Passive exoskeletons that assist with human locomotion are often lightweight and compact, but are unable to provide net mechanical power to the exoskeletal wearer. In contrast, powered exoskeletons often provide biologically appropriate levels of mechanical power, but the size and mass of their actuator/power source designs often lead to heavy and unwieldy devices. In this study, we extend the design and evaluation of a lightweight and powerful autonomous exoskeleton evaluated for loaded walking in (J Neuroeng Rehab 11:80, 2014) to the case of unloaded walking conditions.
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 ankle's 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.
Many soldiers are expected to carry heavy loads over extended distances, often resulting in physical and mental fatigue. In this study, the design and testing of an autonomous leg exoskeleton is presented. The aim of the device is to reduce the energetic cost of loaded walking. In addition, we present the Augmentation Factor, a general framework of exoskeletal performance that unifies our results with the varying abilities of previously developed exoskeletons.
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.
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.
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.
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.
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.
In order to provide natural, biomimetic control to recently developed powered ankle prostheses, we must characterize the impedance of the ankle during ambulation tasks. To this end, a platform robot was developed that can apply an angular perturbation to the ankle during ambulation and simultaneously acquire ground reaction force data. In this study, we detail the design of the platform robot and characterize the impedance of the ankle during quiet standing. Subjects were perturbed by a 3° dorsiflexive ramp perturbation with a length of 150 ms. The impedance was defined parametrically, using a second order model to map joint angle to the torque response. The torque was determined using the inverted pendulum assumption, and impedance was identified by the least squares best estimate, yielding an average damping coefficient of 0.03 ± 0.01 Nms/° and an average stiffness coefficient of 3.1 ± 1.2 Nm/°. The estimates obtained by the proposed platform robot compare favorably to those published in the literature. Future work will investigate the impedance of the ankle during ambulation for powered prosthesis controller development.
In control subjects, trips during the early and late swing phase of walking elicit elevating and lowering strategies, respectively. However, the transition between these recovery strategies during mid-swing is unclear. A better understanding of this transition would provide insight into what factors cause individuals to choose one strategy over another. Three control subjects walked on a treadmill while attached to a custom-made tripping device. Perturbations of various lengths (ranging from 50 ms to 350 ms) were applied throughout the swing phase of gait. The results suggest that as perturbation length increased, the transition from elevating to lowering strategies occurred at earlier perturbation onsets. The transition period varied linearly with perturbation length. Perturbation lengths of 150 ms to 250 ms more closely replicated strategy selection in trips induced by real obstacles. Perturbations that are longer in duration force the transition from an elevating to a lowering strategy to occur at an earlier percentage of swing. These results show that perturbation length affects recovery strategy selection in response to trips.
The lack of proprioceptive feedback is a serious deficiency of current prosthetic control systems. The Osseo-Magnetic Link (OML) is a novel humeral or wrist rotation control system that could preserve proprioception. It utilizes a magnet implanted within the residual bone and sensors mounted in the prosthetic socket to detect magnetic field vectors and determine the bones orientation. This allows the use of volitional bone rotation to control a prosthetic rotator. We evaluated the performance of the OML using a physical model of a transhumeral residual limb. A small Neodymium-Iron-Boron magnet was placed in a model humerus, inside a model upper arm. Four three-axis Hall-effect sensors were mounted on a ring 3 cm distal to the magnet. An optimization algorithm based on Newtons method determined the position and orientation of the magnet within the model humerus under various conditions, including bone translations, interference, and magnet misalignment. The orientation of the model humerus was determined within 3° for rotations centered in the arm; an additional 6° error was found for translations 20 mm from center. Adjustments in sensor placement may reduce these errors. The results demonstrate that the OML is a feasible solution for providing prosthesis rotation control while preserving rotational proprioception.
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.
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.
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