title: Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer.
abstract: BACKGROUND
  Parkinson's disease (PD) is a neurodegenerative disease that predominantly alters patients' motor performance. Reduced step length and inability of step are important symptoms associated with PD. Assessing patients' motor state monitoring step length helps to detect periods in which patients suffer lack of medication effect.
  
  
  OBJECTIVE
  Evaluate the adaption of existing step length estimation methods based on accelerometer sensors to a new position on left lateral side of waist in 28 PD patients.
  
  
  METHODS
  In this paper, a user-friendly position, the lateral side of the waist, is selected to place a tri-axial accelerometer. A newly developed step detection algorithm - Sliding Window Averaging Technique (SWAT) is evaluated in detecting steps using signals from this location. The detected steps are then used to estimate step length using four proposed correction factors for Zijlstra's, Gonzalez's and Weinberg's methods that were originally developed for the signals from lower back.
  
  
  RESULTS
  Results obtained from 28 PD patients are discussed and the effects of calibrating in each motor state are compared. A generic correction factor is also proposed and compared with the best method to use instead of individual calibration. Despite variable gait speed and different motor state, SWAT achieved overall accuracy of 96.76% in step detection. Among the different step length estimators, the Zijlstra method performs better with multiplying individual correction factors that consider left and right step length separately providing average error of 0.033 m.
  
  
  CONCLUSIONS
  Zijlstra's method with individual correction factor that considers left and right step length separately and obtained from during ON state of a PD patients provide most accurate estimation among the others. As training session is during ON state, data from induced OFF state to train the methods are not required. A generic correction factor is also proposed to apply with Zijlstra's method to avoid individual calibration process.
authors.affiliation: Technical Research Centre for Dependency Care and Autonomous Living, Rambla de l'Exposicio, Vilanova i la Geltru, Spain.
authors.forename: Taufique
authors.initials: T
authors.lastname: Sayeed
authors.affiliation: School of Engineering and Informatics of National University Ireland Galway, Galway, Ireland.
authors.forename: Albert
authors.initials: A
authors.lastname: Samà
authors.affiliation: School of Engineering and Informatics of National University Ireland Galway, Galway, Ireland.
authors.forename: Andreu
authors.initials: A
authors.lastname: Català
authors.affiliation: Clinical Research Unit, Consorci Sanitari del Garraf, Vilanova i~la Geltrú, Spain.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Rodríguez-Molinero
authors.affiliation: Technical Research Centre for Dependency Care and Autonomous Living, Rambla de l'Exposicio, Vilanova i la Geltru, Spain.
authors.forename: Joan
authors.initials: J
authors.lastname: Cabestany
mesh_terms: D061725:Accelerometry / Q000295:instrumentation / Q000379:methods*; D000368:Aged; D000465:Algorithms; D005684:Gait / Q000502:physiology; D006801:Humans; D008124:Locomotion / Q000502:physiology*; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology*
keywords: Parkinson's disease; accelerometers; gait properties; signal processing