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Understanding movement variability of simplistic gestures using an inertial sensor

Published: 20 June 2016 Publication History

Abstract

We present a preliminary experiment to understand the variability of six simple movements. Six participants, wearing inertial measurement units on their wrist, performed six actions. The data collected were analysed using time-delay embedding theorem, PCA and percentage of cumulative energy to characterise variability in these movements. Of these movements, circular and 8-shape movements show a constant trend between participants; however, such a trend is not evident for static, horizontal, vertical and diagonal movements. Such analysis can be useful in determining different states of interactions with the display of user's behavior (enthusiasm, boredom, tiredness or confusion) over the course of training, practice or rehabilitation.

References

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Jordan Frank, Shie Mannor and Doina Precup. 2010. Activity and Gait Recognition with Time-Delay Embeddings. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 1581--1586.
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Nils Y Hammerla, Thomas Plötz, Peter Andras and Patrick Olivier. 2011. Assessing motor performance with pca. In Proceedings of the International Workshop on Frontiers in Activity Recognition using Pervasive Sensing, 18--23.
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Karl M. Newell and Daniel M. Corcos. 1993. Variability and motor control. United States of America: Human Kinetics Publishers.
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Albert Samà, Francisco J. Ruiz, Núria Agell, Carlos Pérez-López, Andreu Català and Joan Cabestany. 2013. Gait identification by means of box approximation geometry of reconstructed attractors in latent space. In Neurocomputing, 121, 79--88.
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Lucas C. Uzal, Guillermo L. Grinblat, and Pablo F. Verdes. 2011. Optimal reconstruction of dynamical systems: A noise amplification approach. In Phyical Review, 84, 016223.
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Ionuţ-Alexandru Zaiţi, Ştefan-Gheorghe Pentiuc, Radu-Daniel Vatavu. 2015. On free-hand TV control: experimental results on user-elicited gestures with Leap Motion. In Personal and Ubiquitous Computing, 19, 821--838.

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  • (2025)In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against VariabilitySensors10.3390/s2502043025:2(430)Online publication date: 13-Jan-2025

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PerDis '16: Proceedings of the 5th ACM International Symposium on Pervasive Displays
June 2016
266 pages
ISBN:9781450343664
DOI:10.1145/2914920
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 June 2016

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Author Tags

  1. activity recognition
  2. dimensionality reduction
  3. phase space reconstruction
  4. time-delay embedding

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PerDis '16

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PerDis '16 Paper Acceptance Rate 28 of 47 submissions, 60%;
Overall Acceptance Rate 213 of 384 submissions, 55%

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View all
  • (2025)In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against VariabilitySensors10.3390/s2502043025:2(430)Online publication date: 13-Jan-2025

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