Gait detection algorithm applying for transtibial prosthesis
Creators: name: Vu, Huong Thi Thu
Publisher: name: Zenodo
Year: 2023

description: New generations of powered transtibial prostheses have been developed through-out the last decade to restore the functionalities of the biological leg and the ap-pearance of the amputation. However, there are still limitations in technology asamputees wearing prostheses walk at slower speeds and lose more metabolismthan those with the biological limb. Prosthetic design efforts have mainly beenfocused on mechanical device, while gait phase detection algorithms are con-strained. Especially gait phase detection is considered a great important param-eter for creating the artificial limb to define where the prosthesis is in the gaitcycle during walking. Thus, this thesis aims to develop the gait phase predic-tion embedded into the prosthetic control strategy for improving the precisionand stability of the prosthesis so that the user’s safety is enhanced. Therefore, anovel gait phase prediction algorithm is proposed in this thesis that could predicta full gait cycle discretised within one percent. This algorithm is called an Expo-nentially Delayed Fully Connected Neural Network (ED-FNN). Currently, moststudies propose methods to detect from two to eight phases of the step. However,for real-world applications, more than this may be required for controlling activeprosthetic devices. As a result, a more densely sampled gait phase is needed toobtain more important gait phase information and give more control possibilities.The performance of the proposed model ED-FNN is evaluated in terms of Accu-racy, Mean Square Error (MSE). The obtained results of MSE demonstrate thatthe proposed method is a robust and reliable solution for gait phase detection. Be-sides, the prosthesis has yet to walk like a human because it is difficult to adjustthe movements to respond to the transitions to different environments that theyencounter during daily activities such as going up and down stairs, slopes, stop-ping walking, and flat walking. To resolve this issue, the prosthesis is providedwith a locomotion mode recognition algorithm which helps the prosthesis switchsmoothly between different locomotion modes. Though existing studies have em-ployed several classical machine learning methods for locomotion mode recogni-tion, these methods are less effective for data with complex decision boundariesand result in misclassifications of motion recognition. Deep learning is the lat-est advancement of machine learning which potentially resolves these limitationsbecause deep learning is more sophisticated, and it can learn and make intelli-gent decisions on its own. This thesis, therefore, introduces several deep learningmodels such as Recurrent Neural Network (RNN), Long Short-Term Memory(LSTM) neural network, and Convolutional Neural Network (CNN) for locomo-tion mode recognition, including level ground walking (LW), standing (ST), andstair ascent/stair descent (SA/SD) and evaluates their performance of them. Theresults show that CNN and LSTM models outperform other models, and thesemodels are promising for applying locomotion mode recognition in real-time forrobotic prostheses. The proposed gait phase detection method was applied for theAMP-foot, which is an active prosthesis in the Robotics & Multibody Mechanicsresearch group. In order to collect the necessary data to train the neural networkdeep learning models, the thesis proposes an electronic system design for the mea-surement unit, which mainly consists of a microcontroller board, one IMU sensorattached to the lower shank, and two force sensors mounted under the foot. Thisunit will measure the acceleration and velocity of the subjects when they walk indifferent scenarios. Although the experiments are conducted in an offline setting,due to the forecasting capabilities of the deep learning approaches, this systemprovides an opportunity to eliminate detection delays for real-time applicationsand the capability to reproduce the dynamic functions of the limbs of amputees.