaluate the performance of HAR using five MLA: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QLA), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). Our results show that the success rate of these algorithms is 98.3%, 90.4%, 97.6%, 99.9%, and 100% respectively, which exceeds the results obtained in a previous work based on the same dataset.