Ankle rehabilitation system with feedback from a smartphone wireless gyroscope platform and machine learning classification

Robert Lemoyne, Timothy Mastroianni, Anthony Hessel, Kiisa C Nishikawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

With the prevalence of traumatic brain injury and associated motor function impairment, an advance in the capacity to measure the efficacy of a rehabilitation strategy is a topic of considerable interest. For example, the development of a rehabilitation system that can quantify the efficacy to an ankle dorsiflexion therapy prescription would be beneficial. An ankle rehabilitation system is presented that amalgamates multiple technologies, such as a smartphone (iPhone) wireless gyroscope platform, machine learning, and 3D printing. The ankle rehabilitation system is produced by mostly 3D printing. A smartphone wireless gyroscope platform records the ankle rehabilitation system's therapy usage with wireless transmission to the Internet as an email attachment. The gyroscope signal data is processed for machine learning. A support vector machine attains 97% classification between a hemiplegic affected ankle and unaffected ankle feature set while using the ankle rehabilitation system. The application can be readily applied to a homebound setting of the subject's convenience.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages406-409
Number of pages4
ISBN (Print)9781509002870
DOIs
StatePublished - Mar 2 2016
EventIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 - Miami, United States
Duration: Dec 9 2015Dec 11 2015

Other

OtherIEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015
CountryUnited States
CityMiami
Period12/9/1512/11/15

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Keywords

  • 3D printing
  • Ankle rehabilitation
  • Dorsiflexion
  • Gyroscope
  • Machine learning
  • Smartphone
  • Support vector machine
  • Therapy
  • Therapy quantification
  • Wireless gyroscope
  • Wireless sensor

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Lemoyne, R., Mastroianni, T., Hessel, A., & Nishikawa, K. C. (2016). Ankle rehabilitation system with feedback from a smartphone wireless gyroscope platform and machine learning classification. In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015 (pp. 406-409). [7424346] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMLA.2015.213