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@ARTICLE{Baechlin_TITB_2010,
    author = {B{\"{a}}chlin, Marc and Plotnik, Meir and Roggen, Daniel and Maidan, Inbal and Hausdorff, Jeffrey M and Giladi, Nir and Tr{\"{o}}ster, Gerhard},
  keywords = {CaseStudyFOG, DAPHNET},
     month = mar,
     title = {Wearable assistant for Parkinson's disease patients with the freezing of gait symptom},
   journal = {IEEE Transactions on Information Technology in Biomedicine},
    volume = {14},
    number = {2},
      year = {2010},
     pages = {436 - 446},
      issn = {1089-7771},
       url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5325884},
       doi = {10.1109/TITB.2009.2036165},
  abstract = {In this paper, we present a wearable assistant for Parkinson’s disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients’ movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1\% and a specificity of 81.6\%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.}
}