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  \entry{pratt2010toward}{inproceedings}{}
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    \field{booktitle}{Unmanned Systems Technology XII}
    \field{pages}{769212}
    \field{title}{Toward humanoid robots for operations in complex urban
  environments}
    \field{volume}{7692}
    \field{year}{2010}
  \endentry

  \entry{siciliano2016springer}{book}{}
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    \field{title}{Springer handbook of robotics}
    \field{year}{2016}
  \endentry

  \entry{hopkins2015design}{inproceedings}{}
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    \field{booktitle}{2015 IEEE-RAS 15th International Conference on Humanoid
  Robots (Humanoids)}
    \field{pages}{831\bibrangedash 837}
    \field{title}{Design of a compliant bipedal walking controller for the
  DARPA Robotics Challenge}
    \field{year}{2015}
  \endentry

  \entry{walas2016terrain}{inproceedings}{}
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    \field{booktitle}{2016 IEEE-RAS 16th International Conference on Humanoid
  Robots (Humanoids)}
    \field{pages}{133\bibrangedash 140}
    \field{title}{Terrain classification and locomotion parameters adaptation
  for humanoid robots using force/torque sensing}
    \field{year}{2016}
  \endentry

  \entry{multi_axis_review}{article}{}
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    \verb{doi}
    \verb 10.1109/JSEN.2019.2947719
    \endverb
    \field{number}{3}
    \field{pages}{1126\bibrangedash 1141}
    \field{title}{{Multi-Axis} {Force-Torque} {Sensors} for {Measuring}
  {Zero-Moment} {Point} in {Humanoid} {Robots}: A {Review}}
    \field{volume}{20}
    \field{journaltitle}{IEEE Sensors Journal}
    \field{month}{02}
    \field{year}{2020}
  \endentry

  \entry{schmidhuber1997long}{article}{}
    \name{author}{2}{}{%
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    \field{number}{8}
    \field{pages}{1735\bibrangedash 1780}
    \field{title}{Long short-term memory}
    \field{volume}{9}
    \field{journaltitle}{Neural Comput}
    \field{year}{1997}
  \endentry

  \entry{Yu20201335}{article}{}
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      {{hash=YJ}{%
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         given={J.},
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      {{hash=ZG}{%
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      {{hash=RX}{%
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      {{hash=WY}{%
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         given={Y.},
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    \field{labeltitlesource}{title}
    \field{title}{Method for Generating Cyclic Gait of Biped Robots Based on
  LSTM Neural Network}
  \endentry

  \entry{Ko20201200}{article}{}
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      {{hash=KWR}{%
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      {{hash=JM}{%
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      {{hash=KJ}{%
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    \verb{doi}
    \verb 10.1109/SMC42975.2020.9283177
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    \field{note}{cited By 0}
    \field{pages}{1200\bibrangedash 1205}
    \field{title}{End-to-End Learning of Social Behaviors for Humanoid Robots}
    \field{volume}{2020-October}
    \field{journaltitle}{IEEE Transactions on Systems, Man, and Cybernetics:
  Systems}
    \field{year}{2020}
  \endentry

  \entry{Lobos-Tsunekawa20183247}{article}{}
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    \verb{doi}
    \verb 10.1109/LRA.2018.2851148
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    \field{note}{cited By 21}
    \field{number}{4}
    \field{pages}{3247\bibrangedash 3254}
    \field{title}{Visual navigation for biped humanoid robots using deep
  reinforcement learning}
    \field{volume}{3}
    \field{journaltitle}{IEEE Robotics and Automation Letters}
    \field{year}{2018}
  \endentry

  \entry{Kerzel2019}{inproceedings}{}
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      {{hash=KM}{%
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      {{hash=GE}{%
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    \verb{doi}
    \verb 10.1109/IJCNN.2019.8852359
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    \field{note}{cited By 2}
    \field{title}{Neuro-Robotic Haptic Object Classification by Active
  Exploration on a Novel Dataset}
    \field{volume}{2019-July}
    \field{journaltitle}{Proceedings of the International Joint Conference on
  Neural Networks}
    \field{year}{2019}
  \endentry

  \entry{Bhattacharjee2018}{article}{}
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      {{hash=BT}{%
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      {{hash=RJ}{%
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      {{hash=KC}{%
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    \verb{doi}
    \verb 10.1142/S0219843617500244
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    \field{note}{cited By 7}
    \field{number}{1}
    \field{title}{Inferring Object Properties with a Tactile-Sensing Array
  Given Varying Joint Stiffness and Velocity}
    \field{volume}{15}
    \field{journaltitle}{International Journal of Humanoid Robotics}
    \field{year}{2018}
  \endentry

