Elliott J. Rouse

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.

Estimation of Human Ankle Impedance During the Stance Phase of Walking

Human joint impedance is the dynamic relationship between the differential change in the position of a perturbed joint and the corresponding response torque; it is a fundamental property that governs how humans interact with their environments. It is critical to characterize ankle impedance during the stance phase of walking to elucidate how ankle impedance is regulated during locomotion, as well as provide the foundation for future development of natural, biomimetic powered prostheses and their control systems. In this study, ankle impedance was estimated using a model consisting of stiffness, damping and inertia. Ankle torque was well described by the model, accounting for 98 ±1.2% of the variance. When averaged across subjects, the stiffness component of impedance was found to increase linearly from 1.5 to 6.5 Nm/rad/kg between 20% and 70% of stance phase. The damping component was found to be statistically greater than zero only for the estimate at 70% of stance phase, with a value of 0.03 Nms/rad/kg. The slope of the ankles torque-angle curve-known as the quasi-stiffness-was not statistically different from the ankle stiffness values, and showed remarkable similarity. Finally, using the estimated impedance, the specifications for a biomimetic powered ankle prosthesis were introduced that would accurately emulate human ankle impedance during locomotion.

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.

The Difference Between Stiffness and Quasi-Stiffness in the Context of Biomechanical Modeling

The ankle contributes the majority of mechanical power during walking and is a frequently studied joint in biomechanics. Specifically, researchers have extensively investigated the torque-angle relationship for the ankle during dynamic tasks, such as walking and running. The slope of this relationship has been termed the “quasi-stiffness.” However, over time, researchers have begun to interchange the concepts of quasi-stiffness and stiffness. This is an especially important distinction as researchers currently begin to investigate the appropriate control systems for recently developed powered prosthetic legs. The quasi-stiffness and stiffness are distinct concepts in the context of powered joints, and are equivalent in the context of passive joints. The purpose of this paper is to demonstrate the difference between the stiffness and quasi-stiffness using a simple impedance-controlled inverted pendulum model and a more sophisticated biped walking model, each with the ability to modify the trajectory of an impedance controllers equilibrium angle position. In both cases, stiffness values are specified by the controller and the quasi-stiffness are shown during a single step. Both models have widely varying quasi-stiffness but each have a single stiffness value. Therefore, from this simple modeling approach, the differences and similarities between these two concepts are elucidated.

Clutchable series-elastic actuator: Implications for prosthetic knee design

Currently, the mobility of above-knee amputees is limited by the lack of available prostheses that can efficiently replicate biologically accurate movements. In this study, a powered knee prosthesis was designed utilizing a novel mechanism, known as a clutchable series-elastic actuator (CSEA).The CSEA includes a low-power clutch in parallel with an electric motor within a traditional series-elastic actuator. The stiffness of the series elasticity was tuned to match the elastically conservative region of the knee’s torque-angle relationship during the stance phase of locomotion. During this region, the clutch was used to efficiently store energy in the series elasticity. The fully autonomous knee prosthesis design utilized a brushless electric motor, ballscrew transmission and cable drive, as well as commercial electrical components. The knee was lighter than the eighth percentile and shorter than the first percentile male shank segment. The CSEA Knee was tested in a unilateral above-knee amputee walking at 1.3 m/s. During walking, the CSEA Knee provided biomechanically accurate torque-angle behavior, agreeing within 17% of the net work and 27% of the stance flexion angle produced by the biological knee. In addition, the process of locomotion reduced the net electrical energy consumption of the CSEA Knee. The knee’s motor generated 1.8 J/stride, and the net energy consumption was 3.6 J/stride, an order of magnitude less energy than previously published powered knee prostheses.

Clutchable series-elastic actuator: Design of a robotic knee prosthesis for minimum energy consumption

The cyclic and often linear torque-angle relationship of locomotion presents the opportunity to innovate on the design of traditional series-elastic actuators (SEAs). In this paper, a novel modification to the SEA architecture was proposed by adding a clutch in parallel with the motor within the SEA - denoted as a CSEA. This addition permits bimodal dynamics where the system is characterized by an SEA when the clutch is disengaged and a passive spring when the clutch is engaged. The purpose of the parallel clutch was to provide the ability to store energy in a tuned series spring, while requiring only reactionary torque from the clutch. Thus, when the clutch is engaged, a tuned elastic relationship can be achieved with minimal electrical energy consumption. The state-based model of the CSEA is introduced and the implementation of the CSEA mechanism in a powered knee prosthesis is detailed. The series elasticity was optimized to fit the spring-like torque-angle relationship of early stance phase knee flexion and extension during level ground walking. In simulation, the CSEA knee required 70% less electrical energy than a traditional SEA. Future work will focus on the mechanical implementation of the CSEA knee and an empirical demonstration of reduced electrical energy consumption during walking.

