Evaluation of Linear Regression Simultaneous Myoelectric Control Using Intramuscular EMG.

GOAL: The objective of this study was to evaluate the ability of linear regression models to decode patterns of muscle coactivation from intramuscular electromyogram (EMG) and provide simultaneous myoelectric control of a virtual 3-DOF wrist/hand system. Performance was compared to the simultaneous control of conventional myoelectric prosthesis methods using intramuscular EMG (parallel dual-site control)-an approach that requires users to independently modulate individual muscles in the residual limb, which can be challenging for amputees. METHODS: Linear regression control was evaluated in eight able-bodied subjects during a virtual Fitts' law task and was compared to performance of eight subjects using parallel dual-site control. An offline analysis also evaluated how different types of training data affected prediction accuracy of linear regression control. RESULTS: The two control systems demonstrated similar overall performance; however, the linear regression method demonstrated improved performance for targets requiring use of all three DOFs, whereas parallel dual-site control demonstrated improved performance for targets that required use of only one DOF. Subjects using linear regression control could more easily activate multiple DOFs simultaneously, but often experienced unintended movements when trying to isolate individual DOFs. Offline analyses also suggested that the method used to train linear regression systems may influence controllability. CONCLUSION AND SIGNIFICANCE: Linear regression myoelectric control using intramuscular EMG provided an alternative to parallel dual-site control for 3-DOF simultaneous control at the wrist and hand. The two methods demonstrated different strengths in controllability, highlighting the tradeoff between providing simultaneous control and the ability to isolate individual DOFs when desired.

Myoelectric Control System and Task-Specific Characteristics Affect Voluntary Use of Simultaneous Control.

Clinically available myoelectric control does not enable simultaneous proportional control of prosthetic degrees of freedom. Multiple studies have proposed systems that provide simultaneous control, though few have investigated whether subjects voluntarily use simultaneous control or how they implement it. Additionally, few studies have explicitly evaluated the effect of providing proportional velocity control. The objective of this study was to evaluate factors influencing when and how subjects use simultaneous myoelectric control, including the ability to proportionally control the velocity and the required task precision. Five able-bodied subjects used simultaneous myoelectric control systems with and without proportional velocity control in a virtual Fitts' Law task. Though subjects used simultaneous control to a substantial degree when proportional velocity control was present, they used very little simultaneous control when using constant-velocity control. Furthermore, use of simultaneous control varied significantly with target distance and width, reflecting a strategy of using simultaneous control for gross cursor positioning and sequential control for fine corrective movements. These results provide insight into how users take advantage of simultaneous control and highlight the need for real-time evaluation of simultaneous control algorithms, as the potential benefit of providing simultaneous control may be affected by other characteristics of the myoelectric control system.

Use of probabilistic weights to enhance linear regression myoelectric control.

OBJECTIVE: Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. APPROACH: Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. MAIN RESULTS: Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. SIGNIFICANCE: Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

Targeted muscle reinnervation and advanced prosthetic arms.

Targeted muscle reinnervation (TMR) is a surgical procedure used to improve the control of upper limb prostheses. Residual nerves from the amputated limb are transferred to reinnervate new muscle targets that have otherwise lost their function. These reinnervated muscles then serve as biological amplifiers of the amputated nerve motor signals, allowing for more intuitive control of advanced prosthetic arms. Here the authors provide a review of surgical techniques for TMR in patients with either transhumeral or shoulder disarticulation amputations. They also discuss how TMR may act synergistically with recent advances in prosthetic arm technologies to improve prosthesis controllability. Discussion of TMR and prosthesis control is presented in the context of a 41-year-old man with a left-side shoulder disarticulation and a right-side transhumeral amputation. This patient underwent bilateral TMR surgery and was fit with advanced pattern-recognition myoelectric prostheses.

Nonlinear mappings between discrete and simultaneous motions to decrease training burden of simultaneous pattern recognition myoelectric control.

