Taufique Sayeed1, Albert Samà2, Andreu Català2, Alejandro Rodríguez-Molinero3, Joan Cabestany1. 1. Technical Research Centre for Dependency Care and Autonomous Living, Rambla de l'Exposicio, Vilanova i la Geltru, Spain. 2. School of Engineering and Informatics of National University Ireland Galway, Galway, Ireland. 3. Clinical Research Unit, Consorci Sanitari del Garraf, Vilanova i~la Geltrú, Spain.
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. RESULT: 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.
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 PDpatients. 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. RESULT: Results obtained from 28 PDpatients 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 PDpatients 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.
Entities:
Keywords:
Parkinson's disease; accelerometers; gait properties; signal processing
Authors: Daniel Rodríguez-Martín; Carlos Pérez-López; Albert Samà; Andreu Català; Joan Manuel Moreno Arostegui; Joan Cabestany; Berta Mestre; Sheila Alcaine; Anna Prats; María de la Cruz Crespo; Àngels Bayés Journal: Sensors (Basel) Date: 2017-04-11 Impact factor: 3.576
Authors: Daniel Rodríguez-Martín; Joan Cabestany; Carlos Pérez-López; Marti Pie; Joan Calvet; Albert Samà; Chiara Capra; Andreu Català; Alejandro Rodríguez-Molinero Journal: Front Neurol Date: 2022-06-02 Impact factor: 4.086