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Article

Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns

1
Department of Computer Engineering, Kwangwoon University, Seoul 01897, Korea
2
School of Applied Science, Telkom University, Bandung 40257, Indonesia
3
Department of Intelligent Information and Embedded Software Engineering, Kwangwoon University, Seoul 01897, Korea
4
Human Convergence Technology R&D Department, Korea Institute of Industrial Technology, Ansan 15588, Korea
5
Department of Computer Science and Engineering Seoul National University of Science and Technology, Seoul 01811, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(21), 6253; https://doi.org/10.3390/s20216253
Received: 28 September 2020 / Revised: 25 October 2020 / Accepted: 28 October 2020 / Published: 2 November 2020
Gait analysis is commonly used to detect foot disorders and abnormalities such as supination, pronation, unstable left foot and unstable right foot. Early detection of these abnormalities could help us to correct the walking posture and avoid getting injuries. This paper presents extensive feature analyses on smart shoes sensor data, including pressure sensors, accelerometer and gyroscope signals, to obtain the optimum combination of the sensors for gait classification, which is crucial to implement a power-efficient mobile smart shoes system. In addition, we investigated the optimal length of data segmentation based on the gait cycle parameters, reduction of the feature dimensions and feature selection for the classification of the gait patterns. Benchmark tests among several machine learning algorithms were conducted using random forest, k-nearest neighbor (KNN), logistic regression and support vector machine (SVM) algorithms for the classification task. Our experiments demonstrated the combination of accelerometer and gyroscope sensor features with SVM achieved the best performance with 89.36% accuracy, 89.76% precision and 88.44% recall. This research suggests a new state-of-the-art gait classification approach, specifically on detecting human gait abnormalities. View Full-Text
Keywords: smart shoes; gait analysis; feature analysis; pronation; supination; accelerometer; gyroscope; pressure sensor smart shoes; gait analysis; feature analysis; pronation; supination; accelerometer; gyroscope; pressure sensor
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MDPI and ACS Style

Sunarya, U.; Sun Hariyani, Y.; Cho, T.; Roh, J.; Hyeong, J.; Sohn, I.; Kim, S.; Park, C. Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns. Sensors 2020, 20, 6253. https://doi.org/10.3390/s20216253

AMA Style

Sunarya U, Sun Hariyani Y, Cho T, Roh J, Hyeong J, Sohn I, Kim S, Park C. Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns. Sensors. 2020; 20(21):6253. https://doi.org/10.3390/s20216253

Chicago/Turabian Style

Sunarya, Unang, Yuli Sun Hariyani, Taeheum Cho, Jongryun Roh, Joonho Hyeong, Illsoo Sohn, Sayup Kim, and Cheolsoo Park. 2020. "Feature Analysis of Smart Shoe Sensors for Classification of Gait Patterns" Sensors 20, no. 21: 6253. https://doi.org/10.3390/s20216253

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