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• W2897511529 abstract "Human gait analysis is a common but also challenging method to diagnose and characterize gait disorders. In recent years, wearable technologies have been investigated for gait analysis including gait phase detection with promising results. In this paper, we investigated the feasibility of a textile-based strain sensor integrated into an ankle brace to detect gait phases. This threadlike resistive sensor detects elongations and is, flexible and easy to integrate into textiles. In a laboratory study, five healthy subjects wearing the ankle brace were asked to walk at various low speeds while the sensor data were recorded. Three commonly used machine learning algorithms (random forest, support vector machines, and neural network) were investigated to differentiate between four phases (initial contact, mid-stance, pre-swing, swing) of the walking data. The random forest classifier performed best on our data set followed by neural network and support vector machines. Using to-fold cross-validation, an average accuracy of 95.49% ± 2.32% was achieved. This result compares well to other methods such as IMU-based technologies and demonstrates the feasibility of our approach of using textile-based strain sensors to detect gait phases in human walking." @default.
• W2897511529 created "2018-10-26" @default.
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• W2897511529 date "2018-08-01" @default.
• W2897511529 modified "2023-09-26" @default.
• W2897511529 title "Preliminary Investigation of Textile-Based Strain Sensors for the Detection of Human Gait Phases Using Machine Learning" @default.
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• W2897511529 doi "https://doi.org/10.1109/biorob.2018.8487880" @default.
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