The Median Point DTW Template to Classify Upper Limb Gestures at Different Speeds

  • R. Muscillo
  • M. Schmid
  • S. Conforto
Part of the IFMBE Proceedings book series (IFMBE, volume 22)


Detecting activities of daily living (ADL) and classifying the gesture typology are important tasks for rehabilitation and for applications in robotics. The use of wearable sensors, such as accelerometers, could facilitate the previous tasks since it would open the possibility of monitoring patients in real-life conditions.

This study aims at detecting and classifying gestures recorded by accelerometers: in particular, a set of upper limb motor tasks that are contained in the rehabilitation scale known as Wolf Motor Function Test (WMFT), were used in this work. Two accelerometers, respectively a dual-axis one placed on the biceps and a three-axis one placed internally on the wrist, constitute the sensors set. Five normal subjects were included in the protocol and were asked to perform five different gestures. Dynamic Time Warping (DTW) approach was chosen to process data: this is a template matching technique that assesses the similarity between signals by using a reference signal (i.e. the Template). In this work a novel approach for the construction of the Template is proposed. The Median point DTW Template (MDTW), which is built by connecting the non-linear path between two signals corresponding to movements performed at two different speeds, is introduced. One MDTW was built for each channel, each gesture and each subject, and it was used as a reference signal to recognize five different gestures at various velocities. Moreover, aiming at the generalization of the approach, the recognition performance was also assessed on the classification obtained by using a unique Template for all the five voluntaries for each channel and for each gesture. The recognition percentage obtained by using the subject specific version of MDTW is around 94%, while with the subject independent approach the increase of generalization makes the recognition percentage decrease at around 88%. This latter approach would improve the applicability of wearable monitoring, by substantially decreasing the burden time for the template set construction on a subject-by-subject basis.


Accelerometers DTW Recognition Upper Limb 


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  1. 1.
    A. Pentland, “Healthwear: medical technology becomes wearable,” Stud Health Technol Inform, vol. 118, pp. 55–65, 2005.Google Scholar
  2. 2.
    P. Bonato, “Advances in wearable technology and applications in physical medicine and rehabilitation,” J Neuroengineering Rehabil, vol. 2, p. 2, Feb 25 2005.CrossRefGoogle Scholar
  3. 3.
    M. J. Mathie, A. C. Coster, N. H. Lovell, and B. G. Celler, “Accelerometry: providing an integrated, practical method for longterm, ambulatory monitoring of human movement,” Physiol Meas, vol. 25, pp. R1–20, Apr 2004.CrossRefGoogle Scholar
  4. 4.
    P. H. Veltink, H. B. Bussmann, W. de Vries, W. L. Martens, and R. C. Van Lummel, “Detection of static and dynamic activities using uniaxial accelerometers,” IEEE Trans Rehabil Eng, vol. 4, pp. 375–85, Dec 1996.CrossRefGoogle Scholar
  5. 5.
    F. Foerster and J. Fahrenberg, “Motion pattern and posture: correctly assessed by calibrated accelerometers,” Behav Res Methods Instrum Comput, vol. 32, pp. 450–7, Aug 2000.Google Scholar
  6. 6.
    D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B.G. Celler, “Implementation of a real-time human movement classifier using a three-axis accelerometer for ambulatory monitoring,” IEEE Trans Inf Technol Biomed, vol. 10, pp. 156–67, Jan 2006.CrossRefGoogle Scholar
  7. 7.
    B. Knorr, R. Hughes, D. Sherril, J. Stein, M. Akai, P. Bonato, “Quantitative Measures of Functional Upper Limb Movement in Persons after Stroke”, Proceedings of the 2 International IEEE EMBS Conference on Neural Engineering Virginia · March 16–19, 200Google Scholar
  8. 8.
    Schasfoort FC, Bussmann JBJ, Stam HJ. “Ambulatory measurement of upper limb usage and mobility-related activities during normal daily life with an Upper Limb-Activity Monitor:a feasibility study”, Medical & Biological Engineering & Computing 2002 40(2):173–82CrossRefGoogle Scholar
  9. 9.
    Williamson, R.; Andrews, B.J. (2001). Detecting absolute human knee angle and angular velocity using accelerometers and rate gyroscopes. Med. Biol. Eng. Comput., 39, 294–302.CrossRefGoogle Scholar
  10. 10.
    Muscillo R, Conforto S, Schmid M, Caselli P, D’Alessio T. Resolving ADL Activities Variability through Derivative Dynamic Time Warping applied on Accelerometer Data. Proceedings of the 29th International Conference of the IEEE EMBS 2007: 4930–33.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • R. Muscillo
    • 1
  • M. Schmid
    • 1
  • S. Conforto
    • 1
  1. 1.Applied Electronics DepartmentUniversity Roma TreRomeItaly

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