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

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 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.

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices

“…Wi-Fi based HGR systems capture signal reflections due to human movements between the transmitter and receiver pair as raw CSI traces. It has a wide range of application in the field of surveillance [5], physical analytics [6], healthcare [7] and have become a potential study in the smart home environment [8,9]. It is evident that the following factors, namely the number of users [10] and access point (AP) [11], orientation and distance between the users, as well as the transmitter and receiver pair [12,13], environmental factors [14], interferences, and multipath fading effect in the sensing environment influence the recognition accuracy [15].…”

Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning

“…The dataset was made from the raw readings recorded from the sensors. A windowed moving average filter (MAF) was used to filter the data of both the gyroscope and accelerometer [ 44 ]. This filter was used in order to counter the bias drift of the inertial sensors [ 45 ], which can be represented by the following equation [ 46 ]: where is the output filtered data, while the input unfiltered data is x , and P is the length of the window.…”

A Short Review on the Cost, Design, Materials and Challenges of the Prosthetics Leg Development and Usage

“…A new method by means of introducing human gait inference (HGI) in prosthetic where it able to predict the movement of amputated leg parts namely the shanks, thigh and foot has been proposed. This gait inference prosthesis is being called as GaIn, targeting individual with double transfemoral amputation where it is designed to predict the movement of lower legs based on the thighs movement [37]. However, it is expected that GaIn will cause discomfort to the user and adjustment should be applied so that adaptation towards the diversity and urban situations can be achieved [37].…”