Towards the Run and Walk Activity Classification through Step Detection

Towards the Run and Walk Activity Classification through Step Detection – An Android Application Melis Oner, Jeffry A. Pulcifer-Stump, Patrick Seeling, Senior Member, IEEE, and Tolga Kaya, Member, IEEE

Abstract— Falling is one of the most common accidents with potentially irreversible consequences, especially considering special groups, such as the elderly or disabled. One approach to solve this issue would be an early detection of the falling event. Towards reaching the goal of early fall detection, we have worked on distinguishing and monitoring some basic human activities such as walking and running. Since we plan to implement the system mostly for seniors and the disabled, simplicity of the usage becomes very important. We have successfully implemented an algorithm that would not require the acceleration sensor to be fixed in a specific position (the smart phone itself in our application), whereas most of the previous research dictates the sensor to be fixed in a certain direction. This algorithm reviews data from the accelerometer to determine if a user has taken a step or not and keeps track of the total amount of steps. After testing, the algorithm was more accurate than a commercial pedometer in terms of comparing outputs to the actual number of steps taken by the user. Index Terms— Activity classification, Android, Fall Detection, Mobile Applications.

I. INTRODUCTION The world population is experiencing an enormous growth and studies predict that the elderly population will make up the majority of that population in the near future [1]. As the health insurance costs for the elderly are significantly higher than for younger generations, the increase of the number of senior members of societies worldwide will have a negative impact on any nation’s budget, unless preventive healthcare systems are implemented [2]. Even though it might not be possible to avoid some health problems due to aging, it is quite possible to reduce the implications of predictable M. Oner is with the School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA (e-mail: [email protected]). J. A. Pulcifer-Stump is with the Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA (e-mail: [email protected]). P. Seeling is with the Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA (e-mail: [email protected]). T. Kaya (corresponding author) is with the School of Engineering and Technology, Central Michigan University, Mount Pleasant, MI 48859, USA (e-mail: [email protected], phone: 989-774-1494).

accidents, such as falling. Continuous monitoring of senior citizens in their daily activities to prevent or alleviate the impact of these types of events would only be feasible with personal medical or home aides – an approach that is clearly not practical. In turn, home accidents, in general, are one of the most seen accidents, which primarily include falls [3]. Therefore, significant recent efforts were focused on detecting a fall event or to classify a subject’s activities over time. Most commonly, several 3-axis accelerometers are used in different locations of the subject such as ear, chest, arm, wrist, waist, knee, and ankle [4]. While gyroscopes can also be used to detect fall events [5], accelerometers and gyroscopes can be combined to gather more accurate data [6, 7]. It is obvious that the detection accuracy would significantly be enhanced by using multiple physically widespread sensors; however, it is impractical and uncomfortable for subjects to wear many sensors on specific locations. We propose to use a mobile smart phone to detect a fall event regardless of the phone’s position or orientation. Smart phones include various sensors such as gyroscopes, accelerometers, proximity sensors, see, e.g., [8-10], and have become affordable and ubiquitous. User interfaces that are incorporating assistive technologies make these devices usable for all population groups. In turn, using mobile applications for patient monitoring can alleviate some of the cost-related issues and improve both a patient and a caretaker’s quality of life. In this paper, we present our initial work on a pedometer mobile application component that is coupled with an e-mail component that can be used to notify medical assistants or family members. The developed algorithm counts the steps and identifies the type of activity with a higher accuracy than many of the applications that are commercially available. The remainder of this paper is structured as follows. In Section II, we describe the developed pedometer algorithm and present performance results in Section III. We conclude and describe future works in Section IV.

II. ALGORITHM AND CODE else{ //IF current value is within the range THEN if (data[n]data[n+1]){ //IF current value is not a peak THEN if(data[n]