Multiple sensors based hand motion recognition using adaptive directed acyclic graph

Yaxu Xue, Zhaojie Ju, Kui Xiang, Jing Chen, Honghai Liu

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The use of human hand motions as an effective way to interact with computers/robots, robot manipulation learning and prosthetic hand control is being researched in-depth. This~paper proposes a novel and effective multiple sensor based hand motion capture and recognition system. Ten common predefined object grasp and manipulation tasks demonstrated by different subjects are recorded from both the human hand and object points of view. Three types of sensors, including~electromyography, data glove and FingerTPS are applied to simultaneously capture the EMG signals, the finger angle trajectories, and the contact force. Recognising different grasp and manipulation tasks based on the combined signals is investigated by using an adaptive directed acyclic graph algorithm, and results of comparative experiments show the proposed system with a higher recognition rate compared with individual sensing technology, as well as other algorithms. The proposed framework contains abundant information from multimodal human hand motions with the multiple sensor techniques, and it is potentially applicable to applications in prosthetic hand control and artificial systems performing autonomous dexterous manipulation.
Original languageEnglish
Article number358
JournalApplied Sciences
Issue number4
Publication statusPublished - 5 Apr 2017


  • EMG
  • contact force
  • data glove
  • adaptive directed acyclic graph


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