Evidence for a Time-Invariant Phase Variable in Human Ankle Control

Human locomotion is a rhythmic task in which patterns of muscle activity are modulated by state-dependent feedback to accommodate perturbations. Two popular theories have been proposed for the underlying embodiment of phase in the human pattern generator: a time-dependent internal representation or a time-invariant feedback representation (i.e., reflex mechanisms). In either case the neuromuscular system must update or represent the phase of locomotor patterns based on the system state, which can include measurements of hundreds of variables. However, a much simpler representation of phase has emerged in recent designs for legged robots, which control joint patterns as functions of a single monotonic mechanical variable, termed a phase variable. We propose that human joint patterns may similarly depend on a physical phase variable, specifically the heel-to-toe movement of the Center of Pressure under the foot. We found that when the ankle is unexpectedly rotated to a position it would have encountered later in the step, the Center of Pressure also shifts forward to the corresponding later position, and the remaining portion of the gait pattern ensues. This phase shift suggests that the progression of the stance ankle is controlled by a biomechanical phase variable, motivating future investigations of phase variables in human locomotor control.

Development of a Mechatronic Platform and Validation of Methods for Estimating Ankle Stiffness During the Stance Phase of Walking

The mechanical properties of human joints (i.e., impedance) are constantly modulated to precisely govern human interaction with the environment. The estimation of these properties requires the displacement of the joint from its intended motion and a subsequent analysis to determine the relationship between the imposed perturbation and the resultant joint torque. There has been much investigation into the estimation of upper-extremity joint impedance during dynamic activities, yet the estimation of ankle impedance during walking has remained a challenge. This estimation is important for understanding how the mechanical properties of the human ankle are modulated during locomotion, and how those properties can be replicated in artificial prostheses designed to restore natural movement control. Here, we introduce a mechatronic platform designed to address the challenge of estimating the stiffness component of ankle impedance during walking, where stiffness denotes the static component of impedance. The system consists of a single degree of freedom mechatronic platform that is capable of perturbing the ankle during the stance phase of walking and measuring the response torque. Additionally, we estimate the platforms intrinsic inertial impedance using parallel linear filters and present a set of methods for estimating the impedance of the ankle from walking data. The methods were validated by comparing the experimentally determined estimates for the stiffness of a prosthetic foot to those measured from an independent testing machine. The parallel filters accurately estimated the mechatronic platforms inertial impedance, accounting for 96% of the variance, when averaged across channels and trials. Furthermore, our measurement system was found to yield reliable estimates of stiffness, which had an average error of only 5.4% (standard deviation: 0.7%) when measured at three time points within the stance phase of locomotion, and compared to the independently determined stiffness values of the prosthetic foot. The mechatronic system and methods proposed in this study are capable of accurately estimating ankle stiffness during the foot-flat region of stance phase. Future work will focus on the implementation of this validated system in estimating human ankle impedance during the stance phase of walking.

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.

Summary of Human Ankle Mechanical Impedance During Walking

The human ankle joint plays a critical role during walking and understanding the biomechanical factors that govern ankle behavior and provides fundamental insight into normal and pathologically altered gait. Previous researchers have comprehensively studied ankle joint kinetics and kinematics during many biomechanical tasks, including locomotion; however, only recently have researchers been able to quantify how the mechanical impedance of the ankle varies during walking. The mechanical impedance describes the dynamic relationship between the joint position and the joint torque during perturbation, and is often represented in terms of stiffness, damping, and inertia. The purpose of this short communication is to unify the results of the first two studies measuring ankle mechanical impedance in the sagittal plane during walking, where each study investigated differing regions of the gait cycle. Rouse et al. measured ankle impedance from late loading response to terminal stance, where Lee et al. quantified ankle impedance from pre-swing to early loading response. While stiffness component of impedance increases significantly as the stance phase of walking progressed, the change in damping during the gait cycle is much less than the changes observed in stiffness. In addition, both stiffness and damping remained low during the swing phase of walking. Future work will focus on quantifying impedance during the “push off” region of stance phase, as well as measurement of these properties in the coronal plane.The human ankle joint plays a critical role during walking and understanding the biomechanical factors that govern ankle behavior and provides fundamental insight into normal and pathologically altered gait. Previous researchers have comprehensively studied ankle joint kinetics and kinematics during many biomechanical tasks, including locomotion; however, only recently have researchers been able to quantify how the mechanical impedance of the ankle varies during walking. The mechanical impedance describes the dynamic relationship between the joint position and the joint torque during perturbation, and is often represented in terms of stiffness, damping, and inertia. The purpose of this short communication is to unify the results of the first two studies measuring ankle mechanical impedance in the sagittal plane during walking, where each study investigated differing regions of the gait cycle. Rouse et al. measured ankle impedance from late loading response to terminal stance, where Lee et al. quantified ankle impedance from pre-swing to early loading response. While stiffness component of impedance increases significantly as the stance phase of walking progressed, the change in damping during the gait cycle is much less than the changes observed in stiffness. In addition, both stiffness and damping remained low during the swing phase of walking. Future work will focus on quantifying impedance during the “push off” region of stance phase, as well as measurement of these properties in the coronal plane.