On-Line Detection and Segmentation of Sports Motions Using a Wearable Sensor †

In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs. The evaluation on two types of motions (soccer kicking and two-handed ball throwing) verifies that the proposed method is successful for the accurate detection and segmentation of sports motions. By developing a sports motion analysis system using the motion model and the sequence classifier, we show that the proposed method is useful for observation of sports motions by automatically providing relevant motion data for analysis.

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Gérard G. Medioni,et al.  Structured Time Series Analysis for Human Action Segmentation and Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[5]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[6]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[7]  Chris Button,et al.  A Review of Vision-Based Motion Analysis in Sport , 2008, Sports medicine.

[8]  A. Lees.  Technique analysis in sports: a critical review , 2002, Journal of sports sciences.

[9]  Lichao Zhang,et al.  A Kinect based Golf Swing Score and Grade System using GMM and SVM , 2012, 2012 5th International Congress on Image and Signal Processing.

[10]  John Salvatier,et al.  Theano: A Python framework for fast computation of mathematical expressions , 2016, ArXiv.

[11]  Ryan P. Adams,et al.  The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM , 2016, ICML.

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

[13]  Seongmin Baek,et al.  Motion Capture of the Human Body Using Multiple Depth Sensors , 2017 .

[14]  Adri Hartveld MSc Mcsp.  Qualitative Analysis of Human Movement , 1998 .

[15]  Tim J Gabbett,et al.  The Use of Wearable Microsensors to Quantify Sport-Specific Movements , 2015, Sports Medicine.

[16]  Bernt Schiele,et al.  A tutorial on human activity recognition using body-worn inertial sensors , 2014, CSUR.

[17]  S. Backus,et al.  Differences between sexes in lower extremity alignment and muscle activation during soccer kick. , 2010, The Journal of bone and joint surgery. American volume.

[18]  L. Bergroth,et al.  A survey of longest common subsequence algorithms , 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000.

[19]  Yoshua Bengio,et al.  Gated Feedback Recurrent Neural Networks , 2015, ICML.

[20]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[21]  S. Sakurai,et al.  Three-dimensional kinetic analysis of side-foot and instep soccer kicks. , 2002, Medicine and science in sports and exercise.

[22]  Jürgen Schmidhuber,et al.  Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks , 2007, NIPS.

[23]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[24]  Angelo M. Sabatini,et al.  A hidden Markov model-based technique for gait segmentation using a foot-mounted gyroscope , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[26]  Sepp Hochreiter,et al.  Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.

[27]  Yongdong Zhang,et al.  Automatic Detection and Analysis of Player Action in Moving Background Sports Video Sequences , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[28]  Didier Stricker,et al.  LSTM-Based Early Recognition of Motion Patterns , 2014, 2014 22nd International Conference on Pattern Recognition.

[29]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[30]  Ling Shao,et al.  Deep Dynamic Neural Networks for Multimodal Gesture Segmentation and Recognition , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[32]  Pavlo Molchanov,et al.  Online Detection and Classification of Dynamic Hand Gestures with Recurrent 3D Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[33]  Myunggyu Kim,et al.  Sports motion analysis system using wearable sensors and video cameras , 2017, 2017 International Conference on Information and Communication Technology Convergence (ICTC).

[34]  Yoshua Bengio,et al.  Attention-Based Models for Speech Recognition , 2015, NIPS.

[35]  Gregory D. Hager,et al.  Temporal Convolutional Networks for Action Segmentation and Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[36]  Duane Knudson,et al.  Qualitative biomechanical principles for application in coaching , 2007, Sports biomechanics.

[37]  Chao Xu,et al.  TennisMaster: an IMU-based online serve performance evaluation system , 2017, AH.

[38]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.