Multi-Modal Sensing Techniques for Interfacing Hand Prostheses: A Review

Abstract

This paper provides a comprehensive survey of current state of the bio-sensing technologies focusing on hand motion capturing and its application to interfacing hand prostheses. These sensing techniques include electromyography (EMG), sonomyography (SMG), mechnomyography (MMG), electroneurography (ENG), electroencephalograhy (EEG), electrocorticography (ECoG), intracortical neural interfaces, near infrared spectroscopy (NIRS), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), etc. Relevant approaches that interpret bio-signals in the view of prosthetic hand manipulation are involved in as well. Multi-modal sensory fusion provides a new strategy in this area, and the latest multi-modal sensing techniques are surveyed. The paper also outlines the new challenges and directions: exploration of robust sensing technology, multi-modal sensory fusion, on-line signal processing and learning algorithms and bio-feedbacks.

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

“…Myocontrol is still limited to a few DOFs (Arjunan and Kumar, 2010; Yang et al, 2014), and surface electromyography (sEMG) signals are deemed to be no longer enough (Jiang et al, 2012a). 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).…”

A Multi-sensor Approach for Biomimetic Control of a Robotic Prosthetic Hand

“…One of the reasons identified in the literature is due to the sEMG signals that are affected by sweating, electrode shift, ambient noise, fatigue, cross-talk between adjacent muscles, signal drifting, and force level variation (Merletti et al 2010;Atzori et al 2014;Al-Timemy et al 2015). Therefore several alternative sensing techniques including pressure sensors have recently been explored (Fang et al 2015). Ravindra and et al compared three non-invasive HMIs (sEMG, ultrasound imaging, pressure sensing) and concluded that pressure sensing represented a valid alternative/augmentation to sEMG because of its potential to provide the highest PR prediction accuracy, signal stability over time, wearability, simplicity in socket embedding, and affordability of cost.…”

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.)…”