A Framework for Daily Living Activity Recognition using Fusion of Smartphone Inertial Sensors Data

Recent years have seen rapid advancements in the human activity recognition field using data from smart sensor devices. A wide variety of real-world applications can be found in different domains, particularly health and security. Smartphones are common devices that let people do a wide range of everyday tasks anytime, anywhere. The sensors and networking capabilities found in modern smartphones enable context awareness for a wide range of applications. This research mainly focuses on recognizing human activities in the wild for which we selected an in-the-wild extra-sensory dataset. Six human activities i.e., lying down, sitting, standing, running, walking, and bicycling are selected. Time domain features are extracted and human activity recognition is performed using three different machine learning classifiers i.e., random forest, k-nearest neighbors, and decision trees. The proposed human activity recognition scheme resulted in the highest classification accuracy of 89.98%, using the random forest classifier. Our proposed scheme outperforms the state-of-the-art human activity recognition schemes in the wild.