  \entry{chalvatzaki2019lstm}{inproceedings}{}
    \name{author}{6}{}{%
      {{hash=CG}{%
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      {{hash=KP}{%
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      {{hash=HJ}{%
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      {{hash=PXS}{%
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      {{hash=TCS}{%
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      {{hash=MP}{%
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         giveni={P\bibinitperiod},
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      {IEEE}%
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    \strng{namehash}{CG+1}
    \strng{fullhash}{CGKPHJPXSTCSMP1}
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    \field{labeltitlesource}{title}
    \field{booktitle}{2019 International Conference on Robotics and Automation
  (ICRA)}
    \field{pages}{4225\bibrangedash 4232}
    \field{title}{LSTM-based network for human gait stability prediction in an
  intelligent robotic rollator}
    \field{year}{2019}
  \endentry

  \entry{bi_lstm}{article}{}
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      {{hash=LHb}{%
         family={Li},
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         given={Hao\bibnamedelima bo},
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      {{hash=SA}{%
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         familyi={S\bibinitperiod},
         given={Aman},
         giveni={A\bibinitperiod},
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      {{hash=HH}{%
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      {{hash=KJ}{%
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         giveni={J\bibinitperiod},
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      {{hash=FF}{%
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         giveni={F\bibinitperiod},
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    }
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    \verb{doi}
    \verb 10.1109/JSEN.2019.2946095
    \endverb
    \field{number}{3}
    \field{pages}{1191\bibrangedash 1201}
    \field{title}{{Bi-LSTM} {Network} for {Multimodal} {Continuous} {Human}
  {Activity} {Recognition} and {Fall} {Detection}}
    \field{volume}{20}
    \field{journaltitle}{IEEE Sensors Journal}
    \field{month}{02}
    \field{year}{2020}
  \endentry

  \entry{how2014multiple}{inproceedings}{}
    \name{author}{4}{}{%
      {{hash=HDNT}{%
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      }}%
      {{hash=SKSM}{%
         family={Sahari},
         familyi={S\bibinitperiod},
         given={Khairul Salleh\bibnamedelima Mohamed},
         giveni={K\bibinitperiod\bibinitdelim S\bibinitperiod\bibinitdelim
  M\bibinitperiod},
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      {{hash=YH}{%
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         familyi={Y\bibinitperiod},
         given={Hu},
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      {{hash=KLC}{%
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         given={Loo\bibnamedelima Chu},
         giveni={L\bibinitperiod\bibinitdelim C\bibinitperiod},
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      {IEEE}%
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    \strng{namehash}{HDNT+1}
    \strng{fullhash}{HDNTSKSMYHKLC1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2014 IEEE international symposium on robotics and
  manufacturing automation (ROMA)}
    \field{pages}{109\bibrangedash 114}
    \field{title}{Multiple sequence behavior recognition on humanoid robot
  using long short-term memory (LSTM)}
    \field{year}{2014}
  \endentry

  \entry{zhao2018collaborative}{inproceedings}{}
    \name{author}{5}{}{%
      {{hash=ZX}{%
         family={Zhao},
         familyi={Z\bibinitperiod},
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         giveni={X\bibinitperiod},
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      {{hash=CS}{%
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         giveni={S\bibinitperiod},
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      {{hash=DS}{%
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         familyi={D\bibinitperiod},
         given={Shuanda},
         giveni={S\bibinitperiod},
      }}%
      {{hash=RJ}{%
         family={Rojas},
         familyi={R\bibinitperiod},
         given={Juan},
         giveni={J\bibinitperiod},
      }}%
      {{hash=PJ}{%
         family={Pan},
         familyi={P\bibinitperiod},
         given={Jia},
         giveni={J\bibinitperiod},
      }}%
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      {IEEE}%
    }
    \strng{namehash}{ZX+1}
    \strng{fullhash}{ZXCSDSRJPJ1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2018 IEEE-RAS 18th International Conference on Humanoid
  Robots (Humanoids)}
    \field{pages}{1\bibrangedash 9}
    \field{title}{Collaborative human-robot motion generation using LSTM-RNN}
    \field{year}{2018}
  \endentry

