Arm motion analysis using genetic algorithm for rehabilitation and healthcare

Abstract The worlds population is quickly aging. With an aging society, an increase in patients with brain damage is predicted. In rehabilitation, the analysis of arm motion is vital as various day to day activities relate to arm movements. The therapeutic approach and evaluation method are generally selected by therapists based on his/her experience, which can be an issue for quantitative evaluation in any specific movement task. In this paper, we develop a measurement system for arm motion analysis using a 3D image sensor. The method of upper body posture estimation based on a steady-state genetic algorithm (SSGA) is proposed. A continuous model of generation for an adaptive search in dynamical environment using an adaptive penalty function and island model is applied. Experimental results indicate promising results as compared with the literature.

[1]  Takenori Obo,et al.  Joint angle estimation system for rehabilitation evaluation support , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[2]  Linda Denehy,et al.  Validity of the Microsoft Kinect for assessment of postural control. , 2012, Gait & posture.

[3]  F. Nouri,et al.  An extended activities of daily living scale for stroke patients , 1987 .

[4]  Steve J. Brown,et al.  Developing a well-being monitoring system - Modeling and data analysis techniques , 2006, Appl. Soft Comput..

[5]  Huosheng Hu,et al.  Human motion tracking for rehabilitation - A survey , 2008, Biomed. Signal Process. Control..

[6]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[7]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[8]  Matthew D. Lichter,et al.  Assessing Upper Extremity Motor Function in Practice of Virtual Activities of Daily Living , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Enrique Alba,et al.  A survey of parallel distributed genetic algorithms , 1999 .

[10]  Adso Fernández-Baena,et al.  Biomechanical Validation of Upper-Body and Lower-Body Joint Movements of Kinect Motion Capture Data for Rehabilitation Treatments , 2012, 2012 Fourth International Conference on Intelligent Networking and Collaborative Systems.

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

[12]  Subbarao Kambhampati,et al.  Evolutionary Computing , 1997, Lecture Notes in Computer Science.

[13]  Ahmad Lotfi,et al.  Behavioural pattern identification and prediction in intelligent environments , 2013, Appl. Soft Comput..

[14]  Ayanna M. Howard,et al.  Quantitative evaluation of the Microsoft KinectTM for use in an upper extremity virtual rehabilitation environment , 2013, 2013 International Conference on Virtual Rehabilitation (ICVR).

[15]  D. Wade,et al.  The Barthel ADL Index: a reliability study. , 1988, International disability studies.

[16]  F. Zuher,et al.  Recognition of Human Motions for Imitation and Control of a Humanoid Robot , 2012, 2012 Brazilian Robotics Symposium and Latin American Robotics Symposium.

[17]  Ruzena Bajcsy,et al.  Remote Health Coaching System and Human Motion Data Analysis for Physical Therapy with Microsoft Kinect , 2015, ArXiv.

[18]  Abdulhamit Subasi,et al.  Classification of EMG signals using combined features and soft computing techniques , 2012, Appl. Soft Comput..

[19]  Kevin Kam Fung Yuen,et al.  The Primitive Cognitive Network Process in healthcare and medical decision making: Comparisons with the Analytic Hierarchy Process , 2014, Appl. Soft Comput..

[20]  U. Wyss,et al.  Review of arm motion analyses , 2000, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[21]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[22]  Ángel Carmona Poyato,et al.  Comparing evolutionary algorithms and particle filters for Markerless Human Motion Capture , 2014, Appl. Soft Comput..

[23]  Yangsheng Xu,et al.  A real-time human imitation system , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[24]  Juan Manuel Ibarra Zannatha,et al.  Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation , 2013, Comput. Methods Programs Biomed..

[25]  Henry Been-Lirn Duh,et al.  A Wearable Sensing System for Tracking and Monitoring of Functional Arm Movement , 2011, IEEE/ASME Transactions on Mechatronics.

[26]  Francisco Javier Díaz Pernas,et al.  A Kinect-based system for cognitive rehabilitation exercises monitoring , 2014, Comput. Methods Programs Biomed..

[27]  Timo Mantere,et al.  Evolutionary software engineering, a review , 2005, Appl. Soft Comput..

[28]  Chuan-Jun Su,et al.  Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic , 2014, Appl. Soft Comput..

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

[30]  Chun Chen,et al.  Whole-body humanoid robot imitation with pose similarity evaluation , 2015, Signal Process..

[31]  Naoyuki Kubota,et al.  A novel multimodal communication framework using robot partner for aging population , 2015, Expert Syst. Appl..

[32]  Hong Wei,et al.  A survey of human motion analysis using depth imagery , 2013, Pattern Recognit. Lett..

[33]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[34]  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.

[35]  B Bonnechère,et al.  Validity and reliability of the Kinect within functional assessment activities: comparison with standard stereophotogrammetry. , 2014, Gait & posture.

[36]  Gracián Triviño,et al.  A tool for linguistic assessment of rehabilitation exercises , 2014, Appl. Soft Comput..

[37]  Enrique J. Gómez,et al.  Data mining applied to the cognitive rehabilitation of patients with acquired brain injury , 2013, Expert Syst. Appl..

[38]  TeichriebVeronica,et al.  Motor Rehabilitation Using Kinect: A Systematic Review , 2015 .