Functional Principal Component Analysis for Recognition of arm Gestures and humanoid Imitation

This paper investigates the use of functional principal component analysis (FPCA) for automatic recognition of dynamic human arm gestures and robot imitation. FPCA is a statistical technique of functional data analysis that generalizes standard multivariate principal component analysis. Functional data analysis signals (e.g., gestures) are functions that are considered as observations of a random variable on a functional space. In particular, FPCA reduces the dimensionality of the input data by projecting them onto a finite-dimensional space spanned by a few prominent eigenfunctions. The main contribution of this work is the proposal of a novel technique for unsupervised clustering of training data and dynamic gesture recognition based on FPCA. FPCA has not been considered in previous studies on humanoid learning. The proposed approach has been evaluated in two experimental settings for motion capture. In the first setup single arm gestures are recognized from inertial sensors attached to the arm of the user. In the second setup the method is extended to two-arm gestures acquired from a range sensor. Recognized gestures are reproduced by a small humanoid robot. The FPCA method has also been compared to a high performance algorithm for gesture classification based on dynamic time warping (DTW). The FPCA algorithm achieves comparable results in both recognition rate and robustness to missing data, while it outperforms DTW in terms of efficiency in execution time.

[1]  Eiichi Yoshida,et al.  On human motion imitation by humanoid robot , 2008, 2008 IEEE International Conference on Robotics and Automation.

[2]  Stepán Obdrzálek,et al.  Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Christopher G. Atkeson,et al.  Adapting human motion for the control of a humanoid robot , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[4]  Makoto Sato,et al.  "Do Like Me", "Do Like This": Creating Animations by Teaching a Virtual Human , 2007, IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07).

[5]  Aaron F. Bobick,et al.  Parametric Hidden Markov Models for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Masayuki Inaba,et al.  Intent imitation using wearable motion capturing system with on-line teaching of task attention , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[7]  Jin-Hyung Kim,et al.  An HMM-Based Threshold Model Approach for Gesture Recognition , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Markus H. Gross,et al.  A Framework for 3D Spatial Gesture Design and Modeling Using a Wearable Input Device , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[9]  Ales Ude,et al.  Enabling real-time full-body imitation: a natural way of transferring human movement to humanoids , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[10]  Alejandro Hernández Arieta,et al.  Gesture recognition in upper-limb prosthetics: A viability study using dynamic time warping and gyroscopes , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  Joan Climent,et al.  A Performance Evaluation of HMM and DTW for Gesture Recognition , 2012, CIARP.

[12]  Rajesh P. N. Rao,et al.  Robotic imitation from human motion capture using Gaussian processes , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[13]  Sethu Vijayakumar,et al.  Latent spaces for dynamic movement primitives , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[14]  Dana Kulic,et al.  Perception and Generation of Affective Hand Movements , 2013, Int. J. Soc. Robotics.

[15]  J. O. Ramsay,et al.  Functional Data Analysis (Springer Series in Statistics) , 1997 .

[16]  Dana Kulic,et al.  Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains , 2008, Int. J. Robotics Res..

[17]  Weihua Sheng,et al.  Imitation learning of hand gestures and its evaluation for humanoid robots , 2010, The 2010 IEEE International Conference on Information and Automation.

[18]  ChangHwan Kim,et al.  Human-like Arm Motion Generation for Humanoid Robots Using Motion Capture Database , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Michael J. Black,et al.  Representing cyclic human motion using functional analysis , 2005, Image Vis. Comput..

[20]  M. Urban,et al.  Recognition of arm gestures using multiple orientation sensors: repeatability assessment , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[21]  Frank Chongwoo Park,et al.  Natural Movement Generation Using Hidden Markov Models and Principal Components , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[22]  Ales Ude,et al.  Motion imitation and recognition using parametric hidden Markov models , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[23]  Yoshihiko Nakamura,et al.  Humanoid Robot's Autonomous Acquisition of Proto-Symbols through Motion Segmentation , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[24]  Aude Billard,et al.  Goal-Directed Imitation in a Humanoid Robot , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[25]  Shahrokh Valaee,et al.  Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[26]  Dana Kulic,et al.  Discriminative functional analysis of human movements , 2013, Pattern Recognit. Lett..

[27]  Behzad Dariush,et al.  Online and markerless motion retargeting with kinematic constraints , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Yoshihiko Nakamura,et al.  Motion capture based human motion recognition and imitation by direct marker control , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[29]  Yoshihiko Nakamura,et al.  Imitation and primitive symbol acquisition of humanoids by the integrated mimesis loop , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[30]  Stefano Caselli,et al.  Trajectory clustering and stochastic approximation for robot programming by demonstration , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[31]  Andrew T. Irish,et al.  Trajectory Learning for Robot Programming by Demonstration Using Hidden Markov Model and Dynamic Time Warping , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Joris De Schutter,et al.  Recognition of 6 DOF rigid body motion trajectories using a coordinate-free representation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[33]  Shahrokh Valaee,et al.  A Novel Accelerometer-based Gesture Recognition System by , 2010 .

[34]  James O. Ramsay,et al.  Functional Data Analysis , 2005 .

[35]  Odest Chadwicke Jenkins,et al.  Interactive Human Pose and Action Recognition Using Dynamical Motion Primitives , 2007, Int. J. Humanoid Robotics.

[36]  Aude Billard,et al.  Stochastic gesture production and recognition model for a humanoid robot , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[37]  Jun Tani,et al.  On-line imitative interaction with a humanoid robot using a mirror neuron model , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[38]  Maja J. Mataric,et al.  Generating and recognizing free-space movements in humanoid robots , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[39]  Jessica K. Hodgins,et al.  Action capture with accelerometers , 2008, SCA '08.

[40]  Bernt Schiele,et al.  A new approach to enable gesture recognition in continuous data streams , 2008, 2008 12th IEEE International Symposium on Wearable Computers.

[41]  Maja J. Mataric,et al.  Performance-Derived Behavior Vocabularies: Data-Driven Acquisition of Skills from Motion , 2004, Int. J. Humanoid Robotics.

[42]  Paulo Menezes,et al.  A single camera motion capture system dedicated to gestures imitation , 2005, 5th IEEE-RAS International Conference on Humanoid Robots, 2005..

[43]  Martin A. Giese,et al.  On the Representation, Learning and Transfer of Spatio-Temporal Movement Characteristics , 2003, Int. J. Humanoid Robotics.

[44]  Jizhou Sun,et al.  A Motion Retargeting Method for Topologically Different Characters , 2009, 2009 Sixth International Conference on Computer Graphics, Imaging and Visualization.

[45]  Panos E. Trahanias,et al.  Gesture recognition based on arm tracking for human-robot interaction , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[46]  ZhiDong Xiao,et al.  Control of motion in character animation , 2004 .

[47]  Samsu Sempena,et al.  Human action recognition using Dynamic Time Warping , 2011, Proceedings of the 2011 International Conference on Electrical Engineering and Informatics.

[48]  Weihua Sheng,et al.  Imitation learning of arm gestures in presence of missing data for humanoid robots , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[49]  N. Tran AN INTRODUCTION TO THEORETICAL PROPERTIES OF FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS , 2008 .