Walking on irregular surfaces and in the presence of unexpected events is a challenging problem for bipedal machines. Up to date, their ability to cope with gait disturbances is far less successful than humans&#x2019;: Neither trajectory controlled robots, nor dynamic walking machines (Limit Cycle Walkers) are able to handle them satisfactorily. On the contrary, humans reject gait perturbations naturally and efficiently relying on their sensory organs that, if needed, elicit a recovery action. A similar approach may be envisioned for bipedal robots and exoskeletons: An algorithm continuously observes the state of the walker and, if an unexpected event happens, triggers an adequate reaction. This paper presents a monitoring algorithm that provides immediate detection of any type of perturbation based solely on a phase representation of the normal walking of the robot. The proposed method was evaluated in a Limit Cycle Walker prototype that suffered push and trip perturbations at different moments of the gait cycle, providing 100&#x25; successful detections for the current experimental apparatus and adequately tuned parameters, with no false positives when the robot is walking unperturbed.

J. A. Gallego

A. Forner-Cordero

J. C. Moreno

E. A. Turowska

J. L. Pons

Detection of Gait Perturbations Based on Proprioceptive Information. Application to Limit Cycle Walkers

Human-robot interaction has been going stronger and stronger, up to find a notorious level on brain-machines interfaces. This assistive technology offers a great hope for patients suffering severe neuromuscular disorders. Starting from the current limitations hindering its extensive application outside the research laboratories, this paper reviews findings and prospects on functional magnetic resonance imaging showing how fMRI can help to overcome those limitations, while playing a key role on improving the development of brain-machine interfaces based on electroencephalography. The different types of derived benefits for this interfaces, as well as the different kinds of impact on their components, are presented under a field classification that reveals the distinctive roles that fMRI can play on the present context. The review concludes that fMRI provides complementary knowledge of immediate application, and that a greater profit could be obtained from the own EEG signal by integrating both neuroimaging modalities.

Luis J. Barrios

Maria D. del Castillo

Jos&#xe9; I. Serrano

Jos&#xe9; L. Pons

A Review of fMRI as a Tool for Enhancing Eeg-Based Brain-Machine Interfaces

One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.

Giuseppina Gini

Matteo Arvetti

Ian Somlai

Michele Folgheraiter

Acquisition and Analysis of EMG Signals to Recognize Multiple Hand Movements for Prosthetic Applications

The haptic feedback, which is natural in assistive devices intended for visually impaired persons, has been only recently explored for people with motor disability. The aim of this work is to study its potential, particularly for assistance in the driving of powered wheelchairs. After a review of the literature for the previous related work, we present the methodology and the implementation procedure of a haptic feedback control system on a prototype of a smart wheelchair. We will also describe the approaches utilized to determine the appropriate force feedback that will ensure a cooperative behaviour of the system, and we will detail the two haptic driving modes that were developed, namely the active and passive modes. Experiments on a real prototype were carried out to study the contribution of the method in powered wheelchair driving and to evaluate the interest of the force feedback on the control joystick of the wheelchair. They are discussed on the basis of performance measures.

Mohammed-Amine Hadj-Abdelkader

Guy Bourhis

Brahim Cherki

Haptic Feedback Control of a Smart Wheelchair

The objective of this research effort is to integrate therapy instruction with child-robot play interaction in order to better assess upper-arm rehabilitation. Using computer vision techniques such as Motion History Imaging (MHI), edge detection, and Random Sample Consensus (RANSAC), movements can be quantified through robot observation. In addition, incorporating prior knowledge regarding exercise data, physical therapeutic metrics, and novel approaches, a mapping to therapist instructions can be created allowing robotic feedback and intelligent interaction. The results are compared with ground truth data retrieved via the Trimble 5606 Robotic Total Station and visual experts for the purpose of assessing the efficiency of this approach. We performed a series of upper-arm exercises with two male subjects, which were captured via a simple webcam. The specific exercises involved adduction and abduction and lateral and medial movements. The analysis shows that our algorithmic results compare closely to the results obtain from the ground truth data, with an average algorithmic error is less than 9&#x25; for the range of motion and less than 8&#x25; for the peak angular velocity of each subject.

