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.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HD064968-01
Application #
7872426
Study Section
Special Emphasis Panel (ZRG1-MOSS-F (15))
Program Officer
Quatrano, Louis A
Project Start
2010-04-15
Project End
2012-03-31
Budget Start
2010-04-15
Budget End
2011-03-31
Support Year
1
Fiscal Year
2010
Total Cost
$188,056
Indirect Cost
Name
University of Rhode Island
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
144017188
City
Kingston
State
RI
Country
United States
Zip Code
02881
Zhang, Fan; Liu, Ming; Harper, Stephen et al. (2014) Engineering platform and experimental protocol for design and evaluation of a neurally-controlled powered transfemoral prosthesis. J Vis Exp :
Fan Zhang; He Huang (2013) Source selection for real-time user intent recognition toward volitional control of artificial legs. IEEE J Biomed Health Inform 17:907-14
Du, Lin; Zhang, Fan; Liu, Ming et al. (2012) Toward design of an environment-aware adaptive locomotion-mode-recognition system. IEEE Trans Biomed Eng 59:2716-25
Hernandez, Robert; Zhang, Fan; Zhang, Xiaorong et al. (2012) Promise of a low power mobile CPU based embedded system in artificial leg control. Conf Proc IEEE Eng Med Biol Soc 2012:5250-3
Zhang, Fan; Liu, Ming; Huang, He (2012) Preliminary study of the effect of user intent recognition errors on volitional control of powered lower limb prostheses. Conf Proc IEEE Eng Med Biol Soc 2012:2768-71
Zhang, Xiaorong; Wang, Ding; Yang, Qing et al. (2012) An automatic and user-driven training method for locomotion mode recognition for artificial leg control. Conf Proc IEEE Eng Med Biol Soc 2012:6116-9
Zhang, Fan; Fang, Zheng; Liu, Ming et al. (2011) Preliminary design of a terrain recognition system. Conf Proc IEEE Eng Med Biol Soc 2011:5452-5
Zhang, Xiaorong; Liu, Yuhong; Zhang, Fan et al. (2011) On Design and Implementation of Neural-Machine Interface for Artificial Legs. IEEE Trans Industr Inform 2011:1
Huang, He; Zhang, Fan; Hargrove, Levi J et al. (2011) Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Trans Biomed Eng 58:2867-75
Huang, He; Dou, Zhi; Zhang, Fan et al. (2011) Improving the performance of a neural-machine interface for artificial legs using prior knowledge of walking environment. Conf Proc IEEE Eng Med Biol Soc 2011:4255-8

Showing the most recent 10 out of 15 publications