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

• Published in: Engineering Applications of Neural Networks Vol. 744; pp. 609 - 619
• Main Authors: Açıcı, Koray, Erdaş, Çağatay Berke, Aşuroğlu, Tunç, Toprak, Münire Kılınç, Erdem, Hamit, Oğul, Hasan
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Communications in Computer and Information Science
• Subjects: Gait analysis; Parkinson’s Disease; Remote care; Wireless sensor
• ISBN: 3319651714; 9783319651712
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-319-65172-9_51

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.