On-board processing of acceleration data for real-time activity classification

The assessment of a person's ability to consistently perform the fundamental activities of daily living is essential in monitoring the patient's progress and measuring the success of treatment. Therefore, many researchers have been interested in this issue and have proposed various monitoring systems based on accelerometer sensors. However, few systems focus on energy consumption of sensor devices. In this paper, we introduce an energy-efficient physical activity monitoring system using a wearable wireless sensor. The proposed system is capable of monitoring most daily activities of the human body: standing, sitting, walking, lying, running, and so on. To reduce energy consumption and prolong the lifetime of the system, we have focused on minimizing the total energy spent for wireless data exchange by manipulating real-time acceleration data on the sensor platform. Furthermore, one of our key contributions is that all functionalities including data processing, activity classification, wireless communication, and storing classified activities were achieved in a single sensor node without compromising the accuracy of activity classification. Our experimental results show that the accuracy of our classification system is over 95%.