  \entry{yu2020walking}{inproceedings}{}
    \name{author}{5}{}{%
      {{hash=YJ}{%
         family={Yu},
         familyi={Y\bibinitperiod},
         given={Jianjun},
         giveni={J\bibinitperiod},
      }}%
      {{hash=LC}{%
         family={Li},
         familyi={L\bibinitperiod},
         given={Chen},
         giveni={C\bibinitperiod},
      }}%
      {{hash=GD}{%
         family={Gong},
         familyi={G\bibinitperiod},
         given={Daoxiong},
         giveni={D\bibinitperiod},
      }}%
      {{hash=ZG}{%
         family={Zuo},
         familyi={Z\bibinitperiod},
         given={Guoyu},
         giveni={G\bibinitperiod},
      }}%
      {{hash=WY}{%
         family={Wang},
         familyi={W\bibinitperiod},
         given={Yang},
         giveni={Y\bibinitperiod},
      }}%
    }
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      {IEEE}%
    }
    \strng{namehash}{YJ+2}
    \strng{fullhash}{YJLCGDZGWY1}
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    \field{labeltitlesource}{title}
    \field{booktitle}{2020 IEEE International Conference on Real-time Computing
  and Robotics (RCAR)}
    \field{pages}{256\bibrangedash 261}
    \field{title}{Walking Simulation of Biped Robot on Inclined Plane Based on
  Gait Recognition}
    \field{year}{2020}
  \endentry

  \entry{li2018mobile}{article}{}
    \name{author}{3}{}{%
      {{hash=LS}{%
         family={Li},
         familyi={L\bibinitperiod},
         given={Shiwei},
         giveni={S\bibinitperiod},
      }}%
      {{hash=ZY}{%
         family={Zhao},
         familyi={Z\bibinitperiod},
         given={Yongping},
         giveni={Y\bibinitperiod},
      }}%
      {{hash=DM}{%
         family={Ding},
         familyi={D\bibinitperiod},
         given={Mingli},
         giveni={M\bibinitperiod},
      }}%
    }
    \list{publisher}{1}{%
      {World Scientific}%
    }
    \strng{namehash}{LSZYDM1}
    \strng{fullhash}{LSZYDM1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{number}{08}
    \field{pages}{1840034}
    \field{title}{Mobile Robot Motor Bearing Fault Detection and Classification
  on Discrete Wavelet Transform and LSTM Network}
    \field{volume}{18}
    \field{journaltitle}{Journal of Mechanics in Medicine and Biology}
    \field{year}{2018}
  \endentry

  \entry{li_sequential_2021}{article}{}
    \name{author}{6}{}{%
      {{hash=LTHS}{%
         family={Li},
         familyi={L\bibinitperiod},
         given={Tzuu-Hseng\bibnamedelima S.},
         giveni={T\bibinitperiod-H\bibinitperiod\bibinitdelim S\bibinitperiod},
      }}%
      {{hash=KPH}{%
         family={Kuo},
         familyi={K\bibinitperiod},
         given={Ping-Huan},
         giveni={P\bibinitperiod-H\bibinitperiod},
      }}%
      {{hash=CCH}{%
         family={Cheng},
         familyi={C\bibinitperiod},
         given={Chuan-Han},
         giveni={C\bibinitperiod-H\bibinitperiod},
      }}%
      {{hash=HCC}{%
         family={Hung},
         familyi={H\bibinitperiod},
         given={Chia-Ching},
         giveni={C\bibinitperiod-C\bibinitperiod},
      }}%
      {{hash=LPC}{%
         family={Luan},
         familyi={L\bibinitperiod},
         given={Po-Chien},
         giveni={P\bibinitperiod-C\bibinitperiod},
      }}%
      {{hash=CCH}{%
         family={Chang},
         familyi={C\bibinitperiod},
         given={Chien-Hsin},
         giveni={C\bibinitperiod-H\bibinitperiod},
      }}%
    }
    \list{language}{1}{%
      {en}%
    }
    \strng{namehash}{LTHS+1}
    \strng{fullhash}{LTHSKPHCCHHCCLPCCCH1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{abstract}{%
    This study proposes a real-time sequential sensor fusion based gait pattern
  controller. During the walking cycle, the sensor data is analyzed and
  obtained by several inertial measurement units (IMUs) and pressure sensors on
  the robot. Besides, in order to model the correction values of the walking
  parameters, this approach applies long short-term memory (LSTM) to build a
  feedback system for a bipedal humanoid robot. For training the network, four
  sequential features including two-direction Center of Pressure (CoP) and
  two-direction acceleration are acquired by using pressure sensors and IMUs on
  the robot. While these features are mapped to the network inputs, the network
  output is mapped by the optimal solutions generated using particle swarm
  optimization (PSO). The proposed LSTM feedback network comprises four layers.
  The effect of the proposed methodology is first tested in a robot simulation
  environment and then tested on a real bipedal humanoid robot. The
  experimental results demonstrate that this methodology can make the humanoid
  robot perform better than when using a fixed gait. It also provides
  self-adjustment capability to the robot to enable it to avoid situations like
  falling.%
    }
    \verb{doi}
    \verb 10.1109/JSEN.2020.3016968
    \endverb
    \field{issn}{1530-437X, 1558-1748, 2379-9153}
    \field{number}{2}
    \field{pages}{2241\bibrangedash 2255}
    \field{title}{Sequential {Sensor} {Fusion}-{Based} {Real}-{Time} {LSTM}
  {Gait} {Pattern} {Controller} for {Biped} {Robot}}
    \verb{url}
    \verb https://ieeexplore.ieee.org/document/9169709/
    \endverb
    \field{volume}{21}
    \verb{file}
    \verb Li et al. - 2021 - Sequential Sensor Fusion-Based Real-Time LSTM Gait
    \verb .pdf:C\:\\Users\\LMMA\\Zotero\\storage\\A8P9HEVR\\Li et al. - 2021 -
    \verb Sequential Sensor Fusion-Based Real-Time LSTM Gait.pdf:application/pd
    \verb f
    \endverb
    \field{journaltitle}{IEEE Sensors Journal}
    \field{month}{01}
    \field{year}{2021}
    \field{urlday}{21}
    \field{urlmonth}{03}
    \field{urlyear}{2021}
  \endentry

