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Multi-Feature Fusion and Machine Learning: A Model for Early Detection of Freezing of Gait Events in Patients With Parkinson's Disease
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Author(s): Hadeer Elziaat (Future University in Egypt (FUE), Egypt), Nashwa El-Bendary (Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Smart Village, Egypt)and Ramadan Moawad (Future University in Egypt (FUE), Egypt)
Copyright: 2021
Pages: 24
Source title:
Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science
Source Author(s)/Editor(s): Mrutyunjaya Panda (Utkal University, India)and Harekrishna Misra (Institute of Rural Management, Anand, India)
DOI: 10.4018/978-1-7998-6659-6.ch006
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Abstract
Freezing of gait (FoG) is a common symptom of Parkinson's disease (PD) that causes intermittent absence of forward progression of patient's feet while walking. Accordingly, FoG momentary episodes are always accompanied with falls. This chapter presents a novel multi-feature fusion model for early detection of FoG episodes in patients with PD. In this chapter, two feature engineering schemes are investigated, namely time-domain hand-crafted feature engineering and convolutional neural network (CNN)-based spectrogram feature learning. Data of tri-axial accelerometer sensors for patients with PD is utilized to characterize the performance of the proposed model through several experiments with various machine learning (ML) algorithms. Obtained experimental results showed that the multi-feature fusion approach has outperformed typical single feature sets. Conclusively, the significance of this chapter is to highlight the impact of using feature fusion of multi-feature sets through investigating the performance of a FoG episodes early detection model.
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