A Wearable Accelerometer System for Unobtrusive Monitoring of Parkinson's Diease Motor Symptoms

Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted treatment regimens. In this paper we describe the algorithmic development of the system and an evaluation in patients for assessing the onset and duration of advanced PD motor symptoms.

[1]  J. J. van Hilten,et al.  Accelerometric assessment of levodopa‐induced dyskinesias in Parkinson's disease , 2001, Movement disorders : official journal of the Movement Disorder Society.

[2]  Joshua Weaver A wearable health monitor to aid Parkinson disease treatment , 2003 .

[3]  P. Bonato,et al.  Data mining techniques to detect motor fluctuations in Parkinson's disease , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[5]  Kirsi Helkala,et al.  Biometric Gait Authentication Using Accelerometer Sensor , 2006, J. Comput..

[6]  Theodore Raphan,et al.  A Model-Based Approach for Assessing Parkinsonian Gait and Effects of Levodopa and Deep Brain Stimulation , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  M. Mathie,et al.  Detection of daily physical activities using a triaxial accelerometer , 2003, Medical and Biological Engineering and Computing.

[8]  Stan C A M Gielen,et al.  Ambulatory motor assessment in Parkinson's disease , 2006, Movement disorders : official journal of the Movement Disorder Society.

[9]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[10]  G. ÓLaighin,et al.  Direct measurement of human movement by accelerometry. , 2008, Medical engineering & physics.

[11]  Zhenyu He,et al.  Weightlessness feature — a novel feature for single tri-axial accelerometer based activity recognition , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Kenneth Meijer,et al.  Activity identification using body-mounted sensors—a review of classification techniques , 2009, Physiological measurement.

[13]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[14]  Robert LeMoyne,et al.  Quantification of Parkinson's disease characteristics using wireless accelerometers , 2009, 2009 ICME International Conference on Complex Medical Engineering.

[15]  Roger O. Smith,et al.  Feature Extraction Method for Real Time Human Activity Recognition on Cell Phones , 2011 .

[16]  Gary M. Weiss,et al.  Activity recognition using cell phone accelerometers , 2011, SKDD.

[17]  Angelo Antonini,et al.  Evaluation of Motor Complications: Motor Fluctuations , 2012 .

[18]  B. Bloem,et al.  Quantitative wearable sensors for objective assessment of Parkinson's disease , 2013, Movement disorders : official journal of the Movement Disorder Society.