  \entry{zhang_fingervision_2018}{article}{}
    \name{author}{5}{}{%
      {{hash=ZY}{%
         family={Zhang},
         familyi={Z\bibinitperiod},
         given={Yazhan},
         giveni={Y\bibinitperiod},
      }}%
      {{hash=KZ}{%
         family={Kan},
         familyi={K\bibinitperiod},
         given={Zicheng},
         giveni={Z\bibinitperiod},
      }}%
      {{hash=TYA}{%
         family={Tse},
         familyi={T\bibinitperiod},
         given={Yu\bibnamedelima Alexander},
         giveni={Y\bibinitperiod\bibinitdelim A\bibinitperiod},
      }}%
      {{hash=YY}{%
         family={Yang},
         familyi={Y\bibinitperiod},
         given={Yang},
         giveni={Y\bibinitperiod},
      }}%
      {{hash=WMY}{%
         family={Wang},
         familyi={W\bibinitperiod},
         given={Michael\bibnamedelima Yu},
         giveni={M\bibinitperiod\bibinitdelim Y\bibinitperiod},
      }}%
    }
    \list{language}{1}{%
      {en}%
    }
    \keyw{Computer Science - Machine Learning, Computer Science - Robotics}
    \strng{namehash}{ZY+1}
    \strng{fullhash}{ZYKZTYAYYWMY1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{abstract}{%
    Tactile sensing is essential to the human perception system, so as to
  robot. In this paper, we develop a novel optical-based tactile sensor
  “FingerVision” with effective signal processing algorithms. This sensor
  is composed of soft skin with embedded marker array bonded to rigid frame,
  and a web camera with a ﬁsheye lens. While being excited with contact
  force, the camera tracks the movements of markers and deformation ﬁeld is
  obtained. Compared to existing tactile sensors, our sensor features compact
  footprint, high resolution, and ease of fabrication. Besides, utilizing the
  deformation ﬁeld estimation, we propose a slip classiﬁcation framework
  based on convolution Long Short Term Memory (convolutional LSTM) networks.
  The data collection process takes advantage of the human sense of slip,
  during which human hand holds 12 daily objects, interacts with sensor skin
  and labels data with a slip or non-slip identity based on human feeling of
  slip. Our slip classiﬁcation framework performs high accuracy of 97.62\% on
  the test dataset. It is expected to be capable of enhancing the stability of
  robot grasping signiﬁcantly, leading to better contact force control,
  ﬁner object interaction and more active sensing manipulation.%
    }
    \field{note}{arXiv: 1810.02653}
    \field{title}{{FingerVision} {Tactile} {Sensor} {Design} and {Slip}
  {Detection} {Using} {Convolutional} {LSTM} {Network}}
    \verb{url}
    \verb http://arxiv.org/abs/1810.02653
    \endverb
    \verb{file}
    \verb Zhang et al. - 2018 - FingerVision Tactile Sensor Design and Slip Det
    \verb ect.pdf:C\:\\Users\\LMMA\\Zotero\\storage\\3ZSE6TYS\\Zhang et al. - 2
    \verb 018 - FingerVision Tactile Sensor Design and Slip Detect.pdf:applicat
    \verb ion/pdf
    \endverb
    \field{journaltitle}{arXiv:1810.02653 [cs]}
    \field{annotation}{%
    Comment: 7 pages, 7 figures, submitted to ICRA2019%
    }
    \field{month}{10}
    \field{year}{2018}
    \field{urlday}{21}
    \field{urlmonth}{03}
    \field{urlyear}{2021}
  \endentry

