A dynamic Bayesian network for estimating the risk of falls from real gait data

  • German CuayaEmail author
  • Angélica Muñoz-Meléndez
  • Lidia Nuñez Carrera
  • Eduardo F. Morales
  • Ivett Quiñones
  • Alberto I. Pérez
  • Aldo Alessi
Original Article


Pathological and age-related changes may affect an individual’s gait, in turn raising the risk of falls. In elderly, falls are common and may eventuate in severe injuries, long-term disabilities, and even death. Thus, there is interest in estimating the risk of falls from gait analysis. Estimation of the risk of falls requires consideration of the longitudinal evolution of different variables derived from human gait. Bayesian networks are probabilistic models which graphically express dependencies among variables. Dynamic Bayesian networks (DBNs) are a type of BN adequate for modeling the dynamics of the statistical dependencies in a set of variables. In this work, a DBN model incorporates gait derived variables to predict the risk of falls in elderly within 6 months subsequent to gait assessment. Two DBNs were developed; the first (DBN1; expert-guided) was built using gait variables identified by domain experts, whereas the second (DBN2; strictly computational) was constructed utilizing gait variables picked out by a feature selection algorithm. The effectiveness of the second model to predict falls in the 6 months following assessment is 72.22 %. These results are encouraging and supply evidence regarding the usefulness of dynamic probabilistic models in the prediction of falls from pathological gait.


Probabilistic models Dynamic Bayesian networks Elderly Gait analysis Risk of falls 



This research was supported by the National Institute for Astrophysics, Optics and Electronics (INAOE), and the Mexican National Council for Science and Technology (CONACyT), through the scholarship for doctoral studies 174498. The researchers of the Human Motion Analysis Laboratory of the National Institute of Rehabilitation in Mexico provided the gait data to develop the models presented in this work, under the research grant 01-042 of CONACyT-Health Sector Fund 2003.


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Copyright information

© International Federation for Medical and Biological Engineering 2012

Authors and Affiliations

  • German Cuaya
    • 1
    Email author
  • Angélica Muñoz-Meléndez
    • 1
  • Lidia Nuñez Carrera
    • 2
  • Eduardo F. Morales
    • 1
  • Ivett Quiñones
    • 2
  • Alberto I. Pérez
    • 2
  • Aldo Alessi
    • 2
  1. 1.Computer Science DepartmentInstituto Nacional de Astrofsica ptica y ElctroniaTonantzintlaMexico
  2. 2.National Institute of RehabilitationHuman Motion Analysis LaboratoryMexico, D.F.Mexico

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