Classification of Walkers Based on Back Angle Measurements Using Wireless Sensor Node

  • Ramandeep Singh ChowdharyEmail author
  • Mainak Basu
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)


High end technology enabled devices are being used these days to perform classification and analysis of walking styles of athletes and patients for therapeutic applications. Hence it has become an encouraging step to carry out research in related domain. Various sports have significant health benefits which contribute to muscular, heart, and mental health. However, there is high risk of having injuries while playing outdoor sports and are very common in athletes. Relative excessive loading and impulsive impact on the muscle tissues causes almost all type of basic and severe injuries. To study the phenomenon of injury occurrence, avoidance, improvement in training techniques, and therapeutic applications, an open source electronic device has been fabricated using micro-controller and gy-521 sensor module. The designed system was used to study the effect of lower back movement of persons while walking and was able to classify subjects based on the lower back deviation angle. This result shall form the basis of designing customized training sessions suited for athletes to minimize injuries and suggesting of physiotherapy for patients with lower back pain. The designed system can be used as a reliable evaluation device for lower back analysis in various field environments without any constraints. The device could support injury management, performance enhancement, and rehabilitation of lower back pain patients.


Gyroscope Lower back analysis MPU6050 Sports analytics Wearable sensors 



I would like to mention that a non-invasive wearable belt was designed and fabricated for the purpose of performing experiments. Because of the non-invasive nature of the belt no ethical committee was formed by the University but informed consent was taken from all participants before starting the experiment. However information with regards to participant’s confidentiality has been maintained and has not been promoted online or in any other form for any other purpose.


  1. 1.
    Bertoli, M., Cereatti, A., Croce, U.D., Mancini, M.: An objective assessment to investigate the impact of turning angle on freezing of gait in Parkinsons disease. In: Proceedings of IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4, 19–21 October 2017Google Scholar
  2. 2.
    Chong, E., Choi, T., Kim, H., Kim, S.J., Hwang, Y., Min Lee, J.: Informative sensor selection and learning for prediction of lower limb kinematics using generative stochastic neural networks. In: Proceedings of 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1–4, 11–15 July 2017Google Scholar
  3. 3.
    Kam, W., O’Sullivan, K., Mohammed, W.S., Lewis, E.: Low cost portable sensor for real-time monitoring of lower back bending. In: Proceedings of SPIE, 25th International Conference on Optical Fiber Sensors (OFS), pp. 1–4, 24–28 April 2017Google Scholar
  4. 4.
    Esfahani, M.I.M., Nussbaum, M.A.: A smart undershirt for tracking upper body motions: task classification and angle estimation. IEEE Sens. J. 18(18), 7650–7658 (2018)CrossRefGoogle Scholar
  5. 5.
    Xu, W., Sanchez, C.O., Murray, I.: Measuring human joint movement with IMUs. In: Proceedings of 15th IEEE Student Conference on Research and Development (SCOReD), pp. 172–177, 13–14 December 2017Google Scholar
  6. 6.
    Huynh, T., Schiele, B.: Analyzing features for activity recognition. In: Proceedings of Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context Aware Services: Usage and Technologies, Grenoble, France, pp. 159–163, 12–14 October 2005Google Scholar
  7. 7.
    Sekine, M., Tamura, T., Fujimoto, T., Fukui, Y.: Classification of walking pattern using acceleration waveform in elderly people. In: Proceedings of the 22nd Annual IEEE International Conference of the Engineering in Medicine and Biology Society, Chicago, MI, USA, pp. 1356–1359, 23–28 July 2000Google Scholar
  8. 8.
    Bao, L., Intille, S.: Activity recognition from user-annotated acceleration data. In: Pervasive Computing, pp. 1–17. Springer, Berlin (2004)Google Scholar
  9. 9.
    Preece, S.J., Goulermas, J.Y., Kenney, L.P., Howard, D.: A comparision of feature extraction methods for the classification of dynamic activities from accelerometer data. IEEE Trans. Biomed. Eng. 56, 871–879 (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, N., Ambikairajah, E., Lovell, N.H., Cellar, B.G.: Accelerometry based classification of walking pattern using time frequency analysis. In: Proceedings of 29th Annual International Conference of The IEEE Engineering in Medicine and Biology Society, Lyon, France, pp. 4899–4902, 22–26 August 2007Google Scholar
  11. 11.
    Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: Pattern Recognition, Image Analysis, pp. 289–296 (2011)CrossRefGoogle Scholar
  12. 12.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.: Activity recognition from accelerometer data. In: Proceedings of 7th Conference on Innovative Applications of Artificial Intelligence, Menlo Park, CA, USA, pp. 1541–1546, 9–13 July 2005Google Scholar
  13. 13.
    Parkka, J., Cluitmans, L., Ermes, M.: Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision trees. IEEE Trans. Inf. Technol. Biomed. 14, 1211–1215 (2010)CrossRefGoogle Scholar
  14. 14.
    Maurer, U., Smailagic, A., Siewiorek, D.P., Deisher, M.: Activity recognition and monitoring using multiple sensors on different body positions. In: Proceedings of International Workshop on Wearable and Implantable Body Sensor Networks, Boston, MA, USA, pp. 113–116, 3–5 April 2006Google Scholar
  15. 15.
    Antonsson, E.K., Mann, R.W.: The frequency content of gait. J. Biomech. 18, 39–47 (1985)CrossRefGoogle Scholar
  16. 16.
    Bouten, C.V., Koekkoek, K.T., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Trans. Biomed. Eng. 44, 136–147 (1997)CrossRefGoogle Scholar
  17. 17.
    Gjoreski, H., Gams, M., Chorbev, I.: 3-Axis accelerometer activity recognition. ICT Innov. 51–58 (2010)Google Scholar
  18. 18.
    Pietka, E.: Expert systems in parameter extraction of ECG signal. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, New Orleans, LA, USA, pp. 165–166, 4–7 November 1988Google Scholar
  19. 19.
    Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings of the International Conference on Data Mining, San Jose, CA, USA, pp. 289–296, 29 November–2 December 2001Google Scholar
  20. 20.
    Chu, C.: Time series segmentation: a sliding window approach. Inf. Sci. 85, 147–173 (1995)CrossRefGoogle Scholar
  21. 21.
    Kozina, S., Lustrek, M., Gams, M.: Dynamic signal segmentation for activity recognition. In: Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona, Spain, pp. 15–22, 16–22 July 2011Google Scholar
  22. 22.
    Ortiz, J., Olaya, A.G., Borrajo, D.: A dynamic sliding window approach for activity recognition. In: Proceedings of the International Conference on User Modeling, Adaptation and Personalization, Girona, Spain, pp. 219–230, 11–15 July 2011Google Scholar
  23. 23.
    Nyan, M.N., Tay, F., Seah, K., Sitoh, Y.Y.: Classification of gait patterns in the time-frequency domain. J. Biomech. 39, 2647–2656 (2006)CrossRefGoogle Scholar
  24. 24.
    Van Kasteren, T.L.M., Noulas, A., Englebienne, G., Krose, B.J.: Accurate activity recognition in a home setting. In: Proceedings of the Conference on Autonomous Agents and Multi Agent Systems (AAMAS 2007), Seoul, Korea, pp. 1–9, 21–24 September 2007Google Scholar
  25. 25.
    Patterson, D.J., Fox, D., Kautz, H., Philipose, M.: Fine-grained activity recognition by aggregating abstract object usage. In: Proceedings of International Semantic Web Conference (ISWC), Galway, Ireland, pp. 44–51, 18–21 October 2005Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.GD Goenka UniversityGurgaonIndia

Personalised recommendations