Real-time simultaneous pattern recognition (PR) control of multiple degrees of freedom (DOF) has been demonstrated using a set of parallel linear discriminant analysis (LDA) classifiers trained with both discrete (1-DOF) and simultaneous (2-DOF) motion data. However, this training method presents a clinical challenge, requiring large amounts of data necessary to re-train the system. This study presents a parallel classifier training method that aims to reduce the training burden. Artificial neural networks (ANNs) were used to determine a nonlinear mapping between surface EMG features of 2-DOF motions and their 1-DOF motion components. The mapping was then used to transform experimentally collected features of 1-DOF motions into simulated features of 2-DOF motions. A set of parallel LDA classifiers were trained using the novel training method and two previously reported training methods. The training methods evaluated were (1) using experimentally collected 1-DOF data and ANN-simulated 2-DOF data, (2) using only experimentally collected 1-DOF data and (3) using experimentally collected 1- and 2-DOF data. Using the novel training method resulted in significantly lower classification error overall (p<;0.01) and in predicting 2-DOF motions (p<;0.01) compared to training with experimental 1-DOF data only. These findings demonstrate that using a set of ANNs to predict 2-DOF data from 1-DOF data can improve system performance when only discrete training data are available, thus reducing the training burden of simultaneous PR control.

Real-time simultaneous myoelectric control by transradial amputees using linear and probability-weighted regression.

Regression-based prosthesis control using surface electromyography (EMG) has demonstrated real-time simultaneous control of multiple degrees of freedom (DOFs) in transradial amputees. However, these systems have been limited to control of wrist DOFs. Use of intramuscular EMG has shown promise for both wrist and hand control in able-bodied subjects, but to date has not been evaluated in amputee subjects. The objective of this study was to evaluate two regression-based simultaneous control methods using intramuscular EMG in transradial amputees and compare their performance to able-bodied subjects. Two transradial amputees and sixteen able-bodied subjects used fine wire EMG recorded from six forearm muscles to control three wrist/hand DOFs: wrist rotation, wrist flexion/extension, and hand open/close. Both linear regression and probability-weighted regression systems were evaluated in a virtual Fitts' Law test. Though both amputee subjects initially produced worse performance metrics than the able-bodied subjects, the amputee subject who completed multiple experimental blocks of the Fitts' law task demonstrated substantial learning. This subject's performance was within the range of able-bodied subjects by the end of the experiment. Both amputee subjects also showed improved performance when using probability-weighted regression for targets requiring use of only one DOF, and mirrored statistically significant differences observed with able-bodied subjects. These results indicate that amputee subjects may require more learning to achieve similar performance metrics as able-bodied subjects. These results also demonstrate that comparative findings between linear and probability-weighted regression with able-bodied subjects reflect performance differences when used by the amputee population.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG.

OBJECTIVE: Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. APPROACH: We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. MAIN RESULTS: Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts' Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts' Law tasks with high levels of path efficiency. SIGNIFICANCE: These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control.

Real-time and offline performance of pattern recognition myoelectric control using a generic electrode grid with targeted muscle reinnervation patients.

Targeted muscle reinnervation (TMR) is a surgical technique that creates myoelectric prosthesis control sites for high-level amputees. The electromyographic (EMG) signal patterns provided by the reinnervated muscles are well-suited for pattern recognition control. Pattern recognition allows for control of a greater number of degrees of freedom (DOF) than the conventional, EMG amplitude-based approach. Previous pattern recognition studies have shown benefit in placing electrodes directly over the reinnervated muscles. Localizing the optimal TMR locations is inconvenient and time consuming. In this contribution, we demonstrate that a clinically practical grid arrangement of electrodes yields real-time control performance that is equivalent to, or better than, the site-specific electrode placement for simultaneous control of multiple DOFs using pattern recognition. Additional findings indicate that grid-like electrode arrangement yields significantly lower classification errors for classifiers with a large number of movement classes ( > 9). These findings suggest that a grid electrode arrangement can be effectively used to control a multi-DOF upper limb prosthesis while reducing the time and effort associated with fitting the prosthesis due to clinical localization of control sites on amputee patients.

A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements.

