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SCIENCE CHINA Information Sciences, Volume 60, Issue 7: 072104(2017) https://doi.org/10.1007/s11432-015-1014-7

Personalized gesture interactions for cyber-physical smart-home environments

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  • ReceivedMar 16, 2016
  • AcceptedApr 22, 2016
  • PublishedOct 13, 2016

Abstract

A gesture-based interaction system for smart homes is a part of a complex cyber-physical environment, for which researchers and developers need to address major challenges in providing personalized gesture interactions. However, current research efforts have not tackled the problem of personalized gesture recognition that often involves user identification. To address this problem, we propose in this work a new event-driven service-oriented framework called gesture services for cyber-physical environments (GS-CPE) that extends the architecture of our previous work gesture profile for web services (GPWS). To provide user identification functionality, GS-CPE introduces a two-phase cascading gesture password recognition algorithm for gesture-based user identification using a two-phase cascading classifier with the hidden Markov model and the Golden Section Search, which achieves an accuracy rate of 96.2% with a small training dataset. To support personalized gesture interaction, an enhanced version of the Dynamic Time Warping algorithm with multiple gestural input sources and dynamic template adaptation support is implemented. Our experimental results demonstrate the performance of the algorithm can achieve an average accuracy rate of 98.5% in practical scenarios. Comparison results reveal that GS-CPE has faster response time and higher accuracy rate than other gesture interaction systems designed for smart-home environments.


Acknowledgment

This work was supported by National High Technology Research and Development Program of China (Grant No. 2013AA01A210), State Key Laboratory of Software Development Environment (Grant No. SKLSDE-2013ZX-03), and National Natural Science Foundation of China (Grant No. 61532004). Vatavu also acknowledges support from the project “Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control” (Grant No. 671/09.04.2015), Sectorial Operational Program for Increase of the Economic Competitiveness, co-funded from the European Regional Development Fund.


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  • Figure 1

    GS-CPE framework. (a) Architecture; (b) services and events.

  • Figure 2

    Typical workflow of GS-CPE.

  • Figure 3

    The direction quantization scheme, examples of trajectory sequences and likelihood comparison. (a) Quantization scheme; (b) trajectory of “6”; (c) trajectory of “4”; (d) comparison of second maximum likelihood ratio.

  • Figure 4

    The relationship between indicators and TP.

  • Figure 5

    The experiment results of HMM-GSS algorithm. (a) Average win/lost/profit; (b) average accuracy comparison.

  • Figure 6

    Theoretical and practical performance of MS-DTW. (a) Theoretical accuracy comparison; (b) accuracy increment by multiple-source; (c) theoretical rejection comparison; (d) practical accuracy comparison w/ and w/o template adaptation; (e) practical rejection comparison w/ and w/o template adaptation.

  •   

    Algorithm 1 The HMM-GSS algorithm

    Require:Input trajectory $g$, threshold value $\epsilon$, GSS standard template set $\mathbb{ST}$;

    Output:Label;

    $H_g, \bf{lr}^g\leftarrow$ Classification result and likelihood ratio vector of input gesture $g$ from the HMM classifier;

    if $\mathrm{secmax}( \bf{lr}^g)<\epsilon$ then

    ${\rm Label} \Leftarrow H_g$;

    else

    $\boldsymbol{R}=\{h|h\in\boldsymbol{H}\land\frac{l_h^g}{{\rm max}({\boldsymbol l}^g)}\ge\epsilon\}$;

    $ \bf{GT}=\{t_i|t_i\in \bf{ST}\land{i}\in\boldsymbol{R}\}$;

    ${\rm Label} \Leftarrow S_g$, which is the classification result of input gesture $g$ from the GSS classifier according to $\mathbb{ST}$;

    end if

  •   

    Algorithm 2 The multiple-source DTW (MS-DTW) algorithm

    Require:$\mathbb{T}^u, \mathbb{G}$;

    Output:$L$;

    Initialize $ \bf{Da}=\{{\rm Da}_k^i={\rm DTW}( \bf{Ga}, \bf{ta}_k^i )\}, \bf{Dp}=\{{\rm Dp}_k^i={\rm DTW}( \bf{Gp}, \bf{tp}_k^i)\}, \bf{Tr}_i\leftarrow\phi$, where $i\in[1,n],k\in[1,l]$;

    //The template matching process;

    if using the K-nearest neighbor criterion or nearest neighbor criterion then

    $ \bf{da}\leftarrow\{{\rm da}_i|i\in[1,K]\}$ where ${\rm da}_i$ belongs to the minimal K values in $ \bf{Da}$;

    $ \bf{dp}\leftarrow\{{\rm dp}_i|i\in[1,K]\}$ where ${\rm dp}_i$ belongs to the minimal K values in $ \bf{Dp}$;

    ${\rm Label}_a\leftarrow$ The majority class in $ \bf{da}$;

    ${\rm Label}_p\leftarrow$ The majority class in $ \bf{dp}$;

    else

    $ \bf{da}\leftarrow\{{\rm da}_i=\frac{1}{l}\sum_{k=1}^l{{\rm Da}_k^i}|i\in[1,n]\land{{\rm Da}_k^i}\in \bf{Da}\}$;

    $ \bf{dp}\leftarrow\{{\rm dp}_i=\frac{1}{l}\sum_{k=1}^l{{\rm Dp}_k^i}|i\in[1,n]\land{{\rm Dp}_k^i}\in \bf{Dp}\}$;

    ${\rm Label}_a\leftarrow \arg\!\min_{i\in[1,n]}\{{\rm da}_1,{\rm da}_2,\ldots,{\rm da}_n\}$;

    ${\rm Label}_p\leftarrow \arg\!\min_{i\in[1,n]}\{{\rm dp}_1,{\rm dp}_2,\ldots,{\rm dp}_n\}$;

    end if

    //The rejection determination process;

    if ${\rm Label}_a={\rm Label}_p$ then

    $L\leftarrow {\rm Label}_a$;

    else

    $L\leftarrow0$;

    end if

    //The dynamic template adaptation process;

    if $L \ne 0$ then

    $ \bf{Tr}_L\leftarrow \bf{Tr}_L\cup\{\mathbb{G}\}, {\rm rc}_L\leftarrow0$;

    else

    if received correct label $g$ and $| \bf{Tr}_g|>0$ then

    Update $\boldsymbol{T}_g^u$ using $ \bf{Tr}_g\cup\boldsymbol{T}_g^u$ through the same way of selecting initial standard templates;

    $ \bf{Tr}_g\leftarrow\phi$;

    end if

    end if

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