@article{alili_nalam_li_liu_feng_si_huang_2023, title={A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis}, volume={31}, ISSN={["1558-0210"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85147231065&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2023.3236217}, abstractNote={The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, author={Alili, Abbas and Nalam, Varun and Li, Minhan and Liu, Ming and Feng, Jing and Si, Jennie and Huang, He}, year={2023}, pages={895–903} }
 @article{shah_fleming_nalam_liu_huang_2022, title={Design of EMG-driven Musculoskeletal Model for Volitional Control of a Robotic Ankle Prosthesis}, volume={2022-October}, ISSN={["2153-0858"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85146352781&partnerID=MN8TOARS}, DOI={10.1109/IROS47612.2022.9981305}, abstractNote={Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but are inadequate in adapting to variations in non-cyclic and unpredictable tasks. This study aims to address this challenge by designing a novel electromyography (EMG)-driven musculoskeletal model for volitional control of a robotic ankle-foot prosthesis. The proposed controller ensures continuous control of the device, allowing users to freely manipulate the prosthesis behavior. A Hill-type muscle model was implemented to model a dorsiflexor and a plantarflexor to function around a virtual ankle joint. The model parameters for a subject specific model was determined by fitting the model to the experimental data collected from an able-bodied subject. EMG signals recorded from antagonist muscle pairs were used to activate the virtual muscle models. This model-based approach was then validated via offline simulations and real-time prosthesis control. Additionally, the feasibility of the proposed prosthesis control on assisting the user's functional tasks was demonstrated. The present control may further improve the function of robotic prosthesis for supporting versatile activities in individuals with lower-limb amputations.}, journal={2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)}, author={Shah, Chinmay and Fleming, Aaron and Nalam, Varun and Liu, Ming and Huang, He}, year={2022}, pages={12261–12266} }
 @article{nalam_huang_2021, title={Empowering prosthesis users with a hip exoskeleton}, volume={27}, ISSN={["1546-170X"]}, url={http://dx.doi.org/10.1038/s41591-021-01529-w}, DOI={10.1038/s41591-021-01529-w}, number={10}, journal={NATURE MEDICINE}, publisher={Springer Science and Business Media LLC}, author={Nalam, Varun and Huang, He}, year={2021}, month={Oct}, pages={1677–1678} }
 @article{alili_nalam_li_liu_si_huang_2021, title={User Controlled Interface for Tuning Robotic Knee Prosthesis}, ISSN={["2153-0858"]}, url={http://dx.doi.org/10.1109/iros51168.2021.9636264}, DOI={10.1109/IROS51168.2021.9636264}, abstractNote={The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the optimal control parameters based on the provided knee joint profile that the prosthesis is expected to replicate during gait safely. This paper presents an intuitive interface designed for the prosthesis users and clinicians to choose the preferred knee joint profile during gait and use the autotuner to replicate in the prosthesis. The interface-based approach is validated by observing the ability of the tuning algorithm to successfully converge to various alternate knee profiles by testing on two able-bodied subjects walking with a robotic knee prosthesis. The algorithm was found to converge successfully in an average duration of 1.15 min for the first subject and 2.31 min for the second subject. Further, the subjects displayed different preferences for optimal profiles reinforcing the need to tune alternate profiles. The implications of the results in the tuning of robotic prosthetic devices are discussed.}, journal={2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)}, publisher={IEEE}, author={Alili, Abbas and Nalam, Varun and Li, Minhan and Liu, Ming and Si, Jennie and Huang, He}, year={2021}, pages={6190–6195} }