A novel crossings-based segmentation approach for gesture recognition

Abstract

Human activity recognition (HAR) has mainly been directed to the recognition of static or quasi-periodic activities like sitting, walking or running, typically for fitness applications. However, activities like eating or drinking are neither static nor quasi-periodic. Instead, they are composed of sparsely occurring motions or gestures in continuous data streams. This paper presents a novel adaptive segmentation technique based on crosses of moving averages to identify potential eating or drinking gestures from accelerometer data. The novel crossings-based segmentation approach proposed is able to identify all eating and drinking gestures from continuous accelerometer data including different activities. A posteriori, potential gestures are classified as food or drink intake gestures using a combination of Dynamic Time Warping (DTW) as signal similarity measure and a k-Nearest Neighbours (KNN) classifier. An outstanding classification rate of 100% has been achieved.