Electromyography (EMG)-based interfaces are trending toward continuous, simultaneous control with multiple degrees of freedom. Emerging methods range from data-driven approaches to biomechanical model-based methods. However, there has been no direct comparison between these two types of continuous EMG-based interfaces. The aim of this study was to compare a musculoskeletal model (MM) with two data-driven approaches, linear regression (LR) and artificial neural network (ANN), for predicting continuous wrist and hand motions for EMG-based interfaces. Six able-bodied subjects and one transradial amputee subject performed (missing) metacarpophalangeal (MCP) and wrist flexion/extension, simultaneously or independently, while four EMG signals were recorded from forearm muscles. To add variation to the EMG signals, the subjects repeated the MCP and wrist motions at various upper extremity postures. For each subject, the EMG signals collected from the neutral posture were used to build the EMG interfaces; the EMG signals collected from all postures were used to evaluate the interfaces. The performance of the interface was quantified by Pearson’s correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and estimated joint angles. The results demonstrated that the MM predicted movements more accurately, with higher r values and lower NRMSE, than either LR or ANN. Similar results were observed in the transradial amputee. Additionally, the variation in r across postures, an indicator of reliability against posture changes, was significantly lower (better) for the MM than for either LR or ANN. Our findings suggest that incorporating musculoskeletal knowledge into EMG-based human-machine interfaces could improve the estimation of continuous, coordinated motion.

This study aimed to develop a novel electromyography (EMG)-based neural-machine interface (NMI) that is user-generic for continuously predicting coordinated motion between metacarpophalangeal (MCP) and wrist flexion/extension. The NMI requires a minimum calibration procedure that only involves capturing maximal voluntary muscle contraction for the monitored muscles for individual users. At the center of the NMI is a user-generic musculoskeletal model based on the experimental data collected from 6 able-bodied (AB) subjects and 9 different upper limb postures. The generic model was evaluated on-line on both AB subjects and a transradial amputee. The subjects were instructed to perform a virtual hand/wrist posture matching task with different upper limb postures. The on-line performance of the generic model was also compared with that of the musculoskeletal model customized to each individual user (called “specific model”). All subjects accomplished the assigned virtual tasks while using the user-generic NMI, although the AB subjects produced better performance than the amputee subject. Interestingly, compared to the specific model, the generic model produced comparable completion time, a reduced number of overshoots, and improved path efficiency in the virtual hand/wrist posture matching task. The results suggested that it is possible to design an EMG-driven NMI based on a musculoskeletal model that could fit multiple users, including upper limb amputees, for predicting coordinated MCP and wrist motion. The present new method might address the challenges of existing advanced EMG-based NMI that require frequent and lengthy customization and calibration. Our future research will focus on evaluating the developed NMI for powered prosthetic arms.

Simultaneous and proportional control (SPC) of neural-machine interfaces uses magnitudes of smoothed electromyograms (EMG) as control inputs. Though surface EMG (sEMG) electrodes are common for clinical neural-machine interfaces, intramuscular EMG (iEMG) electrodes may be indicated in some circumstances (e.g., for controlling many degrees of freedom). However, differences in signal characteristics between sEMG and iEMG may influence SPC performance. We conducted a pilot study to determine the effect of electrode type (sEMG and iEMG) on real-time task performance with SPC based on a novel 2-degree-of-freedom EMG-driven musculoskeletal model of the wrist and hand. Four able-bodied subjects and one transradial amputee performed a virtual posture matching task with either sEMG or iEMG. There was a trend of better task performance with sEMG than iEMG for both able-bodied and amputee subjects, though the difference was not statistically significant. Thus, while iEMG may permit targeted recording of EMG, its signal characteristics may not be as ideal for SPC as those of sEMG. The tradeoff between recording specificity and signal characteristics is an important consideration for development and clinical implementation of SPC for neural-machine interfaces.

Objective. Evoking haptic sensation on upper limb amputees via peripheral nerve stimulation has been investigated intensively in the past decade, but related studies involving lower limb amputees are limited. This study aimed to evaluate the feasibility of using non-invasive transcutaneous electrical nerve stimulation to evoke haptic sensation along the phantom limb of the amputated foot of transtibial amputees. Approach. A high-density electrode grid (4 × 4) was placed over the skin surface above the distal branching of the sciatic, tibial, and common peroneal nerves. We hypothesized that electrical stimulation delivered to distinct electrode pairs created unique electric fields, which can activate selective sets of sensory axons innervating different skin regions of the foot. Five transtibial amputee subjects (three unilateral and two bilateral) and one able-bodied subject were tested by scanning all possible electrode pair combinations. Main results. All subjects reported various haptic percepts at distinct regions along the foot with each corresponding to specific electrode pairs. These results demonstrated the capability of our non-invasive nerve stimulation method to evoke haptic sensations in the foot of transtibial amputees and the able-bodied subject. Significance. The outcomes contribute important knowledge and evidence regarding missing tactile sensation in the foot of lower limb amputees and might also facilitate future development of strategies to manage phantom pain and enhance embodiment of prosthetic legs in the future.

Recently, we proposed a musculoskeletal model to simultaneously predict motion along metacarpophalangeal (MCP) and wrist flexion/extension degrees-of-freedom (DOFs) from surface electromyography (EMG) signals. Since wrist pronation/supination is also functionally important, we extended the musculoskeletal model to simultaneously estimate wrist pronation/supination in addition to wrist and MCP flexion/extension from surface EMG signals of six corresponding muscles. Kinematic data and surface EMG signals were acquired synchronously from an able-bodied subject. The subject was instructed to perform single-DOF movements at fixed or variable speed and simultaneous 3-DOF movements at variable speed during the experiment. The model included six Hill-type actuators, each with a contractile element and a parallel elastic element. Seven parameters were optimized for each of the six muscles. The average Pearson's correlation coefficient (r) between measured and estimated joint angles across all trials was 0.91, indicating high positive correlation. The results demonstrated that the proposed model could feasibly simultaneously estimate 3-DOF joint angles during either independent-DOF or simultaneous 3-DOF movements from EMG signals. Our results promote the potential of the EMG-driven musculoskeletal model for clinical applications, such as prosthesis control.