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  1. Proceedings of the 3rd International Workshop on Sensor-based Activity Recognition and Interaction (iWOAR '16)
  2. Coping with variability in motion based activity recognition
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SmartMove: a smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep
Coping with variability in motion based activity recognition
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okinesio: the development of open hardware for quantified self
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Tool support for the online annotation of sensor data

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Coping with variability in motion based activity recognition

Content Provider ACM Digital Library
Author Kreil, Matthias Lukowicz, Paul Sick, Bernhard
Abstract A key issue in the automatic recognition of human activities with body worn sensors is the variability of human motions and the huge space of possibilities for executing even fairly simple actions. In this article we introduce a new algorithm to address this issue. The core idea is that often even highly variable actions include short more or less invariant parts which are due to hard physical constraints. The aim is to develop a method that can identify such invariants and use them to improve the classification of the respective activities. The method is meant to be combined with existing classification approaches in an ensemble like fashion, being applied only to the classes for which appropriate invariants can be found and leaving the other classes to be handled by classical methods. We compare our method's results to prior publications on two well known data sets and are able to improve the classification in 5 of 23 respectively 4 of 19 classes, in same cases by a large margin (best case is from 27% to 76% in the first and from 50% to 64% in the second). In each data set there is only one class for which we make the recognition worse and in both cases it is one with poor results to start with and a relatively small decrease (from 54% to 45% in the first and from 65% to 62% in the second). The results are achieved for an user independent case.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781450342452
DOI 10.1145/2948963.2948967
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2016-06-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Imu Segmentation Activity recognition Wearable computing Motif detection
Content Type Text
Resource Type Article
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