Real-time hand tracking for rehabilitation and character animation

Hand and finger tracking has a major importance in healthcare, for rehabilitation of hand function required due to a neurological disorder, and in virtual environment applications, like characters animation for on-line games or movies. Current solutions consist mostly of motion tracking gloves with embedded resistive bend sensors that most often suffer from signal drift, sensor saturation, sensor displacement and complex calibration procedures. More advanced solutions provide better tracking stability, but at the expense of a higher cost. The proposed solution aims to provide the required precision, stability and feasibility through the combination of eleven inertial measurements units (IMUs). Each unit captures the spatial orientation of the attached body. To fully capture the hand movement, each finger encompasses two units (at the proximal and distal phalanges), plus one unit at the back of the hand. The proposed glove was validated in two distinct steps: (a) evaluation of the sensors' accuracy and stability over time; (b) evaluation of the bending trajectories during usual finger flexion tasks based on the intra-class correlation coefficient (ICC). Results revealed that the glove was sensitive mainly to magnetic field distortions and sensors tuning. The inclusion of a hard and soft iron correction algorithm and accelerometer and gyro drift and temperature compensation methods provided increased stability and precision. Finger trajectories evaluation yielded high ICC values with an overall reliability within application's satisfying limits. The developed low cost system provides a straightforward calibration and usability, qualifying the device for hand and finger tracking in healthcare and animation industries.

[1]  Carlos Silvestre,et al.  A Geometric Approach to Strapdown Magnetometer Calibration in Sensor Frame , 2008 .

[2]  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).

[3]  Ninja P. Oess,et al.  Design and evaluation of a low-cost instrumented glove for hand function assessment , 2012, Journal of NeuroEngineering and Rehabilitation.

[4]  Luigi Cinque,et al.  Overall design and implementation of the virtual glove , 2013, Comput. Biol. Medicine.

[5]  W. Rymer,et al.  Comparison of Robot-Assisted Reaching to Free Reaching in Promoting Recovery From Chronic Stroke , 2001 .

[6]  Pedro M. Teixeira,et al.  Palco: A multisensor realtime 3D cartoon production system , 2013, 2013 IEEE 2nd International Conference on Serious Games and Applications for Health (SeGAH).

[7]  Eugene Tunik,et al.  Virtual reality to maximize function for hand and arm rehabilitation: exploration of neural mechanisms. , 2009, Studies in health technology and informatics.

[8]  Chih-Chung Lin,et al.  Building Hand Motion-Based Character Animation: The Case of Puppetry , 2010, 2010 International Conference on Cyberworlds.

[9]  Carlos Silvestre,et al.  Geometric Approach to Strapdown Magnetometer Calibration in Sensor Frame , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[11]  Ming C. Leu,et al.  Linguistic properties based on American Sign Language isolated word recognition with artificial neural networks using a sensory glove and motion tracker , 2007, Neurocomputing.

[12]  Giuseppe Placidi,et al.  A smart virtual glove for the hand telerehabilitation , 2007, Comput. Biol. Medicine.