Depth-Sensor-Based Monitoring of Therapeutic Exercises

In this paper, we propose a self-organizing feature map-based (SOM) monitoring system which is able to evaluate whether the physiotherapeutic exercise performed by a patient matches the corresponding assigned exercise. It allows patients to be able to perform their physiotherapeutic exercises on their own, but their progress during exercises can be monitored. The performance of the proposed the SOM-based monitoring system is tested on a database consisting of 12 different types of physiotherapeutic exercises. An average 98.8% correct rate was achieved.

[1]  L. R. Rabiner,et al.  An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition , 1983, The Bell System Technical Journal.

[2]  Anthony Kuh,et al.  A combined self-organizing feature map and multilayer perceptron for isolated word recognition , 1992, IEEE Trans. Signal Process..

[3]  Jun-Da Huang Kinerehab: a kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities , 2011, ASSETS '11.

[4]  Mike Paterson,et al.  Longest Common Subsequences , 1994, MFCS.

[5]  Erkki Oja,et al.  Engineering applications of the self-organizing map , 1996, Proc. IEEE.

[6]  Yao-Jen Chang,et al.  A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. , 2011, Research in developmental disabilities.

[7]  Anil K. Jain,et al.  A nonlinear projection method based on Kohonen's topology preserving maps , 1992, IEEE Trans. Neural Networks.

[8]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[9]  Horst-Michael Groß,et al.  A Hybrid Stochastic-Connectionist Approach to Gesture Recognition , 2000, Int. J. Artif. Intell. Tools.

[10]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[11]  Dimitrios Gunopulos,et al.  Indexing Large Human-Motion Databases , 2004, VLDB.

[12]  Meinard Müller,et al.  Motion templates for automatic classification and retrieval of motion capture data , 2006, SCA '06.

[13]  Anil K. Jain,et al.  Artificial neural networks for feature extraction and multivariate data projection , 1995, IEEE Trans. Neural Networks.

[14]  Ross Yeager An Automated Physiotherapy Exercise Generator , 2013 .

[15]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[16]  Tobias Schreck,et al.  MotionExplorer: Exploratory Search in Human Motion Capture Data Based on Hierarchical Aggregation , 2013, IEEE Transactions on Visualization and Computer Graphics.

[17]  Tim Lüth,et al.  A measurement device for motion analysis of patients with Parkinson's disease using sensor based smart clothes , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[18]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[19]  Hans-Peter Seidel,et al.  Efficient and Robust Annotation of Motion Capture Data , 2009 .

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

[21]  Dan Morris,et al.  RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises , 2014, CHI.

[22]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Philippe Beaudoin,et al.  Motion-motif graphs , 2008, SCA '08.

[24]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[25]  Wen Gao,et al.  Large vocabulary sign language recognition based on fuzzy decision trees , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[26]  Reinhard Klein,et al.  Efficient unsupervised temporal segmentation of human motion , 2014, SCA '14.

[27]  Mu-Chun Su,et al.  Fast self-organizing feature map algorithm , 2000, IEEE Trans. Neural Networks Learn. Syst..

[28]  Raoul Wessel,et al.  Action graph a versatile data structure for action recognition , 2014, 2014 International Conference on Computer Graphics Theory and Applications (GRAPP).

[29]  Adrian Hilton,et al.  A survey of advances in vision-based human motion capture and analysis , 2006, Comput. Vis. Image Underst..

[30]  Meinard Müller,et al.  Efficient content-based retrieval of motion capture data , 2005, SIGGRAPH '05.

[31]  Pierre Lanchantin,et al.  Unsupervised restoration of hidden nonstationary Markov chains using evidential priors , 2005, IEEE Transactions on Signal Processing.

[32]  Arno Zinke,et al.  Fast local and global similarity searches in large motion capture databases , 2010, SCA '10.

[33]  Sameer Singh,et al.  Video analysis of human dynamics - a survey , 2003, Real Time Imaging.

[34]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[35]  Wen Gao,et al.  A Chinese sign language recognition system based on SOFM/SRN/HMM , 2004, Pattern Recognit..

[36]  Rémi Ronfard,et al.  A survey of vision-based methods for action representation, segmentation and recognition , 2011, Comput. Vis. Image Underst..

[37]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[38]  Ronald Poppe,et al.  Vision-based human motion analysis: An overview , 2007, Comput. Vis. Image Underst..