Application of Kinect technology and artificial neural networks in the control of rehabilitation therapies in people with knee injuries

Bisset Gonzales Loayza, Alberto Calla Bendita, Mario Huaypuna Cjuno, Jose Alfredo Sulla Torres

Research output: Contribution to journalArticlepeer-review

Abstract

In the field of physiotherapy, the recognition of the poses of the human body is obtaining more research so that the patient has an accelerated recovery rate in his rehabilitation. Nowadays, it is not so challenging to have devices like Microsoft Kinect that allow us to interact with the user for the recognition of poses and body gestures. The objective of this work to capture the data of the joints of a person's body through a set of angles using the Kinect device, then artificial neural networks with the Back-Propagation algorithm were used for machine learning, and their precision was determined. The results found on the performance of the neural network show that 99.70% accuracy was achieved in the classification of the patients' postures, which can be used as an alternative in the rehabilitation therapies of patients with knee injuries.

Original languageEnglish
Pages (from-to)509-515
Number of pages7
JournalInternational Journal of Advanced Computer Science and Applications
Volume11
Issue number8
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020, Science and Information Organization.

Keywords

  • Artificial neural network
  • Kinect
  • Machine learning
  • Physiotherapy
  • Rehabilitation

Fingerprint

Dive into the research topics of 'Application of Kinect technology and artificial neural networks in the control of rehabilitation therapies in people with knee injuries'. Together they form a unique fingerprint.

Cite this