Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography

Abstract

One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)–Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human–machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it.

Assessment of a Wearable Force- and Electromyography Device and Comparison of the Related Signals for Myocontrol

“…Researchers have tried to address this issue by increasing the number of sensors (Tenore et al, 2007), although it is known that four to six channels are acceptable for pattern detection (Young et al, 2012), and/or to find their optimal placement given the characteristics of the stump (Castellini and van der Smagt, 2009; Fang et al, 2015); several pattern recognition algorithms have been studied, such as artificial neural networks (Baspinar et al, 2013), linear discriminant analysis (Khushaba et al, 2009) and non-linear incremental learning (Gijsberts et al, 2014). However, one of the major drawbacks of sEMG signals is their variable nature: sweat, electrode shifts, motion artifacts, ambient noise, cross-talk among deep adjacent muscles and muscular fatigue can crucially affect them (Oskoei and Hu, 2007; Cram and Kasman, 2010; Merletti et al, 2011a; Castellini et al, 2014). In general, any change in the muscle configuration during and after the training of the machine learning algorithm (e.g., the position of the limb and the body and the weights to be lifted during grasping and carrying) must be taken into account (Scheme et al, 2010; Cipriani et al, 2011b).…”

On the development of intrinsically-actuated, multisensory dexterous robotic hands

“…Traditional mode-switching methods established on EMG amplitude only give very limited functions, discrete robot-like finger movements, and unintuitive control feelings. By introducing the pattern recognition method [64], a large progress has been made; however, there is still a big gap between the research and its real application [65,66]. Intrinsic timing-varying characters of the EMG signals, environmental change (electromechanical status, temperature, moisture, sweating, etc.)…”

Automatic grasp selection using a camera in a hand prosthesis

“…al. [3] showed that EMG control is still imperfect because EMG signals are stochastic and have issues with robustness. These issues manifest themselves in classification errors when selecting between multiple grasps in real-world settings.…”