Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study

Advancement in assistive technology has led to the commercial availability of multi-dexterous robotic prostheses for the upper extremity. The relatively low performance of the currently used techniques to detect the intention of the user to control such advanced robotic prostheses, however, limits their use. This article explores the use of force myography (FMG) as a potential alternative to the well-established surface electromyography. Specifically, the use of FMG to control different grips of a commercially available robotic hand, Bebionic3, is investigated. Four male transradially amputated subjects participated in the study, and a protocol was developed to assess the prediction accuracy of 11 grips. Different combinations of grips were examined, ranging from 6 up to 11 grips. The results indicate that it is possible to classify six primary grips important in activities of daily living using FMG with an accuracy of above 70% in the residual limb. Additional strategies to increase classification accuracy, such as using the available modes on the Bebionic3, allowed results to improve up to 88.83 and 89.00% for opposed thumb and non-opposed thumb modes, respectively.

The loss of a limb, regardless of the cause, has a significant negative impact on the individual. Prostheses are devices designed to mitigate this loss and have existed since the ancient Egyptian era. Today, the technology of prostheses has evolved considerably; in the case of upper extremity devices, robotic multi-dexterous hands, such as Otto Bock’s Michelangelo hand, Touch Bionics’ i-Limb, and Steeper Group’s Bebionic3 (Connolly, 2008; Medynski and Rattray, 2011; Belter et al., 2013), have been commercially available in the last decade.

However, despite recent technological advances, the overall rate of prostheses use in upper extremity amputees remain low (Biddiss and Chau, 2007) as the state-of-the-art is still far from effectively emulating the human hand and arm (Peerdeman et al., 2011). One of the problems is that, the increased complexity has introduced the new challenge of effectively controlling these devices (Østlie et al., 2012; Yang et al., 2014).

A large component of the difficulty of controlling these devices is due to their unreliability; misclassification of the user’s intentions frequently leads to unplanned movements (Biddiss et al., 2007). Although the conventional myoelectric control strategy involving two sEMG electrodes are sufficient for traditional myoelectric grippers involving only two configurations, opened and closed, the control of a more advanced terminal device requires a series of muscle cocontraction signaling, similar to Morse codes (Yang et al., 2014), making the user experience unintuitive and leading to human errors. One way the community has attempted to address this unintuitive control strategy is by including multiple sEMG electrodes to detect more subtle muscle activation profiles for various grips (Daley et al., 2010; Yang et al., 2014; Naik et al., 2015). Another approach was to modify the configuration of the sEMG electrodes placement (Fang and Liu, 2014). Meanwhile, others have focused more on the pattern recognition algorithm and a self-correcting system to sources of error in classification, such as inertia and force variation (Al-Timemy et al., 2013; Amsuss et al., 2014). However, one of the known limitations to classification accuracy and robustness is due to the sensors themselves. sEMG is prone to signal inconsistency due to interference from ambient noise, such as transmission from fluorescent lighting and televisions, changes in electrochemical signals due to sweat or humidity, electrode shifts as a result of limb movement, and signal cross-talking between adjacent muscles, which may make them unsuitable for prolonged use (Cram and Kasman, 1998; Oskoei et al., 2007; Castellini et al., 2014). Other recognized challenges include the adverse effects of limb position, weight, inertia, and force variation differences during the training on the pattern recognition performance (Cipriani, et al., 2011; Scheme, et al., 2010).

Other approaches, such as targeted muscle reinnervation (TMR) (Kuiken et al., 2009), electroneurography (ENG) (Cloutier and Yang, 2013), intracortical neural interfaces (Fang et al., 2015), and electrocorticography (ECoG) (Pistohl et al., 2012), all of which allow a more direct transmission of neural signals via surgical implants, have been explored. However, due to their invasive natures and costs, alternative non-invasive approaches have been sought. Examples of less invasive methods that have been explored include sonomyography, mechanomyography, electroencephalography, near infrared spectroscopy, magnetoencephalographic, and functional magnetic resonance imaging (Silva et al., 2005; Fang et al., 2015). Although all techniques have their own benefits and limitations, the main focus of this study was to investigate the use of one particular method, termed force myography (FMG) (Wininger et al., 2008).

Force myography, which is also referred to as residual kinetic imaging (RKI) (Phillips and Craelius, 2005) or muscle pressure mapping (MPM) (Radmand et al., 2014), is a technique involving the use of force sensitive resistors (FSRs) on the surface of the limb to detect the volumetric changes in the underlying musculotendinous complex. In a recent study (Ravindra and Castellini, 2014), researchers investigated the pros and cons of three types of non-invasive sensors, including sEMG, ultrasound, and FMG. In the scope of Ravindra and Castellini’s work, they concluded that FMG is the most promising of the three, as it has the potential to provide the highest accuracy in prediction, stability over time, wearability, and affordability of cost. In a different study, Li et al. (2012) investigated the use of FMG for classification and concluded that the use of FMG to decipher the user’s control intention was feasible. However, to the best of our knowledge, there have only been a small number of studies conducted on end-user subjects with transradial amputations.

This study compares the classification accuracy in the sound and residual limbs of four transradially amputated subjects and investigates whether the use of FMG is feasible. In addition, we demonstrated the control of a stand-alone commercially available prosthesis, Bebionic3, in real-time using the FMG technique (for demonstration video – see Supplementary Material).

Materials and Methods

An experiment involving transradially amputated subjects in order to determine feasibility of the use of FMG to classify grip patterns was conducted. Forearm muscular deformation profiles were collected in both residual and sound limbs to compare classification accuracies for various grips.

Hardware

To extract FMG signals, an FSR strap prototype (Xiao and Menon, 2014) developed by the MENRVA Research Group at Simon Fraser University was used and is shown in Figure Figure1.1. The strap is 28.0 cm long and consists of eight embedded FSRs (FSR 402 from Interlink Electronics), which were evenly spaced on the strap’s inner surface. The strap itself is made of flexible chloroprene elastomeric (FloTex) foam with an adjustable Velcro to allow a customized fit for various forearm circumferences.