6th International Conference on Pervasive Computing Technologies for Healthcare

Research Article

Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2012.248680,
        author={Sinziana Mazilu and Michael Hardegger and Zack Zhu and Daniel Roggen and Gerhard Troester and Meir Plotnik and Jeffrey Hausdorff},
        title={Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques},
        proceedings={6th International Conference on Pervasive Computing Technologies for Healthcare},
        publisher={IEEE},
        proceedings_a={PERVASIVEHEALTH},
        year={2012},
        month={7},
        keywords={freezing of gait wearable assistant machine learning smartphones},
        doi={10.4108/icst.pervasivehealth.2012.248680}
    }
    
  • Sinziana Mazilu
    Michael Hardegger
    Zack Zhu
    Daniel Roggen
    Gerhard Troester
    Meir Plotnik
    Jeffrey Hausdorff
    Year: 2012
    Online Detection of Freezing of Gait with Smartphones and Machine Learning Techniques
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2012.248680
Sinziana Mazilu1,*, Michael Hardegger1, Zack Zhu1, Daniel Roggen1, Gerhard Troester1, Meir Plotnik2, Jeffrey Hausdorff2
  • 1: ETH Zurich
  • 2: Neurodynamics & Gait Research Laboratory, Tel Aviv Sourasky Medical Center
*Contact email: sinziana.mazilu@ife.ee.ethz.ch

Abstract

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.