HuGaDB: Human Gait Database for Activity Recognition from Wearable Inertial Sensor Networks

  • Roman Chereshnev
  • Attila Kertész-FarkasEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10716)


This paper presents a human gait data collection for analysis and activity recognition consisting of continues recordings of combined activities, such as walking, running, taking stairs up and down, sitting down, and so on; and the data recorded are segmented and annotated. Data were collected from a body sensor network consisting of six wearable inertial sensors (accelerometer and gyroscope) located on the right and left thighs, shins, and feet. Additionally, two electromyography sensors were used on the quadriceps (front thigh) to measure muscle activity. This database can be used not only for activity recognition but also for studying how activities are performed and how the parts of the legs move relative to each other. Therefore, the data can be used (a) to perform health-care-related studies, such as in walking rehabilitation or Parkinson’s disease recognition, (b) in virtual reality and gaming for simulating humanoid motion, or (c) for humanoid robotics to model humanoid walking. This dataset is the first of its kind which provides data about human gait in great detail. The database is available free of charge


  1. 1.
    Aggarwal, C.C.: Managing and Mining Sensor Data. Springer Science & Business Media, New York (2013). CrossRefGoogle Scholar
  2. 2.
    Amma, C., Georgi, M., Schultz, T.: Airwriting: a wearable handwriting recognition system. Pers. Ubiquit. Comput. 18(1), 191–203 (2014)CrossRefGoogle Scholar
  3. 3.
    Georgi, M., Amma, C., Schultz, T.: Recognizing hand and finger gestures with IMU based motion and EMG based muscle activity sensing. In: Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, pp. 99–108 (2015)Google Scholar
  4. 4.
    Tapia, E.M., Intille, S.S., Lopez, L., Larson, K.: The design of a portable kit of wireless sensors for naturalistic data collection. In: Fishkin, K.P., Schiele, B., Nixon, P., Quigley, A. (eds.) Pervasive 2006. LNCS, vol. 3968, pp. 117–134. Springer, Heidelberg (2006). CrossRefGoogle Scholar
  5. 5.
    Intille, S.S., Larson, K., Beaudin, J., Nawyn, J., Tapia, E.M., Kaushik, P.: A living laboratory for the design and evaluation of ubiquitous computing technologies. In: CHI 2005 Extended Abstracts on Human Factors in Computing Systems, pp. 1941–1944. ACM (2005)Google Scholar
  6. 6.
    Huynh, T., Fritz, M., Schiele, B.: Discovery of activity patterns using topic models. In: Proceedings of the 10th International Conference on Ubiquitous Computing, pp. 10–19. ACM (2008)Google Scholar
  7. 7.
    Pham, C., Olivier, P.: Slice&Dice: recognizing food preparation activities using embedded accelerometers. In: Tscheligi, M., et al. (eds.) European Conference on Ambient Intelligence, AmI 2009. LNCS, vol. 5859, pp. 34–43. Springer, Heidelberg (2009).
  8. 8.
    De la Torre, F., Hodgins, J., Bargteil, A., Martin, X., Macey, J., Collado, A., Beltran, P.: Guide to the Carnegie Mellon University multimodal activity (CMU-MMAC) database, p. 135. Robotics Institute (2008)Google Scholar
  9. 9.
    Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S.T., Tröster, G., del R. Millán, J., Roggen, D.: The opportunity challenge: a benchmark database for on-body sensor-based activity recognition. Pattern Recogn. Lett. (2013)Google Scholar
  10. 10.
    Sagha, H., Digumarti, S.T., Millán, J.d.R., Chavarriaga, R., Calatroni, A., Roggen, D., Tröster, G.: Benchmarking classification techniques using the opportunity human activity dataset. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 36–40. IEEE (2011)Google Scholar
  11. 11.
    Yang, A.Y., Kuryloski, P., Bajcsy, R.: WARD: a wearable action recognition database (2009)Google Scholar
