A Random Forest Method to Detect Parkinson's Disease via Gait Analysis


Acici K., Erdas C. B., Asuroglu T., Toprak M. K., Erdem H., Ogul H.

18th International Conference on Engineering Applications of Neural Networks (EANN), Athens, Yunanistan, 25 - 27 Ağustos 2017, cilt.744, ss.609-619 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 744
  • Doi Numarası: 10.1007/978-3-319-65172-9_51
  • Basıldığı Şehir: Athens
  • Basıldığı Ülke: Yunanistan
  • Sayfa Sayıları: ss.609-619
  • Anahtar Kelimeler: Parkinson's Disease, Gait analysis, Remote care, Wireless sensor, SYSTEM
  • Ankara Üniversitesi Adresli: Hayır

Özet

Remote care and telemonitoring have become essential component of current geriatric medicine. Intelligent use of wireless sensors is a major issue in relevant computational studies to realize these concepts in practice. While there has been a growing interest in recognizing daily activities of patients through wearable sensors, the efforts towards utilizing the streaming data from these sensors for clinical practices are limited. Here, we present a practical application of clinical data mining from wearable sensors with a particular objective of diagnosing Parkinson's Disease from gait analysis through a sets of ground reaction force (GRF) sensors worn under the foots. We introduce a supervised learning method based on Random Forests that analyze the multi-sensor data to classify the person wearing these sensors. We offer to extract a set of time-domain and frequency-domain features that would be effective in distinguishing normal and diseased people from their gait signals. The experimental results on a benchmark dataset have shown that proposed method can significantly outperform the previous methods reported in the literature.