@InProceedings{Aoki2013,
  Title                    = {Segmentation of Human Body Movement Using Inertial Measurement Unit},
  Author                   = {Aoki, T. and Venture, G. and Kuli\'{c}, D.},
  Booktitle                = {Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics},
  Year                     = {2013},
  Pages                    = {1181--1186},

  __markedentry            = {},
  Abstract                 = {This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.},
  Doi                      = {10.1109/SMC.2013.205},
  Keywords                 = {angular velocity;hidden Markov models;motion measurement;pattern recognition;sensors;stochastic processes;time series;IMU sensors;Wii remote;angular velocity data;automatic motion segmentation;continuous time series data;inertial measurement unit;joint angles;motion pattern recognition;online HMM-based segmentation;optical motion capture;real-time human body movement segmentation;stochastic motion modeling;temporal segmentation;Angular velocity;Data models;Hidden Markov models;Joints;Motion segmentation;Pattern recognition;Sensors;Arm motion;HMM;Inertial Measurement Unit;Recognition;Segmentation;Wii Remote},
  Review                   = {This is basically an application of Kohlmorgen/Lemm and Dana's TRO paper, but applied to gyro data instead of joint angles},
  Timestamp                = {2015.06.29}
}

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The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.","doi":"10.1109/SMC.2013.205","keywords":"angular velocity;hidden Markov models;motion measurement;pattern recognition;sensors;stochastic processes;time series;IMU sensors;Wii remote;angular velocity data;automatic motion segmentation;continuous time series data;inertial measurement unit;joint angles;motion pattern recognition;online HMM-based segmentation;optical motion capture;real-time human body movement segmentation;stochastic motion modeling;temporal segmentation;Angular velocity;Data models;Hidden Markov models;Joints;Motion segmentation;Pattern recognition;Sensors;Arm motion;HMM;Inertial Measurement Unit;Recognition;Segmentation;Wii Remote","review":"This is basically an application of Kohlmorgen/Lemm and Dana's TRO paper, but applied to gyro data instead of joint angles","timestamp":"2015.06.29","bibtex":"@InProceedings{Aoki2013,\n Title = {Segmentation of Human Body Movement Using Inertial Measurement Unit},\n Author = {Aoki, T. and Venture, G. and Kuli\\'{c}, D.},\n Booktitle = {Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics},\n Year = {2013},\n Pages = {1181--1186},\n\n __markedentry = {},\n Abstract = {This paper proposes an approach for the temporal segmentation of human body movements using IMU (Inertial Measurement Unit). The approach is based on online HMM-based segmentation of continuous time series data. In previous studies, the real-time segmentation of human body movement using joint angles acquired by optical motion capture has been realized, using stochastic motion modeling. The approach is now adapted for angular velocity data. The segmented motions are recognized via HMM models. The segmentation and recognition results of the proposed algorithm are demonstrated with experiments. Auto segmentation of each motion and recognition of motion patterns are verified using angular velocity data obtained by IMU sensors and the Wii remote. The success rate of auto segmentation using the data obtained by Wii remote was more than 80% on average.},\n Doi = {10.1109/SMC.2013.205},\n Keywords = {angular velocity;hidden Markov models;motion measurement;pattern recognition;sensors;stochastic processes;time series;IMU sensors;Wii remote;angular velocity data;automatic motion segmentation;continuous time series data;inertial measurement unit;joint angles;motion pattern recognition;online HMM-based segmentation;optical motion capture;real-time human body movement segmentation;stochastic motion modeling;temporal segmentation;Angular velocity;Data models;Hidden Markov models;Joints;Motion segmentation;Pattern recognition;Sensors;Arm motion;HMM;Inertial Measurement Unit;Recognition;Segmentation;Wii Remote},\n Review = {This is basically an application of Kohlmorgen/Lemm and Dana's TRO paper, but applied to gyro data instead of joint angles},\n Timestamp = {2015.06.29}\n}\n\n","author_short":["Aoki, T.","Venture, G.","Kulić, D."],"key":"Aoki2013","id":"Aoki2013","bibbaseid":"aoki-venture-kuli-segmentationofhumanbodymovementusinginertialmeasurementunit-2013","role":"author","urls":{},"keyword":["angular velocity;hidden Markov models;motion measurement;pattern recognition;sensors;stochastic processes;time series;IMU sensors;Wii remote;angular velocity data;automatic motion segmentation;continuous time series data;inertial measurement unit;joint angles;motion pattern recognition;online HMM-based segmentation;optical motion capture;real-time human body movement segmentation;stochastic motion modeling;temporal segmentation;Angular velocity;Data models;Hidden Markov models;Joints;Motion segmentation;Pattern recognition;Sensors;Arm motion;HMM;Inertial Measurement Unit;Recognition;Segmentation;Wii Remote"],"downloads":0},"search_terms":["segmentation","human","body","movement","using","inertial","measurement","unit","aoki","venture","kulić"],"keywords":["angular velocity;hidden markov models;motion measurement;pattern recognition;sensors;stochastic processes;time series;imu sensors;wii remote;angular velocity data;automatic motion segmentation;continuous time series data;inertial measurement unit;joint angles;motion pattern recognition;online hmm-based segmentation;optical motion capture;real-time human body movement segmentation;stochastic motion modeling;temporal segmentation;angular velocity;data models;hidden markov models;joints;motion segmentation;pattern recognition;sensors;arm motion;hmm;inertial measurement unit;recognition;segmentation;wii remote"],"authorIDs":[],"dataSources":["iCsmKnycRmHPxmhBd"]}