  \entry{9292620}{inproceedings}{}
    \name{author}{4}{}{%
      {{hash=LD}{%
         family={{Liu}},
         familyi={L\bibinitperiod},
         given={D.},
         giveni={D\bibinitperiod},
      }}%
      {{hash=JH}{%
         family={{Jeong}},
         familyi={J\bibinitperiod},
         given={H.},
         giveni={H\bibinitperiod},
      }}%
      {{hash=WA}{%
         family={{Wei}},
         familyi={W\bibinitperiod},
         given={A.},
         giveni={A\bibinitperiod},
      }}%
      {{hash=KV}{%
         family={{Kapila}},
         familyi={K\bibinitperiod},
         given={V.},
         giveni={V\bibinitperiod},
      }}%
    }
    \strng{namehash}{LD+1}
    \strng{fullhash}{LDJHWAKV1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2020 IEEE International Symposium on Safety, Security,
  and Rescue Robotics (SSRR)}
    \verb{doi}
    \verb 10.1109/SSRR50563.2020.9292620
    \endverb
    \field{pages}{129\bibrangedash 135}
    \field{title}{Bidirectional LSTM-based Network for Fall Prediction in a
  Humanoid}
    \field{year}{2020}
  \endentry

  \entry{farazi2017online}{inproceedings}{}
    \name{author}{2}{}{%
      {{hash=FH}{%
         family={Farazi},
         familyi={F\bibinitperiod},
         given={Hafez},
         giveni={H\bibinitperiod},
      }}%
      {{hash=BS}{%
         family={Behnke},
         familyi={B\bibinitperiod},
         given={Sven},
         giveni={S\bibinitperiod},
      }}%
    }
    \list{organization}{1}{%
      {IEEE}%
    }
    \strng{namehash}{FHBS1}
    \strng{fullhash}{FHBS1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2017 IEEE/RSJ International Conference on Intelligent
  Robots and Systems (IROS)}
    \field{pages}{6118\bibrangedash 6125}
    \field{title}{Online visual robot tracking and identification using deep
  LSTM networks}
    \field{year}{2017}
  \endentry

  \entry{zhen_walking_2019}{article}{}
    \name{author}{3}{}{%
      {{hash=ZT}{%
         family={Zhen},
         familyi={Z\bibinitperiod},
         given={Tao},
         giveni={T\bibinitperiod},
      }}%
      {{hash=YL}{%
         family={Yan},
         familyi={Y\bibinitperiod},
         given={Lei},
         giveni={L\bibinitperiod},
      }}%
      {{hash=YP}{%
         family={Yuan},
         familyi={Y\bibinitperiod},
         given={Peng},
         giveni={P\bibinitperiod},
      }}%
    }
    \list{language}{1}{%
      {en}%
    }
    \strng{namehash}{ZTYLYP1}
    \strng{fullhash}{ZTYLYP1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{abstract}{%
    Gait phase detection is a new biometric method which is of great
  signiﬁcance in gait correction, disease diagnosis, and exoskeleton assisted
  robots. Especially for the development of bone assisted robots, gait phase
  recognition is an indispensable key technology. In this study, the main
  characteristics of the gait phases were determined to identify each gait
  phase. A long short-term memory-deep neural network (LSTM-DNN) algorithm is
  proposed for gate detection. Compared with the traditional threshold
  algorithm and the LSTM, the proposed algorithm has higher detection accuracy
  for diﬀerent walking speeds and diﬀerent test subjects. During the
  identiﬁcation process, the acceleration signals obtained from the
  acceleration sensors were normalized to ensure that the diﬀerent features
  had the same scale. Principal components analysis (PCA) was used to reduce
  the data dimensionality and the processed data were used to create the input
  feature vector of the LSTM-DNN algorithm. Finally, the data set was
  classiﬁed using the Softmax classiﬁer in the full connection layer.
  Diﬀerent algorithms were applied to the gait phase detection of multiple
  male and female subjects. The experimental results showed that the gait-phase
  recognition accuracy and F-score of the LSTM-DNN algorithm are over 91.8\%
  and 92\%, respectively, which is better than the other three algorithms and
  also veriﬁes the eﬀectiveness of the LSTM-DNN algorithm in practice.%
    }
    \verb{doi}
    \verb 10.3390/a12120253
    \endverb
    \field{issn}{1999-4893}
    \field{number}{12}
    \field{pages}{253}
    \field{title}{Walking {Gait} {Phase} {Detection} {Based} on {Acceleration}
  {Signals} {Using} {LSTM}-{DNN} {Algorithm}}
    \verb{url}
    \verb https://www.mdpi.com/1999-4893/12/12/253
    \endverb
    \field{volume}{12}
    \verb{file}
    \verb Zhen et al. - 2019 - Walking Gait Phase Detection Based on Accelerati
    \verb on.pdf:C\:\\Users\\LMMA\\Zotero\\storage\\SEPWA5BN\\Zhen et al. - 201
    \verb 9 - Walking Gait Phase Detection Based on Acceleration.pdf:applicatio
    \verb n/pdf
    \endverb
    \field{journaltitle}{Algorithms}
    \field{month}{11}
    \field{year}{2019}
    \field{urlday}{21}
    \field{urlmonth}{03}
    \field{urlyear}{2021}
  \endentry

