Human Activity Recognition using Dynamic Time Warping

Abstract

Human activity recognition is now a well-known field of Human Computer Interaction (HCI) because its capability of providing personalized support using different applications. The recognition of human activities has become a task of high interest within the field, especially for medical, military, and security applications. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. In our work we will try to recognize human activity using wearable sensors. The works done before had the problem of using multimodal system with satisfactory results. Computation of the inputs to recognize activities are not simple. So, we designed a multi-modal system that will take accelerometer, gyroscope and ultrasonic sensor’s data as input and use optimized Dynamic Time Warping algorithm to classify data to recognize activity with satisfactory success rate.