He Huang - US grants
Affiliations: | 2010 | Electrical Engineering | University of Maryland, College Park, College Park, MD |
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, He Huang is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2010 — 2011 | Huang, He | R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Towards Neural Control of Artificial Legs: Design of a Real-Time Fusion-Based Neu @ University of Rhode Island DESCRIPTION (provided by applicant): Advances in computerized and powered artificial legs show great promise to permit persons with lower limb amputations to perform versatile activities beyond level ground walking. These prostheses are, however, inadequate for users to perform seamless transitions between activities due to the lack of neural control. To "tell" the prosthesis the intended movement, the user must make extra body motions or use a remote key fob, which are both cumbersome and not robust. Obtaining decisions directly from the user through a neural control interface is crucial to providing accurate, intuitive control of computerized artificial legs. Our long-term goal is to develop a neural-controlled artificial knee and/or ankle to improve the function of computerized artificial legs and the quality of life of people with lower limb amputations. Towards this goal, we propose to develop a robust neural-machine interface that can recognize the user's intended lower limb tasks in real-time. A functional, embedded neural interfacing system will be delivered at the end of this project that may start a complete paradigm shift in the design of computerized artificial legs. The specific aims of this grant are: Aim 1: Develop a neural interface algorithm that accurately and responsively decodes the user's intended lower limb tasks and task transitions. Aim 2: Implement the algorithm designed in Aim 1 on real-time embedded hardware. Aim 3: Evaluate the real-time neural interfacing system on subjects with knee disarticulation or transfemoral amputations. We propose a neural-mechanical-fusion-based interfacing design for the development of the algorithm (Aim 1). The algorithm will integrate the neuromuscular control information gathered through electromyographic (EMG) recordings with mechanical feedback from the prosthesis to achieve improved accuracy for identifying user intent. A phase-dependent pattern recognition strategy is proposed to ensure a fast system time response for real-time application. Additional components such as sensor fault detectors and a finite-state machine will be designed to enhance the system robustness. The designed algorithm will be implemented on real-time testing hardware composed of a self-constrained instrumented leg and an embedded system (Aim 2). The data structures and programs will be optimized to make the best use of the embedded architecture and the multilevel memory hierarchy for real-time operation. The finalized real-time neural- machine interface will be evaluated on patients with knee disarticulation or transfemoral amputations, which are high and challenging levels (Aim 3). PUBLIC HEALTH RELEVANCE: The neural-machine interface developed for neural control of artificial legs will lead to improved functional usage of impaired limbs, reduced disability, and improved quality of life of patients with lower limb amputations. |
0.951 |
2012 — 2018 | Huang, He | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Rhode Island Although volitional control of prosthetic arms has been studied intensively in recent years, similar technology has not yet been developed for lower limb prostheses, due in part to the lack of control capability in current passive prosthetic legs. Two emerging technologies, powered lower limb prostheses and neural-machine interfaces (NMI), have opened up new possibilities for allowing leg amputees to operate prostheses intuitively and to perform various activities in a natural way. It remains, however, to demonstrate the feasibility of volitional control for powered prosthetic legs. This is the PI's goal in the current project: to develop and implement a novel volitional controller that allows users with transfemoral amputations to operate a multifunctional, powered prosthetic leg intuitively and safely. To this end, she will systematically investigate and quantify the interaction effects between lower limb amputees and powered artificial legs. Intrinsic control (i.e., control based on intrinsic mechanical feedback) for a prototype powered transfemoral (TF) prosthesis will be developed with finite-state machine and impedance control mechanisms so that it can assist amputees in performing various activities in weight bearing and non-weight bearing situations. The design of the volitional control will be based on a multi-model engineering framework that integrates intent decoders with intrinsic prosthesis control so as to create feed-forward control in a powered TF prosthesis while ensuring amputee safety. Finally, a proof-of-concept prototype of a volitionally-controlled, powered TF prosthesis will be implemented and evaluated in real time on patients with TF amputations. |
0.951 |
