Context Aware Reminder System : Activity Recognition Using Smartphone Accelerometer and Gyroscope Sensors Supporting Context-Based Reminder Systems

Abstract: Context. Reminder system offers flexibility in daily life activities and assists to be independent. The reminder system not only helps reminding daily life activities, but also serves to a great extent for the people who deal with health care issues. For example, a health supervisor who monitors people with different health related problems like people with disabilities or mild dementia. Traditional reminders which are based on a set of defined activities are not enough to address the necessity in a wider context. To make the reminder more flexible, the user’s current activities or contexts are needed to be considered. To recognize user’s current activity, different types of sensors can be used. These sensors are available in Smartphone which can assist in building a more contextual reminder system. Objectives. To make a reminder context based, it is important to identify the context and also user’s activities are needed to be recognized in a particular moment. Keeping this notion in mind, this research aims to understand the relevant context and activities, identify an effective way to recognize user’s three different activities (drinking, walking and jogging) using Smartphone sensors (accelerometer and gyroscope) and propose a model to use the properties of the identification of the activity recognition. Methods. This research combined a survey and interview with an exploratory Smartphone sensor experiment to recognize user’s activity. An online survey was conducted with 29 participants and interviews were held in cooperation with the Karlskrona Municipality. Four elderly people participated in the interview. For the experiment, three different user activity data were collected using Smartphone sensors and analyzed to identify the pattern for different activities. Moreover, a model is proposed to exploit the properties of the activity pattern. The performance of the proposed model was evaluated using machine learning tool, WEKA. Results. Survey and interviews helped to understand the important activities of daily living which can be considered to design the reminder system, how and when it should be used. For instance, most of the participants in the survey are used to using some sort of reminder system, most of them use a Smartphone, and one of the most important tasks they forget is to take their medicine. These findings helped in experiment. However, from the experiment, different patterns have been observed for three different activities. For walking and jogging, the pattern is discrete. On the other hand, for drinking activity, the pattern is complex and sometimes can overlap with other activities or can get noisy. Conclusions. Survey, interviews and the background study provided a set of evidences fostering reminder system based on users’ activity is essential in daily life. A large number of Smartphone users promoted this research to select a Smartphone based on sensors to identify users’ activity which aims to develop an activity based reminder system. The study was to identify the data pattern by applying some simple mathematical calculations in recorded Smartphone sensors (accelerometer and gyroscope) data. The approach evaluated with 99% accuracy in the experimental data. However, the study concluded by proposing a model to use the properties of the identification of the activities and developing a prototype of a reminder system. This study performed preliminary tests on the model, but there is a need for further empirical validation and verification of the model.