Using artificial neural network to determine favorable wheelchair tilt and recline usage in people with spinal cord injury: Training ANN with genetic algorithm to improve generalization

Jicheng Fu, Jerrad Genson, Yih Kuen Jan, Maria Jones

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

Abstract

People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to overfitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Pages25-32
Number of pages8
DOIs
StatePublished - 2011
Event23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 - Boca Raton, FL, United States
Duration: Nov 7 2011Nov 9 2011

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
CountryUnited States
CityBoca Raton, FL
Period11/7/1111/9/11

Keywords

  • Artificial neural network
  • Genetic algorithm
  • Pressure ulcer
  • Skin perfusion
  • Support vector machine
  • Wheelchair tilt and recline

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Fu, J., Genson, J., Jan, Y. K., & Jones, M. (2011). Using artificial neural network to determine favorable wheelchair tilt and recline usage in people with spinal cord injury: Training ANN with genetic algorithm to improve generalization. In Proceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 (pp. 25-32). [6103302] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). https://doi.org/10.1109/ICTAI.2011.13