This paper presents a robust control approach to control the movement of a prosthetic hand based on an estimation of the finger angles using surface electromyographic (sEMG) signals. All the available prosthesis uses the motion control strategy which is pre-programmed get initiated when some threshold value of the measured sEMG signal is reached for a particular motion set. Here we use a novel approach to model the finger angle which utilizes System Identification (SI) techniques. The dynamic model obtained allows the instantaneous control for the finger motions. sEMG data is acquired using an array of nine sensors and the corresponding finger angle is acquired using a finger angle measuring device and a data glove. A nonlinear Teager–Kaiser Energy (TKE) operator based nonlinear spatial filter is used to filter sEMG data whereas the angle data is filtered using a Chebyshev type-II filters. An EMG-angle estimation model is proposed then the estimated angles are used to control to control movement of a prosthetic hand using a robust approach which can deal with modeling uncertainty. The overall performance of the prosthetic hand are measured based on numerical simulation. The resulting fusion based output of this approach plus the robust controller gives improved the prosthetic hand motion control.

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Taking care of older adults is among the major challenges currently faced by ageing populations. Researchers, designers, and engineers have proposed socially assistive robots as one way of helping elderly people stay in their homes longer. In a systematic literature review, this paper wants to investigate if and how evaluations of the acceptance of socially assistive robots by older people take into account sociodemographic factors. The results indicate that this only holds true for a few studies. Research that incorporates age, gender, education, and so forth; clearly shows that these key factors have a significant impact. However, the relations are complex and experience with technology mitigates the influence of sociodemographic factors on acceptance. Assistive devices should be adaptable to individual needs to be able to consider all these factors.

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Restoration of walking ability of Spinal Cord Injury subjects can be achieved by different approaches, as the use of robotic exoskeletons or electrical stimulation of the user’s muscles. The combined (hybrid) approach has the potential to provide a solution to the drawback of each approach. Specific challenges must be addressed with specific sensory systems and control strategies. In this paper we present a system and a procedure to estimate muscle fatigue from online physical interaction assessment to provide hybrid control of walking, regarding the performances of the muscles under stimulation.

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There has been a dramatic increase over the last decade in research on technologies for enhancing movement training and exercise for people with a disability. This paper reviews some of the recent developments in this area, using examples from a National Science Foundation initiated study of mobility research projects in Europe to illustrate important themes and key directions for future research. This paper also reviews several recent studies aimed at combining movement training with plasticity or regeneration therapies, again drawing in part from European research examples. Such combination therapies will likely involve complex interactions with motor training that must be understood in order to achieve the goal of eliminating severe motor impairment.

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Although electromechanical-assisted gait training after stroke seems to be effective, in the absence of a direct comparison between electromechanical devices it is not clear which device may be the most effective for recovery of walking. The aim of this study was therefore to compare the effects of different devices used in gait training after stroke.

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