Abstract
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.
Highlights
As an extraordinarily dexterous part of the human body, the human hand achieves most of the tasks in daily life
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
In order to further verify the actual result of Adaptive DAG (ADAG), it is very necessary to compare with other classical methods of multiclass Support Vector Machine (SVM)
Summary
As an extraordinarily dexterous part of the human body, the human hand achieves most of the tasks in daily life. A comprehensive review of recent Kinect-based computer vision algorithms and applications, including preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3D mapping is presented in [9]. Because of the complex properties involved in the human hand motions, hand motion capture will be faced with some additional challenges, like large posture variations, different colored skin and severe occlusions of the fingers during movements. A generalized framework integrating multiple sensors to study and analyze the hand motions that contained multimodal information was proposed in [13]. This paper designs a multiple sensor based hand motion capture system, which includes EMG, data glove, and Finger Tactile Pressure Sensing (FingerTPS) for hand motion recognition.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
