@article{hong_kumar_patrick_moon_hur_2023, title={A Feasibility Study of Piecewise Phase Variable Based on Variable Toe-Off for the Powered Prosthesis Control: A Case Study}, volume={8}, ISSN={["2377-3766"]}, DOI={10.1109/LRA.2023.3256927}, abstractNote={Achieving stable walking and proper assistance in prosthesis control requires synchronized control, treating the user and the prosthesis as a coupled system. Furthermore, speed adaptability is essential for controlling the prosthesis at different walking speeds. One approach involves using a phase variable to estimate the user's gait phase and synchronize prosthesis control accordingly. However, the current phase variable (i.e., PV) fails to reflect variable toe-off timing at different speeds, despite individuals having different toe-off timings per walking speed. To address this issue, we propose a piecewise phase variable (i.e., PW-PV) that accounts for different toe-off timings while estimating the user's gait phase at various speeds. We conducted a treadmill walking experiment with two participants (one healthy and one amputee) using a custom-built powered prosthesis to validate the PW-PV's feasibility. We collected and analyzed joint kinematics, kinetics, and ground reaction force data during the experiment. The PW-PV implementation resulted in faster load transfer and a more natural rollover for both participants during walking. This allowed healthy and amputee participants to experience longer push-off durations of 10.6% and 15.2%, respectively, and greater ankle push-off work of 7.3% and 16.9%. Furthermore, with the PW-PV, the amputee participant demonstrated higher vertical ground reaction forces of 5.4% and 4.7% on her prosthesis side leg during load acceptance and push-off periods, potentially suggesting increased confidence in using the prosthesis. We anticipate that by using the proposed phase variable, we will be able to provide more appropriate and timely assistance to individuals at variable walking speeds.}, number={5}, journal={IEEE ROBOTICS AND AUTOMATION LETTERS}, author={Hong, Woolim and Kumar, Namita Anil and Patrick, Shawanee' and Moon, Sunwoong and Hur, Pilwon}, year={2023}, month={May}, pages={2590–2597} }
 @article{hong_huang_2023, title={Towards Personalized Control for Powered Knee Prostheses: Continuous Impedance Functions and PCA-Based Tuning Method}, ISSN={["1945-7898"]}, DOI={10.1109/ICORR58425.2023.10304689}, abstractNote={Optimizing control parameters is crucial for personalizing prosthetic devices. The current method of finite state machine impedance control (FSM-IC) allows interaction with the user but requires time-consuming manual tuning. To improve efficiency, we propose a novel approach for tuning knee prostheses using continuous impedance functions (CIFs) and Principal Component Analysis (PCA). The CIFs, which represent stiffness, damping, and equilibrium angle, are modeled as fourth-order polynomials and optimized through convex optimization. By applying PCA to the CIFs, we extract principal components (PCs) that capture common features. The weights of these PCs serve as tuning parameters, allowing us to reconstruct various impedance functions. We validated this approach using data from 10 able-bodied individuals walking. The contributions of this study include: i) generating CIFs via convex optimization; ii) introducing a new tuning space based on the obtained CIFs; and iii) evaluating the feasibility of this tuning space.}, journal={2023 INTERNATIONAL CONFERENCE ON REHABILITATION ROBOTICS, ICORR}, author={Hong, Woolim and Huang, He}, year={2023} }
 @article{ryu_hong_hur_2023, title={Towards Realistic Prosthetic Gait Simulations: Enhancing the Accuracy of OpenSim Analysis by Integrating the Transfemoral Prosthesis Model}, ISSN={["1944-9445"]}, DOI={10.1109/RO-MAN57019.2023.10309521}, abstractNote={Powered transfemoral prostheses offer the potential to improve mobility and quality of life for individuals with amputations. This study aimed to develop and validate an OpenSim model of a subject with a unilateral transfemoral amputation wearing a powered transfemoral prosthesis and to compare the model’s performance with that of a model without prosthesis characteristics. We utilized experimental walking data from a single transfemoral amputee subject to demonstrate the feasibility of the model. Inverse kinematics and inverse dynamics were performed to compare the results using the encoder and current data of the knee and ankle actuators, which served as ground truth. The model with prosthesis characteristics demonstrated a closer match to the actuator data, particularly during the stance phase, suggesting that it better reflects the dynamic features of a real powered prosthesis. However, discrepancies were observed during the swing phase, highlighting the need for further refinements. This study provides valuable insights into the importance of incorporating prosthesis characteristics in biomechanical models to simulate joint behavior accurately. It has implications for the development and assessment of prosthetic devices.}, journal={2023 32ND IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, RO-MAN}, author={Ryu, Hyungseok and Hong, Woolim and Hur, Pilwon}, year={2023}, pages={600–606} }
 @article{hong_lee_hur_2023, title={Piecewise Linear Labeling Method for Speed-Adaptability Enhancement in Human Gait Phase Estimation}, volume={31}, ISSN={["1558-0210"]}, DOI={10.1109/TNSRE.2022.3229220}, abstractNote={Human gait phase estimation has been studied in the field of robotics due to its importance in controlling wearable devices (e.g., robotic prostheses or exoskeletons) in a synchronized manner with the user. Researchers have attempted to estimate the user’s gait phase using a learning-based method, as data-driven approaches have recently emerged in the field. In this study, we propose a new labeling method (i.e., a piecewise linear label) to have the estimator learn the ground truth based on variable toe-off onset at different walking speeds. Using whole-body marker data, we computed the angular positions and velocities of thigh and torso segments and utilized them as input data for model training. Three models (i.e., general, slow, and normal-fast) were obtained based on long short-term memory (LSTM). These models are compared in order to identify the effect of the piecewise linear label at various walking speeds. As a result, when the proposed labeling method was used while training the general model, the estimation accuracy was significantly improved. This fact was also found when estimating the user’s gait phase during the mid-stance phase. Furthermore, the proposed method maintained good performance in detecting the heel-strike and toe-off. According to the findings of this study, the newly proposed labeling method could improve speed-adaptability in gait phase estimation, resulting in outstanding accuracy for both gait phase, heel-strike, and toe-off estimation.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Hong, Woolim and Lee, Jinwon and Hur, Pilwon}, year={2023}, pages={628–635} }