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Open AccessArticle

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering from Double Trans-Femoral Amputation

Department of Data Analysis and Artificial Intelligence, Faculty of Computer Science, National Research University Higher School of Economics, 20 Myasnitskaya ulitsa, Moscow 101000, Russia
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Sensors 2018, 18(12), 4146; https://doi.org/10.3390/s18124146
Received: 24 October 2018 / Revised: 15 November 2018 / Accepted: 20 November 2018 / Published: 26 November 2018
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the legs, which can be applied in prosthesis to imitate the missing part of the leg in walking. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55° prediction error for shank movements on average. However, a patient’s intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices. View Full-Text
Keywords: human activity recognition; gait analysis; human gait inference; wearable sensors; limb amputation; lower limbic prosthesis; machine learning; recurrent neural networks human activity recognition; gait analysis; human gait inference; wearable sensors; limb amputation; lower limbic prosthesis; machine learning; recurrent neural networks
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Chereshnev, R.; Kertész-Farkas, A. GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering from Double Trans-Femoral Amputation. Sensors 2018, 18, 4146.

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