2013 — 2017 | Huang, He | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Hcc: Medium: Collaborative Research: Neural Control of Powered Artificial Legs @ University of Rhode Island Recent breakthroughs in the mechatronics of powered lower limb (LL) prostheses hold the promise of enabling restoration for the large and growing population of lower limb amputees of a broad spectrum of functionality (e.g., standing up when seated in a chair, climbing stairs, and even running). The PIs argue that to realize this potential it is essential to provide neural control of artificial legs. The application of existing upper limb (UL) neural control approaches is inappropriate to this end, because the UL and LL neural control mechanisms are significantly different. In particular, most activities involving the lower limbs recruit both involuntary (spinal cord) and voluntary (supra-spinal) neural control, present high dynamics, and require multi-joint coordination and control of unstable locomotion, characteristics which combine to make the design specifications for neural control of LL prostheses much more demanding than those for UL devices. In this project the PIs will address this challenge by developing an innovative neural control system for powered artificial legs that can recognize and exploit multi-scale user intent (e.g., general motor commands such as intended task vs. detailed motor commands such as intended joint motion) to modulate intrinsic (autonomous) control of multiple LL prosthetic joints for locomotor and nonlocomotor task performance. The goals are to support reverse-engineering of the neural control of human locomotion while creating innovative neural-machine interfacing (NMI) technology that enables users to control the dynamics of LL prostheses in a natural, adaptive and flexible way. Inspired by what is currently known about the neurological organization and function of the human motor control system, the PIs' approach is to design a novel NMI based on a combination of noninvasive scalp electroencephalography (EEG) and surface electromyography (EMG). The hypothesis is that fusion of low-level peripheral and high-level central neural control sources can achieve multi-scale user intent recognition with higher accuracy and more rapid response time than can be realized with either EEG or EMG alone. A hierarchical control scheme for powered LL prostheses, in which multi-scale user intent identified by the NMI modulates intrinsic (autonomous) control, will support intuitive and efficient prosthesis use in dynamic, multi-joint coordinated movements while significantly reducing the mental burden of the prosthesis user in locomotion because the cyclic motion is achieved autonomously (this is desired because we rarely think about knee and ankle control when walking). The PIs will also explore correlation across EEG and EMG signals, which may provide insight into neural adaption and the time course of cortical control during the initiation and generation of gait, including how the brain initiates walking and regulates motor output in anticipation of key events such as foot placement at landing or during stepping up and down, weight acceptance, and push-off into swing phase. Finally, the PIs will use translational research to validate their novel approach in patients with trans-femoral amputations (a high and challenging amputation level). |
0.951 |
2015 — 2018 | Huang, He Stallings, Jonathan |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ North Carolina State University Upper limb amputation is a major cause of disability for nearly 160,000 Americans, many of whom could benefit from emergent sophisticated robotic, multifunctional prosthetic arms/hands. In these advanced prostheses, movements are typically controlled by interpreting the user's electromyographic (EMG) signals from residual or reinnervated muscles. State-of-the-art pattern recognition (PR) has been the most promising EMG control interface for multifunctional artificial arms. However, EMG PR-based control algorithms often require lengthy and frequent algorithm training and lack reliability when the external loading or arm posture changes. This is partly because EMG PR is data-driven and does not account for the behavior of the underlying neural or biomechanical system from which the EMG signals are sourced. The objective of this project is to develop a novel EMG control of multifunctional transradial (TR) prostheses based on a systematic study of neuromuscular control and biomechanical roles of residual muscles in TR amputees. This research can potentially enhance the health, function, and quality of life of upper limb amputees. This project's concept, methods, and frameworks for enhancing EMG-based prosthesis control may be extended to other assistive robotics to benefit other patient populations such as stroke survivors. This project will impact STEM education by promoting project-based cross-training among K-12, undergraduate, and graduate students in underrepresented groups including females, minorities, and students with disabilities. The research may also impact the neuroscience and movement science communities by elucidating the control mechanism of the arm/hand and unveiling new knowledge of neuroplasticity and the internal model in upper limb amputees. |
