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A Novel Crossings-Based Segmentation Approach for Gesture Recognition

  • Dario Ortega Anderez
  • Ahmad Lotfi
  • Caroline Langensiepen
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

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.

Keywords

Adaptive signal segmentation Gesture recognition Wearable sensors 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Dario Ortega Anderez
    • 1
  • Ahmad Lotfi
    • 1
  • Caroline Langensiepen
    • 1
  1. 1.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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