  12. 12.
    Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108–109. IEEE (2012)Google Scholar
  13. 13.
    Reiss, A., Stricker, D.: Creating and benchmarking a new dataset for physical activity monitoring. In: Proceedings of the 5th International Conference on Pervasive Technologies Related to Assistive Environments, 40 pages. ACM (2012)Google Scholar
  14. 14.
    Kawaguchi, N., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Inoue, S., Kawahara, Y., Sumi, Y., Nishio, N.: HASC challenge: gathering large scale human activity corpus for the real-world activity understandings. In: Proceedings of the 2nd Augmented Human International Conference, 27 pages. ACM (2011)Google Scholar
  15. 15.
    Kawaguchi, N., Watanabe, H., Yang, T., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Hada, H., Inoue, S., et al.: HASC2012corpus: large scale human activity corpus and its application. In: Proceedings of the IPSN, vol. 12 (2012)Google Scholar
  16. 16.
    Kawaguchi, N., Yang, Y., Yang, T., Ogawa, N., Iwasaki, Y., Kaji, K., Terada, T., Murao, K., Inoue, S., Kawahara, Y., et al.: HASC2011corpus: towards the common ground of human activity recognition. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 571–572. ACM (2011)Google Scholar
  17. 17.
    Zhang, M., Sawchuk, A.A.: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pp. 1036–1043. ACM (2012)Google Scholar
  18. 18.
    Zhang, M., Sawchuk, A.A.: Human daily activity recognition with sparse representation using wearable sensors. IEEE J. Biomed. Health Inform. 17(3), 553–560 (2013)CrossRefGoogle Scholar
  19. 19.
    Khandelwal, S., Wickström, N.: Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the Marea gait database. Gait Posture 51, 84–90 (2017)CrossRefGoogle Scholar
  20. 20.
    Giuberti, M., Ferrari, G.: Simple and robust BSN-based activity classification: winning the first BSN contest. In: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, 34 pages. ACM (2011)Google Scholar
  21. 21.
    Gjoreski, H., Kozina, S., Gams, M., Lustrek, M., Álvarez-García, J.A., Hong, J.H., Dey, A.K., Bocca, M., Patwari, N.: Competitive live evaluations of activity-recognition systems. IEEE Pervasive Comput. 14(1), 70–77 (2015)CrossRefGoogle Scholar
  22. 22.
    Sant’Anna, A., Salarian, A., Wickstrom, N.: A new measure of movement symmetry in early Parkinson’s disease patients using symbolic processing of inertial sensor data. IEEE Trans. Biomed. Eng. 58(7), 2127–2135 (2011)CrossRefGoogle Scholar
  23. 23.
    Sant’Anna, A.: A symbolic approach to human motion analysis using inertial sensors: framework and gait analysis study. Ph.D. thesis, Halmstad University (2012)Google Scholar
  24. 24.
    Bachlin, M., Roggen, D., Troster, G., Plotnik, M., Inbar, N., Meidan, I., Herman, T., Brozgol, M., Shaviv, E., Giladi, N., et al.: Potentials of enhanced context awareness in wearable assistants for Parkinson’s disease patients with the freezing of gait syndrome. In: 2009 International Symposium on Wearable Computers, pp. 123–130. IEEE (2009)Google Scholar
  25. 25.
    Bovi, G., Rabuffetti, M., Mazzoleni, P., Ferrarin, M.: A multiple-task gait analysis approach: kinematic, kinetic and emg reference data for healthy young and adult subjects. Gait Posture 33(1), 6–13 (2011)CrossRefGoogle Scholar
  26. 26.
    Kertész-Farkas, A., Dhir, S., Sonego, P., Pacurar, M., Netoteia, S., Nijveen, H., Kuzniar, A., Leunissen, J.A., Kocsor, A., Pongor, S.: Benchmarking protein classification algorithms via supervised cross-validation. J. Biochem. Biophys. Methods 70(6), 1215–1223 (2008)CrossRefGoogle Scholar

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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.National Research University Higher School of Economics (HSE)MoscowRussia

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