Terrain identification for prosthetic knees based on electromyographic signal features

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: Related Workmentioning

confidence: 99%

“…Two previous studies have attempted to use EMG signals to identify locomotion modes for prosthetic leg control [10, 27]. Jin et al [27] used features extracted from EMG signals from a complete stride cycle. Using such features, the algorithm results in a time delay of one stride cycle in real-time.…”

Section: Related Workmentioning

confidence: 99%

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

Liu

et al. 2012

IEEE Trans. Ind. Inf.

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

“…al. [5] developed an algorithm that identified terrain during levelground walking. However, features were extracted from one entire gait-cycle, which would make implementation in a realtime system dangerous; the delay introduced by waiting one whole gait-cycle to determine terrain can easily lead to falls.…”

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.

2007 IEEE International Conference on Integration Technology

“…As an important aspect of gait recognition, terrain identification is a process of environment recognition, which determines the adaptive capacity to environment for IBL. Jin et al [12] proposed a terrain identification method using electromyography (EMG) signal, where corresponding relations between EMG signal and terrain type were obtained and used to control the transfemoral prosthesis based on the feature extraction of EMG signal. In their experiments, only one participant was selected for walking test under different conditions, and the accuracy rate for identification ranged from 75% ∼ 100% according to the type of terrain.…”

Section: Introductionmentioning

confidence: 99%

Terrain Identification of Intelligent Bionic Leg Based on Ground Reaction Force

Wang

Xie

et al. 2007

Biomedical research indicates that human gait is considerable different when walking on different terrain. In this paper, terrain identification scheme for an intelligent bionic leg (IBL) is presented. Terrain identification system is modeled by the procedure neural networks (PNNs) and is used to identify the current terrain type based on the ground reaction force (GRF) information. According to identification result, damping moment of magneto-rheological (MR) damper mounted on knee joint of IBL is adjusted to accommodate changing conditions. This paper presents the PNNs model and calculates the related arithmetics. Terrain identification results prove the rationality and feasibility of the proposed scheme.