Transfemoral Prostheses

Liu

et al. 2012

IEEE Trans. Ind. Inf.

The quality of life of leg amputees can be improved dramatically by using a cyber physical system (CPS) that controls artificial legs based on neural signals representing amputees’ intended movements. The key to the CPS is the neural-machine interface (NMI) that senses electromyographic (EMG) signals to make control decisions. This paper presents a design and implementation of a novel NMI using an embedded computer system to collect neural signals from a physical system - a leg amputee, provide adequate computational capability to interpret such signals, and make decisions to identify user’s intent for prostheses control in real time. A new deciphering algorithm, composed of an EMG pattern classifier and a post-processing scheme, was developed to identify the user’s intended lower limb movements. To deal with environmental uncertainty, a trust management mechanism was designed to handle unexpected sensor failures and signal disturbances. Integrating the neural deciphering algorithm with the trust management mechanism resulted in a highly accurate and reliable software system for neural control of artificial legs. The software was then embedded in a newly designed hardware platform based on an embedded microcontroller and a graphic processing unit (GPU) to form a complete NMI for real time testing. Real time experiments on a leg amputee subject and an able-bodied subject have been carried out to test the control accuracy of the new NMI. Our extensive experiments have shown promising results on both subjects, paving the way for clinical feasibility of neural controlled artificial legs.

Section: Introductionmentioning

confidence: 99%

On Design and Implementation of Neural-Machine Interface for Artificial Legs

Liu

et al. 2012

IEEE Trans. Ind. Inf.

Prosthetics &Amp; Orthotics International

“…A prosthetic knee joint serves a vital role in the overall function of a transfemoral prosthesis by providing stability during stance phase and controlled flexion during the swing phase of gait. 1 Stance phase knee stability is achieved by resisting knee flexion during weight bearing, which is termed stance phase control. Sophisticated prosthetic knee joint mechanisms utilize microprocessors and hydraulics to effectively control knee resistance and enable safe and efficient gait.…”

Section: Introductionmentioning

confidence: 99%

“…12 This has limits, however, since a hyper-stabilized polycentric knee joint, which may be preferable in developed countries to accommodate walking on uneven terrains, 13 impedes swing phase initiation in late stance. 12 Due to these functional limitations and issues of durability and reliability associated with more complex mechanisms, such as four-bar linkage knees, 1,8,11 it was concluded at a recent state-of-the -science meeting on the status of prosthetic technologies for developing countries that ‘there is no appropriate (knee joint) stance phase control technology available at this time’. 14…”

Section: Introductionmentioning

confidence: 99%

Mobility function of a prosthetic knee joint with an automatic stance phase lock

Andrysek

Klejman

Torres-Moreno

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

“…Currently, users can change the mode of their prosthesis manually at the cost of a relatively non-intuitive interface [7]. We feel that these interfaces are too cumbersome for biomimetic terrain adaptation.…”

Section: Introductionmentioning

confidence: 99%

A method to determine the optimal features for control of a powered lower-limb prostheses

Farrell

Herr

2011

Abstract-Lower-limb prostheses are rapidly advancing with greater computing power and sensing modalities. This paper is an attempt to begin exploring the trade-off between extrinsic and intrinsic control modalities. In this case, between electromyographic (extrinsic) and several internal sensors that can be used for intrinsic control. We propose a method that will identify the particular features, taken from two trans-femoral amputee and one trans-tibial amputee, during locomotion on varying terrain, that perfectly discriminate between locomotion modes. From this we are able to identify the source of the discriminability from a large-set of features that does not depend on the type of amputation. Also, we comment on the use of this algorithm in selecting the most discriminatory and least encumbering sensor/feature combination for transitions when the ground underneath the foot is unknown for trans-tibial amputees.