Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models
Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2015-04-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.5772/60044 |
| _version_ | 1818156692987183104 |
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| author | Fei Chen Qiubo Zhong Ferdinando Cannella Kosuke Sekiyama Toshio Fukuda |
| author_facet | Fei Chen Qiubo Zhong Ferdinando Cannella Kosuke Sekiyama Toshio Fukuda |
| author_sort | Fei Chen |
| collection | DOAJ |
| description | Gesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM) method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio. |
| first_indexed | 2024-12-11T15:02:21Z |
| format | Article |
| id | doaj.art-ad75b3bd3cb14a079c41c3a9b35585c5 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-11T15:02:21Z |
| publishDate | 2015-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-ad75b3bd3cb14a079c41c3a9b35585c52022-12-22T01:01:05ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142015-04-011210.5772/6004410.5772_60044Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov ModelsFei Chen0Qiubo Zhong1Ferdinando Cannella2Kosuke Sekiyama3Toshio Fukuda4 Department of Micro-nano System Engineering, Nagoya University, Japan State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China Advanced Robotics Department, Istituto Italiano di Tecnologia, Genova, Italy Department of Micro-nano System Engineering, Nagoya University, Japan Department of Micro-nano System Engineering, Nagoya University, JapanGesture recognition is essential for human and robot collaboration. Within an industrial hybrid assembly cell, the performance of such a system significantly affects the safety of human workers. This work presents an approach to recognizing hand gestures accurately during an assembly task while in collaboration with a robot co-worker. We have designed and developed a sensor system for measuring natural human-robot interactions. The position and rotation information of a human worker's hands and fingertips are tracked in 3D space while completing a task. A modified chain-code method is proposed to describe the motion trajectory of the measured hands and fingertips. The Hidden Markov Model (HMM) method is adopted to recognize patterns via data streams and identify workers' gesture patterns and assembly intentions. The effectiveness of the proposed system is verified by experimental results. The outcome demonstrates that the proposed system is able to automatically segment the data streams and recognize the gesture patterns thus represented with a reasonable accuracy ratio.https://doi.org/10.5772/60044 |
| spellingShingle | Fei Chen Qiubo Zhong Ferdinando Cannella Kosuke Sekiyama Toshio Fukuda Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models International Journal of Advanced Robotic Systems |
| title | Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models |
| title_full | Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models |
| title_fullStr | Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models |
| title_full_unstemmed | Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models |
| title_short | Hand Gesture Modeling and Recognition for Human and Robot Interactive Assembly Using Hidden Markov Models |
| title_sort | hand gesture modeling and recognition for human and robot interactive assembly using hidden markov models |
| url | https://doi.org/10.5772/60044 |
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