Development of a Wearable Device for Motion Capturing Based on Magnetic and Inertial Measurement Units

This paper presents a novel wearable device for gesture capturing based on inertial and magnetic measurement units that are made up of micromachined gyroscopes, accelerometers, and magnetometers. The low-cost inertial and magnetic measurement unit is compact and small enough to wear and there are altogether thirty-six units integrated in the device. The device is composed of two symmetric parts, and either the right part or the left one contains eighteen units covering all the segments of the arm, palm, and fingers. The offline calibration and online calibration are proposed to improve the accuracy of sensors. Multiple quaternion-based extended Kalman filters are designed to estimate the absolute orientations, and kinematic models of the arm-hand are considered to determine the relative orientations. Furthermore, position algorithm is deduced to compute the positions of corresponding joint. Finally, several experiments are implemented to verify the effectiveness of the proposed wearable device.

[1]  P. Veltink,et al.  Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Elena Mugellini,et al.  A Smart Watch with Embedded Sensors to Recognize Objects, Grasps and Forearm Gestures , 2012 .

[3]  Xiangluo Wang,et al.  Constructing Gyro-free Inertial Measurement Unit from Dual Accelerometers for Gesture Detection , 2014 .

[4]  Robert F. Kirsch,et al.  Miniature Low-Power Inertial Sensors: Promising Technology for Implantable Motion Capture Systems , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Paolo Dario,et al.  A Survey of Glove-Based Systems and Their Applications , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Giovanni Saggio,et al.  A novel array of flex sensors for a goniometric glove , 2014 .

[7]  Carlos Sagüés,et al.  Human-Computer Interaction Based on Hand Gestures Using RGB-D Sensors , 2013, Sensors.

[8]  Xiao Zhang,et al.  A Novel Calibration Method of Magnetic Compass Based on Ellipsoid Fitting , 2011, IEEE Transactions on Instrumentation and Measurement.

[9]  Caterina Rizzi,et al.  RGB cams vs RGB-D sensors: Low cost motion capture technologies performances and limitations , 2014 .

[10]  Yan Su,et al.  Research on the Calibration Method of Micro Inertial Measurement Unit for Engineering Application , 2016, J. Sensors.

[11]  Athanasios V. Vasilakos,et al.  Body Area Networks: A Survey , 2010, Mob. Networks Appl..

[12]  Olga De Troyer,et al.  Designing and Using Semantic Virtual Environment over the Web , 2008, Int. J. Virtual Real..

[13]  Peter H Veltink,et al.  Assessment of hand kinematics using inertial and magnetic sensors , 2014, Journal of NeuroEngineering and Rehabilitation.

[14]  Robert E. Mahony,et al.  Attitude estimation on SO[3] based on direct inertial measurements , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  Norbert Schmitz,et al.  A Low-Cost and Light-Weight Motion Tracking Suit , 2013, 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing.

[16]  Joseph Classen,et al.  Development and evaluation of a low-cost sensor glove for assessment of human finger movements in neurophysiological settings , 2009, Journal of Neuroscience Methods.

[17]  Jafar Keighobadi Fuzzy calibration of a magnetic compass for vehicular applications , 2011 .

[18]  Paolo Dario,et al.  Preliminary evaluation of SensHand V1 in assessing motor skills performance in Parkinson disease , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[19]  Ma Xin,et al.  Two-step optimal filter design for the low-cost attitude and heading reference systems , 2013 .

[20]  Anupam Agrawal,et al.  Vision based hand gesture recognition for human computer interaction: a survey , 2012, Artificial Intelligence Review.

[21]  Xiaoping Yun,et al.  Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking , 2006, IEEE Trans. Robotics.

[22]  Ruize Xu,et al.  MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition , 2012, IEEE Sensors Journal.

[23]  Barnabás Takács How and Why Affordable Virtual Reality Shapes the Future of Education , 2008, Int. J. Virtual Real..

[24]  Philippe Martin,et al.  Non-Linear Symmetry-Preserving Observers on Lie Groups , 2007, IEEE Transactions on Automatic Control.

[25]  Bor-Shing Lin,et al.  Data Glove Embedded with 6-DOF Inertial Sensors for Hand Rehabilitation , 2014, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[26]  Kongqiao Wang,et al.  A Sign-Component-Based Framework for Chinese Sign Language Recognition Using Accelerometer and sEMG Data , 2012, IEEE Transactions on Biomedical Engineering.

[27]  Tae-Seong Kim,et al.  3-D hand motion tracking and gesture recognition using a data glove , 2009, 2009 IEEE International Symposium on Industrial Electronics.

[28]  Joost C. F. de Winter,et al.  Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements , 2015, Sensors.