No abstract

Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including; classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components; a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.

The essence of context awareness has changed the revolution of ubiquitous computing, and the wireless sensor network technologies paved the way towards many applications. Activity recognition is a key component in identifying the context of a user for providing services based on the application. In this study, we propose a context management model that is based on activity recognition. The model is composed of four components: a set of sensors, a set of activities, a backend server with machine learning algorithms, and a GUI application for the interaction with the user. A prototype is developed to show the usability of the proposed model. As a pilot testing, only accelerometer data of an Android phone is used to identify the activities of daily living (ADLs): sitting, standing, walking, and jogging. A good accuracy of results that is about 96% on average is achieved in all activities.

Abstract-Population aging problem has become a severe challenge worldwide and will become even severer in the near future. Meanwhile the technology of smart phones and various sensors is developing by leaps and bounds in the last few years. In order to help people who have difficulty in living independently, especially the senior citizens and facilitate efficient use of these sensors, it is worthwhile to explore the possibility of making use of smart phones to manage these sensors and provide valuable information. In this paper, a context engine run on the Android platform is introduced. The context engine targets healthcare usage, collecting healthcare related information from three kinds of sensors: ambient sensors, wearable sensors, and embedded sensors, with the attention of providing real time reminders, sharing information to other applications and recording the user's daily life. Experiment result shows that average accuracy of reminders can reach 82% in user's daily life within the context engine. The results are expected to be improved with more sensors and more accurate sensors involved in the system. Compared to other similar work, this context engine focuses on the mobile platform that can cover most of user's time in daily time while maintaining portability.