Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks
Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
[1] Alcaide-Aguirre RE, Morgenroth DC, Ferris DP; Motor control and
learning with lower-limb myoelectric control in amputees. Journal of
Rehabilitation Research & Development (JRRD). 687-698. Volume 50,
Number 5, 2013.
[2] Cisi RRL, Cabral FE, J; Human Gait Analysed by an Artificial Neural
Network Model, Proceedings of the IV Brazilian Conference on Neural
Networks - IV Congresso Brasileiro de Redes Neuraispp. 148-151, July
20-22, 1999.
[3] Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J, Piłsudski J; Artificial
Neural Networks (ANN) Applied for Gait Classification and
Physiotherapy Monitoring in Post Stroke Patients, Chapter 16, Intech,
published in Artificial Neural Networks – Methodological Advances and
Biomedical Applications”, book edited by Suzuki K, 2011 Apr.
[4] Patrick JH, Keenan MA; Gait analysis to assist walking after stroke.
Lancet. 2007 Jan 27; 369(9558):256-7.
[5] Barton, JG; Lees, A; An application of neural networks for
distinguishing gait patterns on the basis of hip-knee joint angle
diagrams, Gait & Posture 5, 1997, 28-33.
[6] Rumelhart, DE; Geoffrey, EH; Williams, RJ; Learning Internal
Representations by Error Propagation. David E. Rumelhart, James L.
McClelland, and the PDP research group. (Editors), Parallel distributed
processing: Explorations in the microstructure of cognition, Volume 1:
Foundations. MIT Press, 1986.
[7] Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP; Section
3.7.1. Radial Basis Function Interpolation, Numerical Recipes: The Art
of Scientific Computing (3rd ed.), New York: Cambridge University
Press, 2007, ISBN 978-0-521-88068-8.
[8] De Asha AR, Munjal R, Kulkarni J, Buckley JG; Walking speed related
joint kinetic alterations in trans-tibial amputees: impact of hydraulic
‘ankle’ damping. Journal of NeuroEngineering and Rehabilitation,
10:107, 2013.
[9] Cappozzo A, Catini F, Croce UD, Leardini A: Position and orientation
in space of bones during movement: anatomical frame definition and
determination. Clinical Biomechanics 1995, 10(4):171–178.
[10] Karlsson D, Tranberg R: On skin movement artefact – resonant
frequencies of skin markers attached to the leg. Hum Mov Sci 1999,
18(5):627–635.
[11] MathWorks. (2014). Improve Neural Network Generalization and Avoid
Overfitting. Available:
http://www.mathworks.co.uk/help/nnet/ug/improve-neural-networkgeneralization-
and-avoid-overfitting.html . Last accessed 19th Sep 2014.
[12] Vali AA, Ramesht MH, Mokarram M; The Comparison of RBF and
MLP Neural Networks Performance for the Estimation of Land
Suitability. Vali et al/ Journal of Environment, Vol. 02, Issue 03, pp. 74-
78, 2013.
[1] Alcaide-Aguirre RE, Morgenroth DC, Ferris DP; Motor control and
learning with lower-limb myoelectric control in amputees. Journal of
Rehabilitation Research & Development (JRRD). 687-698. Volume 50,
Number 5, 2013.
[2] Cisi RRL, Cabral FE, J; Human Gait Analysed by an Artificial Neural
Network Model, Proceedings of the IV Brazilian Conference on Neural
Networks - IV Congresso Brasileiro de Redes Neuraispp. 148-151, July
20-22, 1999.
[3] Kaczmarczyk K, Wit A, Krawczyk M, Zaborski J, Piłsudski J; Artificial
Neural Networks (ANN) Applied for Gait Classification and
Physiotherapy Monitoring in Post Stroke Patients, Chapter 16, Intech,
published in Artificial Neural Networks – Methodological Advances and
Biomedical Applications”, book edited by Suzuki K, 2011 Apr.
[4] Patrick JH, Keenan MA; Gait analysis to assist walking after stroke.
Lancet. 2007 Jan 27; 369(9558):256-7.
[5] Barton, JG; Lees, A; An application of neural networks for
distinguishing gait patterns on the basis of hip-knee joint angle
diagrams, Gait & Posture 5, 1997, 28-33.
[6] Rumelhart, DE; Geoffrey, EH; Williams, RJ; Learning Internal
Representations by Error Propagation. David E. Rumelhart, James L.
McClelland, and the PDP research group. (Editors), Parallel distributed
processing: Explorations in the microstructure of cognition, Volume 1:
Foundations. MIT Press, 1986.
[7] Press, WH; Teukolsky, SA; Vetterling, WT; Flannery, BP; Section
3.7.1. Radial Basis Function Interpolation, Numerical Recipes: The Art
of Scientific Computing (3rd ed.), New York: Cambridge University
Press, 2007, ISBN 978-0-521-88068-8.
[8] De Asha AR, Munjal R, Kulkarni J, Buckley JG; Walking speed related
joint kinetic alterations in trans-tibial amputees: impact of hydraulic
‘ankle’ damping. Journal of NeuroEngineering and Rehabilitation,
10:107, 2013.
[9] Cappozzo A, Catini F, Croce UD, Leardini A: Position and orientation
in space of bones during movement: anatomical frame definition and
determination. Clinical Biomechanics 1995, 10(4):171–178.
[10] Karlsson D, Tranberg R: On skin movement artefact – resonant
frequencies of skin markers attached to the leg. Hum Mov Sci 1999,
18(5):627–635.
[11] MathWorks. (2014). Improve Neural Network Generalization and Avoid
Overfitting. Available:
http://www.mathworks.co.uk/help/nnet/ug/improve-neural-networkgeneralization-
and-avoid-overfitting.html . Last accessed 19th Sep 2014.
[12] Vali AA, Ramesht MH, Mokarram M; The Comparison of RBF and
MLP Neural Networks Performance for the Estimation of Land
Suitability. Vali et al/ Journal of Environment, Vol. 02, Issue 03, pp. 74-
78, 2013.
@article{"International Journal of Medical, Medicine and Health Sciences:68179", author = "L. Parisi", title = "Exploiting Kinetic and Kinematic Data to Plot Cyclograms for Managing the Rehabilitation Process of BKAs by Applying Neural Networks", abstract = "Kinematic data wisely correlate vector quantities in
space to scalar parameters in time to assess the degree of symmetry
between the intact limb and the amputated limb with respect to a
normal model derived from the gait of control group participants.
Furthermore, these particular data allow a doctor to preliminarily
evaluate the usefulness of a certain rehabilitation therapy.
Kinetic curves allow the analysis of ground reaction forces (GRFs)
to assess the appropriateness of human motion.
Electromyography (EMG) allows the analysis of the fundamental
lower limb force contributions to quantify the level of gait
asymmetry. However, the use of this technological tool is expensive
and requires patient’s hospitalization. This research work suggests
overcoming the above limitations by applying artificial neural
networks.
", keywords = "Kinetics, kinematics, cyclograms, neural networks.", volume = "8", number = "10", pages = "733-5", }
