ارائه یک مدل فراابتکاری برای تشخیص ریزحرکات انسان مبتنی بر حس‌گرهای اینرسی

نویسندگان

دانشکده فنی و مهندسی - دانشگاه رازی

چکیده

پیشرفت اخیر حس‌گرهای اینرسی مبتنی بر فناوری سیستم‌های میکرو الکترومکانیکی امکان طراحی دستگاه‌های پوشیدنی را برای تشخیص خودکار حرکات انسان فراهم نموده است. رهگیری حرکات طبیعی بدن در تعامل با محیط، نیازمند پردازش سیگنال‌های اینرسی در سطح جزئی است. این رهگیری به کمک تشخیص حرکات کوتاه و پیوسته انسان انجام می‌شود. در این مقاله روشی برای تشخیص ریزحرکات پیوسته انسان برمبنای پردازش سیگنال‌های اینرسی معرفی شده است. در این روش، ابتدا با استفاده از الگوریتم‌های ناوبری اینرسی، سیگنال شتاب خطی و جاذبه زمین محاسبه می‌شود؛ سپس با ترکیب این دو سیگنال، ویژگی‌های متمایزکننده استخراج می‌شود. نوآوری این مقاله برای تشخیص ریزحرکات پیوسته معرفی یک مدل طبقه‌بندی جدید تحت عنوان مدل‌های شرطی است. هر مدل متعلق به یک کلاس ریزحرکت است که عملیاتی را به‌منظور تشکیل یک مجموعه عبارت منطقی و طبقه‌بندی نمونه‌های آن کلاس خاص انجام می‌دهد. در روش معرفی شده، به‌منظور پیدا کردن عبارت‌های منطقی بهینه برای هر مدل، از بهینه‌سازی ازدحام ذرات (Particle Swarm Optimization) استفاده شده است. به‌منظور ارزیابی، روش پیشنهادی برای تشخیص ریزحرکات پیوسته نماز مورد آزمایش قرار گرفته است. اجرای الگوریتم بر روی جامعه آماری از نمازگزاران و مقایسه آن با مدل‌های مرسوم طبقه‌بندی ریزحرکات پیوسته، نشان از درستی و تشخیص بالای روش پیشنهادی دارد.

کلیدواژه‌ها


عنوان مقاله [English]

A Meta-heuristic Model for Human Micro Movements Recognition Based on Inertial Sensors

نویسندگان [English]

  • M. Sepahvand
  • F. Abdali-Mohammadi
Faculty of Engineering, Razi University, Kermanshah, Iran
چکیده [English]

Current developments in inertial sensors based on Microelectromechanical Systems technology allows us to design wearable devices for human movements automatic detection. Detection of human movements in natural environments needs detailed inertial signals processing. These activities are detected using human short movements detection. In this paper a method is proposed for continuous human tiny movements based on inertial signal processing. In the proposed method, at first linear acceleration and earth gravity signals are calculated using inertial navigation algorithms. Then discriminant features are extracted using a combination of these to signals. Innovation of this paper is introducing a new classification algorithm for continuous tiny movements recognition named Conditional Models. Each model belongs to a class of micro movement which performs some operations for generating a logical expression set and classifying the samples in that class. The proposed method uses the Particle Swarm Optimization to finding the optimized logical expression for each model. In order to evaluating, this method is tested on prayer micro movements recognition. Running the algorithm on the population of prayers and comparing with well-known micro movements classification models demonstrates the accuracy and high recognition of the proposed method.

کلیدواژه‌ها [English]