Douglas A. Brooks

Ayanna M. Howard

Quantifying Upper-Arm Rehabilitation Metrics for Children through Interaction with a Humanoid Robot

In this work a time-frequency approach to estimate the Cortico-Muscular Coherence for the detection of the movement intent is presented, assessed on simulated data, and evaluated experimentally during different motor tasks performed by healthy subjects and patients suffering from different types of tremor. Cortico-Muscular Coherence is an index of the coupling of EEG signal in the cortical area with sEMG activity in the frequency domain, and its contributions in the beta band (15&#x2013;30 Hz) have been associated to the movement intent. Cortico-Muscular Coherence estimation is here achieved by considering a closed-loop representation of the signals under analysis obtained through Multivariate Auto Regressive modeling. Significance levels for Cortico-Muscular Coherence are assessed by means of a surrogate data analysis approach. The detection technique is able to reveal significant Cortico-Muscular Coherence changes in 79&#x25; of the experimental trials, with a mean anticipation of 1.35 s with respect to movement onset. Time-frequency estimation of Cortico-Muscular Coherence can provide an insight for the development of a multimodal BCI able to integrate information from the brain activity in the functioning of assistive devices.

G. Severini

S. Conforto

M. Schmid

T. D'Alessio

A Multivariate Auto-Regressive Method to Estimate Cortico-Muscular Coherence for the Detection of Movement Intent

Troubles in social communication as well as deficits in the cognitive treatment of emotions are supposed to be a fundamental part of autism. We present a case study based on multimodal interaction between a mobile robot and a child with autism in spontaneous, free game play. This case study tells us that the robot mediates the interaction between the autistic child and therapist once the robot-child interaction has been established. In addition, the child uses the robot as a mediator to express positive emotion playing with the therapist. It is thought that the three-pronged interaction i.e., child-robot-therapist could better facilitate the transfer of social and emotional abilities to real life settings. Robot therapy has a high potential to improve the condition of brain activity in autistic children.

I. Giannopulu

G. Pradel

From Child-Robot Interaction to Child-Robot-Therapist Interaction: A Case Study in Autism

Robotic rehabilitation of CVA (stroke) survivors is an emerging field. However, the development of effective gait rehabilitation robots used to treat stroke survivors is and remains a challenging task. This article discusses existing approaches and gives an overview of limitations with existing wearable robots. Challenges and potential solutions are being discussed in this article. Most difficulties lie in the implementation of physical and cognitive human robot interfaces. Many issues like actuation principles, control strategies, portability and wearing comfort, such as correct determination of user intention and effective guidance have to be tackled in future designs. Different solutions are being proposed. Clever anthropometric design and smart brain computer interfaces are key factors in effective exoskeleton design.

Claude Lagoda

Juan C. Moreno

Jose Luis Pons

Human-Robot Interfaces in Exoskeletons for Gait Training after Stroke: State of the Art and Challenges

About this journal

Applied Bionics and Biomechanics publishes original research articles as well as review articles that seek to understand the mechanics of biological systems, or that use the functions of living organisms as inspiration for the design of new devices.

Journal profile

Meet the editorial board

Chief Editor, Professor Qiguo Rong, is the Vice Chair of the Department of Mechanics and Engineering Science at Peking University, China. His research focuses on the biomechanical behaviors of musculoskeletal systems.

Editor spotlight

Submit your proposal

Do you think there is an emerging area of research that really needs to be highlighted? Or an existing research area that has been overlooked or would benefit from deeper investigation? Raise the profile of a research area by leading a Special Issue.

Special Issues

Author guidelines

Editorial board

Databases and indexing

Applied Bionics and Biomechanics | Hindawi

title

Applied Bionics and Biomechanics publishes original research articles as well as review articles that seek to understand the mechanics of biological systems, or that use the functions of living organisms as inspiration for the design of new devices.

description

https://images.hindawi.com/journals/abb/abb.banner.jpg

twitter:card

twitter:description

twitter:site

twitter:title

twitter:url

twitter:image

Applied Bionics and Biomechanics

This Issue is now open for submissions.
Papers are published upon acceptance, regardless of the Special Issue publication date.