Online detection of freezing of gait with smartphones and machine learning techniques

Freezing of gait (FoG) is a common gait deficit in advanced Parkinson's disease (PD). FoG events are associated with falls, interfere with daily life activities and impair quality of life. FoG is often resistant to pharmacologic treatment; therefore effective non-pharmacologic assistance is needed. We propose a wearable assistant, composed of a smartphone and wearable accelerometers, for online detection of FoG. The system is based on machine learning techniques for automatic detection of FoG episodes. When FoG is detected, the assistant provides rhythmic auditory cueing or vibrotactile feedback that stimulates the patient to resume walking. We tested our solution on more than 8h of recorded lab data from PD patients that experience FoG in daily life. We characterize the system performance on user-dependent and user-independent experiments, with respect to different machine learning algorithms, sensor placement and preprocessing window size. The final system was able to detect FoG events with an average sensitivity and specificity of more than 95%, and mean detection latency of 0.34s in user-dependent settings.

[1]  Takao Hashimoto,et al.  Speculation on the responsible sites and pathophysiology of freezing of gait , 2006 .

[2]  Frank Sposaro,et al.  iFall: An android application for fall monitoring and response , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Brian L. Day,et al.  Doorway‐provoked freezing of gait in Parkinson's disease , 2012, Movement disorders : official journal of the Movement Disorder Society.

[4]  Lloyd A. Smith,et al.  Practical feature subset selection for machine learning , 1998 .

[5]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[6]  Kwang Suk Park,et al.  Gait analysis for freezing detection in patients with movement disorder using three dimensional acceleration system , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[7]  Jeffrey M. Hausdorff,et al.  Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom , 2010, IEEE Transactions on Information Technology in Biomedicine.

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

[9]  Kaat Desloovere,et al.  Freezing of gait in Parkinson's disease: The impact of dual‐tasking and turning , 2010, Movement disorders : official journal of the Movement Disorder Society.

[10]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[11]  Emiliano Miluzzo,et al.  A survey of mobile phone sensing , 2010, IEEE Communications Magazine.

[12]  Lars Widmer,et al.  An Educational and Research Kit for Activity and Context Recognition from On-body Sensors , 2010, 2010 International Conference on Body Sensor Networks.

[13]  Jeffrey M. Hausdorff,et al.  The power of cueing to circumvent dopamine deficits: A review of physical therapy treatment of gait disturbances in Parkinson's disease , 2002, Movement disorders : official journal of the Movement Disorder Society.

[14]  Nir Giladi,et al.  Freezing of gait affects quality of life of peoples with Parkinson's disease beyond its relationships with mobility and gait , 2007, Movement disorders : official journal of the Movement Disorder Society.

[15]  Jeffrey M. Hausdorff,et al.  The role of mental function in the pathogenesis of freezing of gait in Parkinson's disease , 2006, Journal of the Neurological Sciences.

[16]  Paolo Bonato,et al.  Monitoring Motor Fluctuations in Patients With Parkinson's Disease Using Wearable Sensors , 2009, IEEE Transactions on Information Technology in Biomedicine.

[17]  W. Ondo,et al.  Ambulatory monitoring of freezing of gait in Parkinson's disease , 2008, Journal of Neuroscience Methods.

[18]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[19]  Nir Giladi,et al.  Characterization of freezing of gait subtypes and the response of each to levodopa in Parkinson's disease , 2003, European journal of neurology.

[20]  Andrew T. Campbell,et al.  Bewell: A smartphone application to monitor, model and promote wellbeing , 2011, PervasiveHealth 2011.

[21]  Christoph Busch,et al.  Using Hidden Markov Models for accelerometer-based biometric gait recognition , 2011, 2011 IEEE 7th International Colloquium on Signal Processing and its Applications.

[22]  Jeffrey M. Hausdorff,et al.  Falls and freezing of gait in Parkinson's disease: A review of two interconnected, episodic phenomena , 2004, Movement disorders : official journal of the Movement Disorder Society.

[23]  H. Ellgring,et al.  Predictors of freezing in Parkinson's disease: A survey of 6,620 patients , 2007, Movement disorders : official journal of the Movement Disorder Society.

[24]  Alice Nieuwboer,et al.  Cueing for freezing of gait in patients with Parkinson's disease: A rehabilitation perspective , 2008, Movement disorders : official journal of the Movement Disorder Society.

[25]  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.

[26]  Nir Giladi,et al.  Objective detection of subtle freezing of gait episodes in Parkinson's disease , 2010, Movement disorders : official journal of the Movement Disorder Society.

[27]  E. Jovanov,et al.  deFOG — A real time system for detection and unfreezing of gait of Parkinson’s patients , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Sue Leurgans,et al.  Short-term and practice effects of metronome pacing in Parkinson's disease patients with gait freezing while in the 'on' state: randomized single blind evaluation. , 2004, Parkinsonism & related disorders.

[29]  Luca Chittaro,et al.  MOPET: A context-aware and user-adaptive wearable system for fitness training , 2008, Artif. Intell. Medicine.

[30]  Stephen R Lord,et al.  Clinical and physiological assessments for elucidating falls risk in Parkinson's disease , 2009, Movement disorders : official journal of the Movement Disorder Society.

[31]  M. Hallett,et al.  Freezing of gait: moving forward on a mysterious clinical phenomenon , 2011, The Lancet Neurology.