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Classifying Upper Limb Activities Using Deep Neural Networks

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Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

This paper presents a classification method using Inertial Measurement Unit (IMU) in order to classify six human upper limb activities. The study was also carried out to investigate whether theses activities are being performed normally or abnormally using two different neural networks: Artificial neural network (ANN) and convolutional neural network (CNN). Human activities that were included in the study: arm flexion and extension, arm pronation and supination, shoulder internal and external rotations. Before activities categorization, training data was obtained by the means of an IMU sensor fixed on an armband worn around the forearm. The training data obtained were positions, velocities, accelerations and jerks around x, y and z axes. Training samples of 264 have been collected from 10 participants, 2 women and 8 men from ages 19 to 23. Then, 204 features were extracted from IMU data, nonetheless, 15 features only have been used as inputs to the proposed neural networks because they were the most distinguished ones. After all, the networks classify the data into one of 6 classes and their results were compared. Furthermore, these proposed methods of classification have been validated by real experiments showing that ANN network gives the best performance.

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References

  1. Anter, A.M., Azar, A.T., Fouad, K.M.: Intelligent hybrid approach for feature selection. In: Hassanien, A.E., Azar, A.T., Gaber, T., Bhatnagar, R., Tolba, M.F. (eds.) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2019), pp. 71–79. Springer, Cham (2020)

    Google Scholar 

  2. Arif, M., Kattan, A.: Physical activities monitoring using wearable acceleration sensors attached to the body. PLoS ONE 10(7), e0130851 (2015)

    Article  Google Scholar 

  3. Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 34, 450–457 (2014)

    Article  Google Scholar 

  4. Bhattacharya, A., McCutcheon, E., Shvartz, E., Greenleaf, J.: Body acceleration distribution and O2 uptake in humans during running and jumping. J. Appl. Physiol. 49(5), 881–887 (1980)

    Article  Google Scholar 

  5. Card, R.K., Lowe, J.B.: Anatomy, shoulder and upper limb, elbow joint. In: StatPearls [Internet]. StatPearls Publishing (2018)

    Google Scholar 

  6. Chen, C., Jafari, R., Kehtarnavaz, N.: UTD-MHAD: a multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 168–172. IEEE (2015)

    Google Scholar 

  7. Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimedia Tools Appl. 76(3), 4405–4425 (2017)

    Article  Google Scholar 

  8. Chen, Y., Xue, Y.: A deep learning approach to human activity recognition based on single accelerometer. In: 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1488–1492. IEEE (2015)

    Google Scholar 

  9. Dehzangi, O., Taherisadr, M., ChangalVala, R.: IMU-based gait recognition using convolutional neural networks and multi-sensor fusion. Sensors 17(12), 2735 (2017)

    Article  Google Scholar 

  10. Ganesan, J., Inbarani, H.H., Azar, A.T., Polat, K.: Tolerance rough set firefly-based quick reduct. Neural Comput. Appl. 28(10), 2995–3008 (2017)

    Article  Google Scholar 

  11. Hammerla, N.Y., Halloran, S., Plötz, T.: Deep, convolutional, and recurrent models for human activity recognition using wearables. arXiv preprint arXiv:160408880 (2016)

  12. Hannah Inbarani, H., Nizar Banu, P.K., Azar, A.T.: Feature selection using swarm-based relative reduct technique for fetal heart rate. Neural Comput. Appl. 25(3), 793–806 (2014)

    Article  Google Scholar 

  13. Ignatov, A.: Real-time human activity recognition from accelerometer data using Convolutional Neural Networks. Appl. Soft Comput. 62, 915–922 (2018)

    Article  Google Scholar 

  14. Ijjina, E.P., Chalavadi, K.M.: Human action recognition using genetic algorithms and convolutional neural networks. Pattern Recogn. 59, 199–212 (2016)

    Article  Google Scholar 

  15. Inbarani, H.H., Bagyamathi, M., Azar, A.T.: A novel hybrid feature selection method based on rough set and improved harmony search. Neural Comput. Appl. 26(8), 1859–1880 (2015)

    Article  Google Scholar 

  16. Iosa, M., Picerno, P., Paolucci, S., Morone, G.: Wearable inertial sensors for human movement analysis. Expert Rev. Med. Dev. 13(7), 641–659 (2016)

    Article  Google Scholar 

  17. Jothi, G., Inbarani, H.H., Azar, A.T., Devi, K.R.: Rough set theory with Jaya optimization for acute Lymphoblastic Leukemia classification. Neural Comput. Appl. 31(9), 5175–5194 (2019)

