A Data Mining Approach to Predict Falls in Humanoid Robot Locomotion

  • João André
  • Brígida Mónica FariaEmail author
  • Cristina Santos
  • Luís Paulo Reis
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 418)


The inclusion of perceptual information in the operation of a dynamic robot (interacting with its environment) can provide valuable insight about its environment and increase robustness of its behaviour. In this regard, the concept of Associative Skill Memories (ASMs) has provided a great contributions regarding an effective and practical use of sensor data, under a simple and intuitive framework [2, 13]. Inspired by [2], this paper presents a data mining solution to the fall prediction problem in humanoid biped robotic locomotion. Sensor data from a large number of simulations was recorded and four data mining algorithms were applied with the aim of creating a classifier that properly identifies failure conditions. Using Support Vector Machines, on top of sensor data from a large number of simulation trials, it was possible to build an accurate and reliable offline fall predictor, achieving accuracy, sensitivity and specificity values up to 95.6%, 96.3% and 94.5%, respectively.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • João André
    • 1
    • 4
  • Brígida Mónica Faria
    • 2
    • 5
    • 6
    Email author
  • Cristina Santos
    • 1
    • 4
  • Luís Paulo Reis
    • 2
    • 3
    • 4
  1. 1.DEI/EEUM - Dep. de Electrónica IndustrialEsc. de Engenharia da Universidade do MinhoGuimarãesPortugal
  2. 2.ESTSP/IPP – Esc. Sup. de Tecnologia da Saúde do PortoInstituto Politécnico do PortoPortoPortugal
  3. 3.DSI/EEUM - Dep. de Sistemas de InformaçãoEsc. de Engenharia da Universidade do MinhoGuimarãesPortugal
  4. 4.Centro ALGORITMIGuimarãesPortugal
  5. 5.LIACC – Laboratório de Inteligência Artificial e Ciência de ComputadoresPortoPortugal
  6. 6.INESC TEC – INESC Tecnologia e CiênciaPortoPortugal

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