  \entry{li_cnn_2019}{article}{}
    \name{author}{4}{}{%
      {{hash=LTHS}{%
         family={Li},
         familyi={L\bibinitperiod},
         given={Tzuu-Hseng\bibnamedelima S.},
         giveni={T\bibinitperiod-H\bibinitperiod\bibinitdelim S\bibinitperiod},
      }}%
      {{hash=KPH}{%
         family={Kuo},
         familyi={K\bibinitperiod},
         given={Ping-Huan},
         giveni={P\bibinitperiod-H\bibinitperiod},
      }}%
      {{hash=TTN}{%
         family={Tsai},
         familyi={T\bibinitperiod},
         given={Ting-Nan},
         giveni={T\bibinitperiod-N\bibinitperiod},
      }}%
      {{hash=LPC}{%
         family={Luan},
         familyi={L\bibinitperiod},
         given={Po-Chien},
         giveni={P\bibinitperiod-C\bibinitperiod},
      }}%
    }
    \list{language}{1}{%
      {en}%
    }
    \strng{namehash}{LTHS+1}
    \strng{fullhash}{LTHSKPHTTNLPC1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{abstract}{%
    Robots must be able to recognize human emotions to improve the
  human–robot interaction (HRI). This study proposes an emotion recognition
  system for a humanoid robot. The robot is equipped with a camera to capture
  users’ facial images, and it uses this system to recognize users’
  emotions and responds appropriately. The emotion recognition system, based on
  a deep neural network, learns six basic emotions: happiness, anger, disgust,
  fear, sadness, and surprise. First, a convolutional neural network (CNN) is
  used to extract visual features by learning on a large number of static
  images. Second, a long short-term memory (LSTM) recurrent neural network is
  used to determine the relationship between the transformation of facial
  expressions in image sequences and the six basic emotions. Third, CNN and
  LSTM are combined to exploit their advantages in the proposed model. Finally,
  the performance of the emotion recognition system is improved by using
  transfer learning, that is, by transferring knowledge of related but
  different problems. The performance of the proposed system is veriﬁed
  through leave-one-out cross-validation and compared with that of other
  models. The system is applied to a humanoid robot to demonstrate its
  practicability for improving the HRI.%
    }
    \verb{doi}
    \verb 10.1109/ACCESS.2019.2928364
    \endverb
    \field{issn}{2169-3536}
    \field{pages}{93998\bibrangedash 94011}
    \field{title}{{CNN} and {LSTM} {Based} {Facial} {Expression} {Analysis}
  {Model} for a {Humanoid} {Robot}}
    \verb{url}
    \verb https://ieeexplore.ieee.org/document/8760246/
    \endverb
    \field{volume}{7}
    \verb{file}
    \verb Li et al. - 2019 - CNN and LSTM Based Facial Expression Analysis Mode
    \verb .pdf:C\:\\Users\\LMMA\\Zotero\\storage\\YM7GRF8U\\Li et al. - 2019 -
    \verb CNN and LSTM Based Facial Expression Analysis Mode.pdf:application/pd
    \verb f
    \endverb
    \field{journaltitle}{IEEE Access}
    \field{year}{2019}
    \field{urlday}{21}
    \field{urlmonth}{03}
    \field{urlyear}{2021}
  \endentry

  \entry{wang2020situ}{inproceedings}{}
    \name{author}{4}{}{%
      {{hash=WM}{%
         family={Wang},
         familyi={W\bibinitperiod},
         given={Maozhen},
         giveni={M\bibinitperiod},
      }}%
      {{hash=WM}{%
         family={Wonsick},
         familyi={W\bibinitperiod},
         given={Murphy},
         giveni={M\bibinitperiod},
      }}%
      {{hash=LX}{%
         family={Long},
         familyi={L\bibinitperiod},
         given={Xianchao},
         giveni={X\bibinitperiod},
      }}%
      {{hash=PT}{%
         family={Padr},
         familyi={P\bibinitperiod},
         given={Ta{\c{s}}kn},
         giveni={T\bibinitperiod},
      }}%
    }
    \list{organization}{1}{%
      {IEEE}%
    }
    \strng{namehash}{WM+1}
    \strng{fullhash}{WMWMLXPT1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2020 IEEE/ASME International Conference on Advanced
  Intelligent Mechatronics (AIM)}
    \field{pages}{765\bibrangedash 770}
    \field{title}{In-situ Terrain Classification and Estimation for NASA’s
  Humanoid Robot Valkyrie}
    \field{year}{2020}
  \endentry