    Article  Google Scholar 

  18. Kaghyan, S., Sarukhanyan, H.: Activity recognition using k-nearest neighbor algorithm on smartphone with tri-axial accelerometer. Int. J. Inform. Models Anal. (IJIMA) 1, 146–156 (2012)

    Google Scholar 

  19. Lee, S.M., Yoon, S.M., Cho, H.: Human activity recognition from accelerometer data using convolutional neural network. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 131–134. IEEE (2017)

    Google Scholar 

  20. Mannini, A., Intille, S.S., Rosenberger, M., Sabatini, A.M., Haskell, W.: Activity recognition using a single accelerometer placed at the wrist or ankle. Med. Sci. Sports Exerc. 45(11), 2193 (2013)

    Article  Google Scholar 

  21. Mocanu, D.C., Ammar, H.B., Lowet, D., Driessens, K., Liotta, A., Weiss, G., Tuyls, K.: Factored four way conditional restricted Boltzmann machines for activity recognition. Pattern Recogn. Lett. 66, 100–108 (2015)

    Article  Google Scholar 

  22. Nam, H.S., Lee, W.H., Seo, H.G., Kim, Y.J., Bang, M.S., Kim, S.: Inertial measurement unit based upper extremity motion characterization for action research arm test and activities of daily living. Sensors 19(8), 1782 (2019)

    Article  Google Scholar 

  23. Norkin, C.C., White, D.J.: Measurement of Joint Motion: A Guide to Goniometry. FA Davis, Philadelphia (2016)

    Google Scholar 

  24. Sayed, G.I., Hassanien, A.E., Azar, A.T.: Feature selection via a novel chaotic crow search algorithm. Neural Comput. Appl. 31(1), 171–188 (2019)

    Article  Google Scholar 

  25. Seshadri, D.R., Li, R.T., Voos, J.E., Rowbottom, J.R., Alfes, C.M., Zorman, C.A., Drummond, C.K.: Wearable sensors for monitoring the internal and external workload of the athlete. NPJ Digit. Med. 2(1), 1–18 (2019)

    Article  Google Scholar 

  26. Sousa Lima, W., Souto, E., El-Khatib, K., Jalali, R., Gama, J.: Human activity recognition using inertial sensors in a smartphone: an overview. Sensors 19(14), 3213 (2019)

    Article  Google Scholar 

  27. Subramanian, K., Suresh, S.: Human action recognition using meta-cognitive neuro-fuzzy inference system. Int. J. Neural Syst. 22(06), 1250028 (2012)

    Article  Google Scholar 

  28. Too, J., Abdullah, A.R., Mohd Saad, N., Tee, W.: EMG feature selection and classification using a Pbest-guide binary particle swarm optimization. Computation 7(1), 12 (2019)

    Article  Google Scholar 

  29. Too, J., Abdullah, A.R., Saad, N.M.: Classification of hand movements based on discrete wavelet transform and enhanced feature extraction. Int. J. Adv. Comput. Sci. Appl. 10(6), 83–89 (2019)

    Google Scholar 

  30. Yu, H., Cang, S., Wang, Y.: A review of sensor selection, sensor devices and sensor deployment for wearable sensor-based human activity recognition systems. In: 2016 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA), pp. 250–257. IEEE (2016)

    Google Scholar 

  31. Zhao, H., Liu, Z.: Human action recognition based on non-linear SVM decision tree. J. Comput. Inf. Syst. 7(7), 2461–2468 (2011)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Robotics and Internet of Things Lab, Prince Sultan University, Riyadh, Saudi Arabia

    Ahmad Taher Azar

  2. Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Smart Engineering Systems Research Center (SESC), Nile University, Sheikh Zayed City, 12588, Giza, Egypt

    Hassan Ashraf Elkholy, Ahmed Magd, Hagar Marzouk & Hossam Hassan Ammar

  4. School of Engineering and Applied Sciences, Nile University Campus, Sheikh Zayed District, Juhayna Square, 6th of October City, 12588, Giza, Egypt

    Hassan Ashraf Elkholy, Ahmed Magd, Hagar Marzouk & Hossam Hassan Ammar

Authors
  1. Hassan Ashraf Elkholy
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  2. Ahmad Taher Azar
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  3. Ahmed Magd
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  4. Hagar Marzouk
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  5. Hossam Hassan Ammar
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Corresponding author

Correspondence to Ahmad Taher Azar .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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Elkholy, H.A., Azar, A.T., Magd, A., Marzouk, H., Ammar, H.H. (2020). Classifying Upper Limb Activities Using Deep Neural Networks. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_26

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