Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

@article{Eskofier2016RecentML,
  title={Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment},
  author={Bjoern M. Eskofier and Sunghoon Ivan Lee and Jean-François Daneault and Fatemeh Noushin Golabchi and Gabriela Ferreira-Carvalho and Gloria Vergara-Diaz and Stefano Sapienza and Gianluca Costante and Jochen Klucken and Thomas Kautz and Paolo Bonato},
  journal={2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  year={2016},
  pages={655-658}
}
The development of wearable sensors has opened the door for long-term assessment of movement disorders. However, there is still a need for developing methods suitable to monitor motor symptoms in and outside the clinic. The purpose of this paper was to investigate deep learning as a method for this monitoring. Deep learning recently broke records in speech and image classification, but it has not been fully investigated as a potential approach to analyze wearable sensor data. We collected data… CONTINUE READING
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