Myoelectric control has been used for decades to control powered upper limb prostheses. Conventional, amplitude-based control has been employed to control a single prosthesis degree of freedom (DOF) such as closing and opening of the hand. Within the last decade, new and advanced arm and hand prostheses have been constructed that are capable of actuating numerous DOFs. Pattern recognition control has been proposed to control a greater number of DOFs than conventional control, but has traditionally been limited to sequentially controlling DOFs one at a time. However, able-bodied individuals use multiple DOFs simultaneously, and it may be beneficial to provide amputees the ability to perform simultaneous movements. In this study, four amputees who had undergone targeted motor reinnervation (TMR) surgery with previous training using myoelectric prostheses were configured to use three control strategies: 1) conventional amplitude-based myoelectric control, 2) sequential (one-DOF) pattern recognition control, 3) simultaneous pattern recognition control. Simultaneous pattern recognition was enabled by having amputees train each simultaneous movement as a separate motion class. For tasks that required control over just one DOF, sequential pattern recognition based control performed the best with the lowest average completion times, completion rates and length error. For tasks that required control over 2 DOFs, the simultaneous pattern recognition controller performed the best with the lowest average completion times, completion rates and length error compared to the other control strategies. In the two strategies in which users could employ simultaneous movements (conventional and simultaneous pattern recognition), amputees chose to use simultaneous movements 78% of the time with simultaneous pattern recognition and 64% of the time with conventional control for tasks that required two DOF motions to reach the target. These results suggest that when amputees are given the ability to control multiple DOFs simultaneously, they choose to perform tasks that utilize multiple DOFs with simultaneous movements. Additionally, they were able to perform these tasks with higher performance (faster speed, lower length error and higher completion rates) without losing substantial performance in 1 DOF tasks.

Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification.

The simultaneous control of multiple degrees of freedom (DOFs) is important for the intuitive, life-like control of artificial limbs. The objective of this study was to determine whether the use of intramuscular electromyogram (EMG) improved pattern classification of simultaneous wrist/hand movements compared to surface EMG. Two pattern classification methods were used in this analysis, and were trained to predict 1-DOF and 2-DOF movements involving wrist rotation, wrist flexion/extension, and hand open/close. The classification methods used were (1) a single pattern classifier discriminating between 1-DOF and 2-DOF motion classes, and (2) a parallel set of three classifiers to predict the activity of each of the 3 DOFs. We demonstrate that in this combined wrist/hand classification task, the use of intramuscular EMG significantly decreases classification error compared to surface EMG for the parallel configuration (p<0.01), but not for the single classifier. We also show that the use of intramuscular EMG mitigates the increase in errors produced when the parallel classifier method is trained without 2-DOF motion class data.

Classification of simultaneous movements using surface EMG pattern recognition.

Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p < 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.

Sparse optimal motor estimation (SOME) for extracting commands for prosthetic limbs.

It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.

Estimation of excitatory drive from sparse motoneuron sampling.

It is possible to replace amputated limbs with mechatronic prostheses, but their operation requires the user's intentions to be detected and converted into control signals sent to the actuators. Fortunately, the motoneurons (MNs) that controlled the amputated muscles remain intact and capable of generating electrical signals, but these signals are difficult to record. Even the latest microelectrode array technologies and targeted motor reinnervation (TMR) can provide only sparse sampling of the hundreds of motor units that comprise the motor pool for each muscle. Simple rectification and integration of such records is likely to produce noisy and delayed estimates of the actual intentions of the user. We have developed a novel algorithm for optimal estimation of motor pool excitation based on the recruitment and firing rates of a small number (2-10) of discriminated motor units. We first derived the motor estimation algorithm from normal patterns of modulated MN activity based on a previously published model of individual MN recruitment and asynchronous frequency modulation. The algorithm was then validated on a target motor reinnervation subject using intramuscular fine-wire recordings to obtain single motor units.

Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.

Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.

Investigations into pulsed high-intensity focused ultrasound-enhanced delivery: preliminary evidence for a novel mechanism.

Pulsed high-intensity focused ultrasound (HIFU) exposures without ultrasound contrast agents have been used for noninvasively enhancing the delivery of various agents to improve their therapeutic efficacy in a variety of tissue models in a nondestructive manner. Despite the versatility of these exposures, little is known about the mechanisms by which their effects are produced. In this study, pulsed-HIFU exposures were given in the calf muscle of mice, followed by the administration of a variety of fluorophores, both soluble and particulate, by local or systemic injection. In vivo imaging (whole animal and microscopic) was used to quantify observations of increased extravasation and interstitial transport of the fluorophores as a result of the exposures. Histological analysis indicated that the exposures caused some structural alterations such as enlarged gaps between muscle fiber bundles. These effects were consistent with increasing the permeability of the tissues; however, they were found to be transient and reversed themselves gradually within 72 h. Simulations of radiation force-induced displacements and the resulting local shear strain they produced were carried out to potentially explain the manner by which these effects occurred. A better understanding of the mechanisms involved with pulsed HIFU exposures for noninvasively enhancing delivery will facilitate the process for optimizing their use.