  • Particle swarm optimization
  • inertial sensors
  • micro movements
  • classification
  • conditional models
[1] رمضان هاونگی، «موقعیت‌یابی ربات بر اساس فیلتر ذره‌ای بهبود یافته با فیلتر کالمن گروهی هوشمند و گام MCMC»، مجله مهندسی برق دانشگاه تبریز، دوره 46، شماره 4، 1395.
[2] J. Fontecha et al., "Elderly frailty detection by using accelerometer-enabled smartphones and clinical information records," Personal and Ubiquitous Computing, vol. 17, no. 6, pp. 1073-1083, 2013.
[3] D. A. James, "The application of inertial sensors in elite sports monitoring," in the Engineering of Sport 6: Volume 3: Developments for Innovation, Springer New York, pp. 289-294, 2006.
[4] P. Gupta and T. Dallas, "Feature selection and activity recognition system using a single triaxial accelerometer," IEEE Transactions on Biomedical Engineering, vol. 61, no. 6, pp. 1780-1786, 2014.
[5] J. S. Wang and F.C. Chuang, "An accelerometer-based digital pen with a trajectory recognition algorithm for handwritten digit and gesture recognition," IEEE Transactions on Industrial Electronics, vol. 59, no. 7, pp. 2998-3007, 2012.
[6] O. D. Lara and M.A. Labrador, "A survey on human activity recognition using wearable sensors," IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1192-1209, 2013.
[7] A. Wang et al., "A comparative study on human activity recognition using inertial sensors in a smartphone," IEEE Sensors Journal, vol. 16, no. 11, pp. 4566-4578, 2016.
[8] O. D. Lara et al., "Centinela: A human activity recognition system based on acceleration and vital sign data," Pervasive and Mobile Computing, vol. 8, no. 5, pp. 717-729, 2012.
[9] J. Xu et al., "Personalized active learning for activity classification using wireless wearable sensors," IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 5, pp. 865-876, 2016.
[10] A. Bulling, U. Blanke and B. Schiele, "A tutorial on human activity recognition using body-worn inertial sensors," ACM Computing Surveys, vol. 46, no. 3, pp. 1-33, 2014.
[11] S. J. Preece et al., "A comparison of feature extraction methods for the classification of dynamic activities from accelerometer Data," IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 871-879, 2009.
[12] D. Fuentes et al., "Online motion recognition using an accelerometer in a mobile device," Expert Systems with Applications, vol. 39, no. 3, pp. 2461-2465, 2012.
[13] A. M. Khan et al., "A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer," IEEE Transactions on Information Technology in Biomedicine, vol. 14, no. 5, pp. 1166-1172, 2010.
[14] L. Tong, Q. Song, Y. Ge and M. Liu, "HMM-based human fall detection and prediction method using tri-axial accelerometer," IEEE Sensors Journal, vol. 13, no. 5, 1849-1856, 2013.
[15] L. Kai-Chun and C. Chia-Tai, "Significant change spotting for periodic human motion segmentation of cleaning tasks using wearable sensors," Sensors, vol. 17, no.1, 2017.
[16] H. Junker, O. Amft, P. Lukowicz, G. Troster, "gesture spotting with body-worn inertial sensors to detect user activities," pattern Recognition, vol. 41, no. 6, pp. 2010-2024, 2008.
[17] J. L. Reyes Ortiz et al., "Transition-aware human activity recognition using smartphones," Neurocomputing, vol. 171, pp. 754-767, 2016.
[18] G. Panahandeh, N. Mohammadiha, A. Leijon and P. Händel, "Continuous hidden markov model for pedestrian activity classification and gait analysis," IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 5, pp. 1073-1083, 2013.
[19] H.-K. Lee, J.H. Kim, "An HMM-based threshold model approach for gesture recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 961-973, 1999.
[20] J. S. Wang, C. W. Lin, Y. T. C. Yang and Y. J. Ho, "walking pattern classification and walking distance estimation algorithms using gait phase information," IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2884-2892, 2012.
[21] Z. Syed, P. Aggarwal, C. Goodall, X. Niu, and N. El-Sheimy, "A new multi-position calibration method for MEMS inertial a navigation systems," Meas. Sci. Technol, vol. 18, no. 7, pp. 1897–1907, 2007.
[22] M. J. Caruso, "Applications of magnetoresistive sensors in navigation systems," Sensors and Actuators, SAE SP-1220, pp. 15–21, 1997.
[23] M. Pedley, "High-Precision Calibration of a Three-Axis Accelerometer," document AN4399, Freescale Semicond., Austin, TX, USA, 2015.
[24] S. O. H. Madgwick, A. J. L. Harrison and R. Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” in Proceedings of the IEEE International Conference on in Rehabilitation Robotics, 2011, pp. 1-7.
[25] H. J. Luinge, P. H. Veltink and C. T. Baten, “Estimating orientation with gyroscopes and accelerometers,” Technology and Health Care, vol. 7, no. 6, pp. 455-459, 1999.
[26] J. L. Marins, X. Yun, E. R. Bachmann, R. B. McGhee and M. J. Zyda, “An extended kalman filter for quaternion-based orientation estimation using marg sensors,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001, pp. 2003-2011.
[27] J. Luinge and H. P. Veltink, "Measuring orientation of human body segments using miniature gyroscopes and accelerometers," Medical and Biological Engineering and Computing, vol. 43, no. 2 p. 273-282, 2005.
[28] M. Haid and J. Breitenbach, “Low cost inertial orientation tracking with Kalman filter,” Applied Mathematics and Computation, vol. 153, no. 2, pp. 567-575, 2004.
[29] M. Sepahvand, F. Abdali-Mohammadi and F. Mardukhi, "Evolutionary Metric-Learning-Based Recognition Algorithm for Online Isolated Persian/Arabic Characters, Reconstructed Using Inertial Pen Signals," IEEE Transactions on Cybernetics, vol. PP, no.99, pp. 1-13, 2016.
[30] N. Wang, E. Ambikairajah, N. H. Lovell and B. G. Celler, “Accelerometry based classification of walking patterns using time-frequency analysis,” in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4899–4902, 2007.
[31] M. Sekine, T. Tamura, M. Akay, T. Fujimoto, T. Togawa and Y. Fukui, “Discrimination of walking patterns using wavelet-based fractal analysis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 10, no. 3, pp. 188-196, 2002.
[32] M. S. H. Aung et al., “Automated Detection of Instantaneous Gait Events Using Time Frequency Analysis and Manifold Embedding,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 6, pp. 908-916, 2013.
[33] J. Kennedy and R. C. Eberhart, "Particle swarm optimization,"  IEEE Intenational Conference on Neural Networks, 1995, pp. 1942-1948.
[34] سیدمحمدرضا موسوی، محمد خویشه، احسان ابراهیمی، فلاح محمدزاده، «دسته‌بندی اهداف سوناری توسط الگوریتم بهینه‌ساز ازدحام ذرات با گروه‌های مستقل»، مجله مهندسی برق دانشگاه تبریز، دوره 47، شماره 1، 1396.
[35] A. Wang, G. Chen, J. Yang, S. Zhao and C. Y. Chang, "A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone," in IEEE Sensors Journal, vol. 16, no. 11, pp. 4566-4578, 2016.