Adapted step length estimators for patients with Parkinson's disease using a lateral belt worn accelerometer.

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.

[1]  Joan Cabestany,et al.  Dyskinesia and motor state detection in Parkinson's Disease patients with a single movement sensor , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Vasilios Kyriazis,et al.  Gait analysis techniques , 2001, Journal of Orthopaedics and Traumatology.

[3]  Joan Cabestany,et al.  A double closed loop to enhance the quality of life of Parkinson's Disease patients: REMPARK system , 2014, InMed.

[4]  D. Alvarez,et al.  Modified Pendulum Model for Mean Step Length Estimation , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Joan Cabestany,et al.  A heterogeneous database for movement knowledge extraction in Parkinson's disease , 2013, ESANN.

[6]  J. Summers,et al.  Stride length regulation in Parkinson's disease. Normalization strategies and underlying mechanisms. , 1996, Brain : a journal of neurology.

[7]  Catherine Dehollain,et al.  Gait assessment in Parkinson's disease: toward an ambulatory system for long-term monitoring , 2004, IEEE Transactions on Biomedical Engineering.

[8]  M. Mathie,et al.  of the 23 rd Annual EMBS International Conference , October 25-28 , Istanbul , Turkey A SYSTEM FOR MONITORING POSTURE AND PHYSICAL ACTIVITY USING ACCELEROMETERS , 2004 .

[9]  Chan Gook Park,et al.  Adaptive step length estimation algorithm using optimal parameters and movement status awareness. , 2011, Medical engineering & physics.

[10]  Andreu Català,et al.  A Wearable Inertial Measurement Unit for Long-Term Monitoring in the Dependency Care Area , 2013, Sensors.

[11]  Andreu Català,et al.  Comparative and adaptation of step detection and step length estimators to a lateral belt worn accelerometer , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[12]  M. Hoehn,et al.  Parkinsonism , 1967, Neurology.

[13]  A Leardini,et al.  Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: Validation on normal subjects by standard gait analysis , 2012, Comput. Methods Programs Biomed..

[14]  Jeffrey M. Hausdorff,et al.  Gait dynamics in Parkinson's disease: relationship to Parkinsonian features, falls and response to levodopa , 2003, Journal of the Neurological Sciences.

[15]  Ruzena Bajcsy,et al.  Determination of a Patient's Speed and Stride Length Minimizing Hardware Requirements , 2011, 2011 International Conference on Body Sensor Networks.

[16]  Bofeng Zhang,et al.  State of the Art in Gait Analysis Using Wearable Sensors for Healthcare Applications , 2012, 2012 IEEE/ACIS 11th International Conference on Computer and Information Science.

[17]  M. Morris,et al.  The biomechanics and motor control of gait in Parkinson disease. , 2001, Clinical biomechanics.

[18]  M. Hoehn,et al.  Parkinsonism , 1998, Neurology.

[19]  J. Hughes,et al.  Accuracy of clinical diagnosis of idiopathic Parkinson's disease: a clinico-pathological study of 100 cases. , 1992, Journal of neurology, neurosurgery, and psychiatry.

[20]  Joan Cabestany,et al.  Comparison and adaptation of step length and gait speed estimators from single belt worn accelerometer positioned on lateral side of the body , 2013, 2013 IEEE 8th International Symposium on Intelligent Signal Processing.

[21]  A. Hof,et al.  Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. , 2003, Gait & posture.

[22]  Sinziana Mazilu,et al.  Feature Learning for Detection and Prediction of Freezing of Gait in Parkinson's Disease , 2013, MLDM.

[23]  A. Hof,et al.  Displacement of the pelvis during human walking: experimental data and model predictions , 1997 .