  \entry{almeida2018novel}{inproceedings}{}
    \name{author}{3}{}{%
      {{hash=AL}{%
         family={Almeida},
         familyi={A\bibinitperiod},
         given={Luís},
         giveni={L\bibinitperiod},
      }}%
      {{hash=SV}{%
         family={Santos},
         familyi={S\bibinitperiod},
         given={Vitor},
         giveni={V\bibinitperiod},
      }}%
      {{hash=SF}{%
         family={Silva},
         familyi={S\bibinitperiod},
         given={Filipe},
         giveni={F\bibinitperiod},
      }}%
    }
    \list{organization}{1}{%
      {IEEE}%
    }
    \strng{namehash}{ALSVSF1}
    \strng{fullhash}{ALSVSF1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2018 IEEE International Conference on Autonomous Robot
  Systems and Competitions (ICARSC)}
    \field{pages}{36\bibrangedash 41}
    \field{title}{A novel wireless instrumented shoe for ground reaction forces
  analysis in humanoids}
    \field{year}{2018}
  \endentry

  \entry{almeida2020learning}{article}{}
    \name{author}{3}{}{%
      {{hash=AL}{%
         family={Almeida},
         familyi={A\bibinitperiod},
         given={Luís},
         giveni={L\bibinitperiod},
      }}%
      {{hash=SV}{%
         family={Santos},
         familyi={S\bibinitperiod},
         given={Vitor},
         giveni={V\bibinitperiod},
      }}%
      {{hash=FJ}{%
         family={Ferreira},
         familyi={F\bibinitperiod},
         given={João},
         giveni={J\bibinitperiod},
      }}%
    }
    \list{publisher}{1}{%
      {World Scientific}%
    }
    \strng{namehash}{ALSVFJ1}
    \strng{fullhash}{ALSVFJ1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{pages}{2050011}
    \field{title}{Learning-Based Analysis of a New Wearable 3D Force System
  Data to Classify the Underlying Surface of a Walking Robot}
    \field{journaltitle}{International Journal of Humanoid Robotics}
    \field{year}{2020}
  \endentry

  \entry{graves_supervised_2012}{book}{}
    \name{author}{1}{}{%
      {{hash=GA}{%
         family={Graves},
         familyi={G\bibinitperiod},
         given={Alex},
         giveni={A\bibinitperiod},
      }}%
    }
    \list{language}{1}{%
      {en}%
    }
    \list{publisher}{1}{%
      {Springer Berlin Heidelberg}%
    }
    \strng{namehash}{GA1}
    \strng{fullhash}{GA1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \verb{doi}
    \verb 10.1007/978-3-642-24797-2
    \endverb
    \field{series}{Studies in {Computational} {Intelligence}}
    \field{title}{Supervised {Sequence} {Labelling} with {Recurrent} {Neural}
  {Networks}}
    \field{volume}{385}
    \list{location}{1}{%
      {Berlin, Heidelberg}%
    }
    \field{year}{2012}
  \endentry

  \entry{varsamopoulos2018designing}{article}{}
    \name{author}{3}{}{%
      {{hash=VS}{%
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         familyi={V\bibinitperiod},
         given={Savvas},
         giveni={S\bibinitperiod},
      }}%
      {{hash=BK}{%
         family={Bertels},
         familyi={B\bibinitperiod},
         given={Koen},
         giveni={K\bibinitperiod},
      }}%
      {{hash=ACG}{%
         family={Almudever},
         familyi={A\bibinitperiod},
         given={Carmen\bibnamedelima G},
         giveni={C\bibinitperiod\bibinitdelim G\bibinitperiod},
      }}%
    }
    \strng{namehash}{VSBKACG1}
    \strng{fullhash}{VSBKACG1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{title}{Designing neural network based decoders for surface codes}
    \field{journaltitle}{arXiv preprint arXiv:1811.12456}
    \field{year}{2018}
  \endentry