0.942 |
2016 — 2020 | Huang, He | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ North Carolina State University Emerging powered lower limb prostheses hold great promise for restoring normative locomotion in amputees. However, these robotic devices currently lack inter- and intra-wearer adaptability to cope with wearers' physical variations and changes. Frequent manual and heuristic adjustment in clinics is required, which limits the practical use of these advanced prostheses. A new generation of prosthesis control that is intelligent, adaptable, and interactive is needed to better support walking function and improve the quality of life of lower limb amputees. The PIs' long-term research goal is to create bionic legs that can adapt to the individual amputee's physical and cognitive capabilities, coordinate with the wearer's movement and intent, adjust to changing environments, and essentially restore the full function of patients with lower limb impairments. To this end, the PIs' objective in this project is to create a novel optimal control framework for these prostheses. They will systematically address the challenge of supporting automatic adaptation to the wearer's physical capability while achieving desired gait performance for the integrated amputee-prosthesis system. And they will provide a preliminary design and evaluation for an interactive interface that would allow wearers to personalize prosthesis control safely and easily. Project outcomes will open up a new frontier of wearable robotics and lay the foundation for clinical translations of these innovative devices, which will impact not only the prosthetics and orthotics industry but also the robotics community by providing new knowledge relating to human-robot interaction, the biomechanics and neuromotor control community by elucidating the control mechanism of amputee locomotion, and healthcare in general by providing innovative and cost-effective prosthesis solutions. |
0.942 |
2016 — 2020 | Bozkurt, Alper Huang, He Ghosh, Tushar (co-PI) [⬀] Lee, Michael (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Sch: Int: Novel Textile Based Sensors For Inner Prosthetic Socket Environment Monitoring @ North Carolina State University Amputation is one of the major causes of disability. Sockets are the important prosthesis components and physical interface to integrate the prosthetic limbs mechanically with the amputee's residual limb to replace lost function. Objective monitoring of the inner socket environment (i.e. pressure, temperature, and humidity) and residual muscle activity during daily prosthesis use requires flexible, unobtrusive and multi-modal sensors that can be integrated into the socket structure without causing subject discomfort. The lack of such an inner-socket sensor technology has been a long-standing problem for evaluating the prosthesis socket, preventing the complications elicited by poor socket design and fit, and advancing the socket technologies. Therefore, advanced socket technologies are urgently needed and will be developed under this project to significantly reduce the number of clinic visits, lower the healthcare costs for amputees, and ultimately improve their quality of life. The impacts of this project will reach far beyond the immediate scientific and engineering contributions that result from it. The use of technologies to further understand the capability of textiles as sensing elements; to design novel systems to monitor the health; and to increase comfort and gait function of amputees in new ways, all provide priceless opportunities to motivate and educate younger generations, their educators and the public-at-large towards the future advancements in manufacturing and biomedical sensing innovation. |
0.942 |
2018 — 2021 | Huang, He | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ North Carolina State University The proposed research aims at designing robust, real time learning controllers for powered lower limb prosthesis worn by above-knee amputees. It centers on adaptive optimal tuning of prosthetic knee joint impedance parameters with an ultimate goal of achieving human-prosthesis symbiosis. Current state-of-the-art approaches rely on a predetermined collection of knee joint impedance parameters, resulted from tedious manual tuning in a clinic. In addition to a lack of adaptability to different users, current impedance controls do not adapt to different use environments. One of the key design challenge is due to the constant interaction between the human user and the robotic leg. As such, advanced robotics including those employing latest artificial intelligence technologies, control system theory and design, and existing biomechanics based controls cannot meet the needs of real time learning control of a powered prosthetic leg in a human-prosthesis system. Given the nature of the problem, reinforcement learning based adaptive optimal control, also referred to as adaptive dynamic programming (ADP), holds great promise to delivering the next generation of prosthesis control solutions. |
