Journal
ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2017
Volume 744, Issue -, Pages 609-619Publisher
SPRINGER-VERLAG BERLIN
DOI: 10.1007/978-3-319-65172-9_51
Keywords
Parkinson's Disease; Gait analysis; Remote care; Wireless sensor
Funding
- Scientific and Technological Research Council of Turkey (TUBITAK) [115E451]
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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.
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