  \entry{kingma2014adam}{article}{}
    \name{author}{2}{}{%
      {{hash=KDP}{%
         family={Kingma},
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         given={Diederik\bibnamedelima P},
         giveni={D\bibinitperiod\bibinitdelim P\bibinitperiod},
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      {{hash=BJ}{%
         family={Ba},
         familyi={B\bibinitperiod},
         given={Jimmy},
         giveni={J\bibinitperiod},
      }}%
    }
    \strng{namehash}{KDPBJ1}
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    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{title}{Adam: A method for stochastic optimization}
    \field{journaltitle}{arXiv preprint arXiv:1412.6980}
    \field{year}{2014}
  \endentry

  \entry{chang2018effective}{inproceedings}{}
    \name{author}{3}{}{%
      {{hash=CZ}{%
         family={Chang},
         familyi={C\bibinitperiod},
         given={Zihan},
         giveni={Z\bibinitperiod},
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      {{hash=ZY}{%
         family={Zhang},
         familyi={Z\bibinitperiod},
         given={Yang},
         giveni={Y\bibinitperiod},
      }}%
      {{hash=CW}{%
         family={Chen},
         familyi={C\bibinitperiod},
         given={Wenbo},
         giveni={W\bibinitperiod},
      }}%
    }
    \list{organization}{1}{%
      {IEEE}%
    }
    \strng{namehash}{CZZYCW1}
    \strng{fullhash}{CZZYCW1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{booktitle}{2018 IEEE 9th international conference on software
  engineering and service science (ICSESS)}
    \field{pages}{245\bibrangedash 248}
    \field{title}{Effective adam-optimized LSTM neural network for electricity
  price forecasting}
    \field{year}{2018}
  \endentry

  \entry{wang2020power}{article}{}
    \name{author}{8}{}{%
      {{hash=WZ}{%
         family={Wang},
         familyi={W\bibinitperiod},
         given={Zhankui},
         giveni={Z\bibinitperiod},
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      {{hash=WJ}{%
         family={Wu},
         familyi={W\bibinitperiod},
         given={Junying},
         giveni={J\bibinitperiod},
      }}%
      {{hash=XR}{%
         family={Xin},
         familyi={X\bibinitperiod},
         given={Rui},
         giveni={R\bibinitperiod},
      }}%
      {{hash=BT}{%
         family={Bai},
         familyi={B\bibinitperiod},
         given={Tao},
         giveni={T\bibinitperiod},
      }}%
      {{hash=ZJ}{%
         family={Zhao},
         familyi={Z\bibinitperiod},
         given={Jianbin},
         giveni={J\bibinitperiod},
      }}%
      {{hash=WM}{%
         family={Wei},
         familyi={W\bibinitperiod},
         given={Minglei},
         giveni={M\bibinitperiod},
      }}%
      {{hash=LJ}{%
         family={Li},
         familyi={L\bibinitperiod},
         given={Jingquan},
         giveni={J\bibinitperiod},
      }}%
      {{hash=ZL}{%
         family={Zhuang},
         familyi={Z\bibinitperiod},
         given={Lei},
         giveni={L\bibinitperiod},
      }}%
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    \field{labeltitlesource}{title}
    \field{number}{1}
    \field{pages}{012056}
    \field{title}{The Power of Short-term Load Algorithm Based on LSTM}
    \field{volume}{453}
    \field{journaltitle}{E\&ES}
    \field{year}{2020}
  \endentry

  \entry{srivastava2014dropout}{article}{}
    \name{author}{5}{}{%
      {{hash=SN}{%
         family={Srivastava},
         familyi={S\bibinitperiod},
         given={Nitish},
         giveni={N\bibinitperiod},
      }}%
      {{hash=HG}{%
         family={Hinton},
         familyi={H\bibinitperiod},
         given={Geoffrey},
         giveni={G\bibinitperiod},
      }}%
      {{hash=KA}{%
         family={Krizhevsky},
         familyi={K\bibinitperiod},
         given={Alex},
         giveni={A\bibinitperiod},
      }}%
      {{hash=SI}{%
         family={Sutskever},
         familyi={S\bibinitperiod},
         given={Ilya},
         giveni={I\bibinitperiod},
      }}%
      {{hash=SR}{%
         family={Salakhutdinov},
         familyi={S\bibinitperiod},
         given={Ruslan},
         giveni={R\bibinitperiod},
      }}%
    }
    \list{publisher}{1}{%
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    }
    \strng{namehash}{SN+1}
    \strng{fullhash}{SNHGKASISR1}
    \field{labelnamesource}{author}
    \field{labeltitlesource}{title}
    \field{number}{1}
    \field{pages}{1929\bibrangedash 1958}
    \field{title}{Dropout: a simple way to prevent neural networks from
  overfitting}
    \field{volume}{15}
    \field{journaltitle}{The journal of machine learning research}
    \field{year}{2014}
  \endentry
\endsortlist
\endinput
