Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 24 October 2018 / Approved: 25 October 2018 / Online: 25 October 2018 (04:51:27 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Sensors 2018, 18, 4146
DOI: 10.3390/s18124146

Abstract

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 missing parts of the legs. 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 degree 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.

Subject Areas

human activity recognition; gait analysis; human gait inference; wearable sensors; limb amputation; lower limbic prosthesis; machine learning; recurrent neural networks

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