Preface

IEEE Trans. Biomed. Eng.

Hargrove

et al. 2011

469

416

In this study, we developed an algorithm based on neuromuscular–mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular–mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular–mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular–mechanical fusion for neural control of prosthetic legs.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion

Huang

IEEE Trans. Biomed. Eng.

Hargrove

et al. 2011

469

416

“…The risk of falling for persons with lower limb amputations is high because of a combination of the following two factors: (1) Lower limb amputations lead to altered balance, strength, and gait pattern [1]–[2]; (2) more than half of the population with lower limb amputations is elderly people (aged 65 years or older) [2]–[3], for whom falls are one of the major causes of serious injuries [4]. It has been reported that falls caused soft tissue injury, boney injury, deterioration in balance, fear of falling, and reduced participation in activities of daily living in patients with leg amputations [1]–[2].…”

Section: Introductionmentioning

confidence: 99%

Towards Design of a Stumble Detection System for Artificial Legs

IEEE Trans. Neural Syst. Rehabil. Eng.

D’Andrea

Nunnery³

et al. 2011

Recent advances in design of powered artificial legs have led to increased potential to allow lower limb amputees to actively recover stumbles. To achieve this goal, promptly and accurately identifying stumbles is essential. This study aimed to (1) select potential stumble detection data sources that react reliably and quickly to stumbles and can be measured from a prosthesis, and (2) investigate two different approaches based on selected data sources to detect stumbles and classify stumble types in patients with transfemoral (TF) amputations during ambulation. In the experiments, the normal gait of TF amputees was perturbed by a controllable treadmill or when they walked on an obstacle course. The results showed that the acceleration of prosthetic foot can accurately detect the tested stumbling events 140–240 ms before the critical timing of falling and precisely classify the stumble type. However, the detector based on foot acceleration produced high false alarm rates, which challenged its real application. Combining electromyographic (EMG) signals recorded from residual limb with the foot acceleration significantly reduced the false alarm rate but sacrificed the detection response time. The results of this study may lead to design of a stumble detection system for instrumented, powered artificial legs; however, continued engineering efforts are required to improve the detection performance and resolve the challenges that remain for implementing the stumble detector on prosthetic legs.

“…Lower limb amputation is a major cause of disability [1]. Recent advancements in powered artificial legs have made it possible to allow leg amputees to perform versatile tasks efficiently [2–3].…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of a neural-machine interface for artificial legs using prior knowledge of walking environment

Huang

Dou

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

et al. 2011

A previously developed neural-machine interface (NMI) based on neuromuscular-mechanical fusion has showed promise for recognizing user locomotion modes; however, errors of NMI during mode transitions were observed, which may challenge its real application. This study aimed to investigate whether or not the prior knowledge of walking environment could further improve the NMI performance. Linear Discriminant Analysis (LDA)-based classifiers were designed to identify user intent based on electromyographic (EMG) signals from residual muscles of leg amputees and ground reaction force (GRF) measured from the prosthetic leg. The prior knowledge of the terrain in front of the user adjusted the prior possibility in the discriminant function. Therefore, the boundaries of LDA were adaptive to the prior knowledge of the walking environment. This algorithm was evaluated on a dataset collected from one patient with a transfemoral (TF) amputation. The preliminary results showed that the NMI with adaptive prior possibilities outperformed the NMI without using the prior knowledge; it produced 98.7% accuracy for identifying tested locomotion modes, accurately predicted all the task transitions with 261–390 ms prediction time, and generated stable decision during task transitions. These results indicate the potential of using prior knowledge about walking environment to further improve the NMI for prosthetic legs.