Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    A Random Forest Method to Detect Parkinson's Disease via Gait Analysis
    (2017) Acici, Koray; Erdas, Cagatay Berke; Asuroglu, Tunc; Toprak, Munire Kilinc; Erdem, Hamit; Ogul, Hasan; 0000-0001-7979-0276; 0000-0003-4153-0764; 0000-0002-3821-6419; 0000-0003-3467-9923; AAJ-8674-2021; AAC-7834-2020; ITV-2441-2023; HDM-9910-2022
    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.