0.942 |
2018 — 2020 | Huang, He | R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Error Tolerance in Wearer-Robot Systems @ North Carolina State University Raleigh Project Summary Human's motor control system adopts various control mechanisms to tolerate internal or external error/noise, so that humans can maintain consistent task performance in an extremely robust way. Whether or not the same control principles can be employed by wearable robots (such as robotic prostheses and exoskeletons) to enhance the robustness of wearer- robot systems remains an open but paramountly important question. Answers to this question can lead us to understand the processes underlying wearer-robot interactions and make advanced wearable robots robust, safe-to-use, and acceptable by the wearers. Our long-term goal is to achieve seamless wearer-robot integration for movement augmentation and clinical translation of knowledge and technology in wearer-robot systems to improve the quality of life of individuals with motor deficits. Specifically, the objective of this proposal is to investigate novel error-tolerant mechanisms, inspired by human motor control principles, to improve the robustness and safety of powered transfemoral prostheses. By (1) systematically exploring the stability response of amputee-prosthesis system to imposed prosthesis errors and (2) mimicking how humans explore the control space and tolerate internal or external errors for task performance, we will demonstrate a new bio- inspired error-tolerant concept for robust control of robotic prostheses. Guided by strong preliminary design and study, our research objective will be accomplished by pursuing three specific aims: Aim 1) systematically determine the effects of imposed prosthesis errors on objectively measured walking stability of amputee-prosthesis systems, Aim 2) systematically determine the effects of imposed prosthesis errors on perceived walking stability in amputees, and Aim 3) demonstrate the capacity of robust lower limb prosthesis control by mimicking human motor control mechanisms. The goal of Aim 1 and Aim 2 is to comprehensively understand the consequences of prosthesis errors and the responses of amputee-prosthesis systems to these errors, which is an existing knowledge gap that hinders the development of robust robotic prosthesis controller. By quantifying and correlating the stability measures (both perceived and biomechanically defined indices), we will map a manifold surface that can truly reflect the responses of amputee-prosthesis system to prosthesis errors. In Aim 3, we propose to translate the knowledge learned in Aim 1 and 2 together with the human motor control theories into error-tolerant mechanisms for powered prostheses. Guided by Minimal Intervention Principle and the response manifold obtained in Aim 1 and 2, error-tolerant mechanisms will be designed to accurately detect prosthesis errors that lead to perceived instability and effectively correct errors via a forward model, and therefore in turn enhance the stability in amputee-prosthesis systems. The success of this proposal can provide theoretical foundations for the development of technologies that can improve amputees' safety in using robotic prostheses, enhance acceptance of these advanced devices, and improve the quality of life of amputees. |
0.931 |
2019 — 2022 | Huang, He Feng, Jing (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Integrating Human Wearers' Perception and Cognition Into Prosthesis Control Policy @ North Carolina State University This grant will support research that will contribute new knowledge related to human-prosthesis interactions and the personalization of robotic prosthesis control. Current powered lower limb (LL) robotic prostheses use intrinsic feedback of joint motion and forces to adjust joint impedance as a function of the current phase of gait. Personalization of the control parameters (controller tuning) is typically performed manually and heuristically by a clinician who modifies one parameter at a time until the amputee's gait "looks good" and the amputee self-reports satisfaction with the control. The research objective of this project is to develop a novel "wearer-led" auto-tuning procedure for LL robotic prostheses that considers and enhances cognitive aspects of the human-machine interaction during gait, such as the user's goals, required attention, cognitive workload, trust and comfort. This project will promote the progress of science and advance the national health by developing intelligent prosthesis controllers that can tune themselves to the personal preferences and perceptions of their users, thereby augmenting the physical performance of individuals with lower limb amputations and maximize their acceptance of the robotic limbs as a functional part of the body. Broader impacts of the project include K-12 outreach and educational efforts at the undergraduate and graduate levels intended to attract and retain women and underrepresented minorities into STEM fields. |
0.942 |