Appropriately responding to mechanical perturbations during gait is critical to maintain balance and avoid falls. Tripping perturbation onset during swing phase is strongly related to the use of different recovery strategies; however, it is insufficient to fully explain how strategies are chosen. The dynamic interactions between the foot and the obstacle may further explain observed recovery strategies but the relationship between such contextual elements and strategy selection has not been explored. In this study, we investigated whether perturbation onset, duration and side could explain strategy selection for all of swing phase. We hypothesized that perturbations of longer duration would elicit lowering and delayed-lowering strategies earlier in swing phase than shorter perturbations. We developed a custom device to trip subjects multiple times while they walked on a treadmill. Seven young, healthy subjects were tripped on the left or right side at 10% to 80% of swing phase for 150 ms, 250 ms or 350 ms. Strategies were characterized by foot motion post-perturbation and identified by an automated algorithm. A multinomial logistic model was used to investigate the effect of perturbation onset, side, and the interaction between duration and onset on recovery strategy selection. Side perturbed did not affect strategy selection. Perturbation duration interacted with onset, limiting the use of elevating strategies to earlier in swing phase with longer perturbations. The choice between delayed-lowering and lowering strategies was not affected by perturbation duration. Although these variables did not fully explain strategy selection, they improved the prediction of strategy used in response to tripping perturbations throughout swing phase.
Lower limb prostheses that can generate net positive mechanical work may restore more ambulation modes to amputees. However, configuration of these devices imposes an additional burden on clinicians relative to conventional prostheses; devices for transfemoral amputees that require configuration of both a knee and an ankle joint are especially challenging. In this paper, we present an approach to configuring such powered devices. We developed modified intrinsic control strategies--which mimic the behavior of biological joints, depend on instantaneous loads within the prosthesis, or set impedance based on values from previous states, as well as a set of starting configuration parameters. We developed tables that include a list of desired clinical gait kinematics and the parameter modifications necessary to alter them. Our approach was implemented for a powered knee and ankle prosthesis in five ambulation modes (level-ground walking, ramp ascent/descent, and stair ascent/descent). The strategies and set of starting configuration parameters were developed using data from three individuals with unilateral transfemoral amputations who had previous experience using the device; this approach was then tested on three novice unilateral transfemoral amputees. Only 17% of the total number of parameters (i.e., 24 of the 140) had to be independently adjusted for each novice user to achieve all five ambulation modes and the initial accommodation period (i.e., time to configure the device for all modes) was reduced by 56%, to 5 hours or less. This approach and subsequent reduction in configuration time may help translate powered prostheses into a viable clinical option where amputees can more quickly appreciate the benefits such devices can provide.
Recently developed powered lower limb prostheses allow users to more closely mimic the kinematics and kinetics of non-amputee gait. However, configuring such a device, in particular a combined powered knee and ankle, for individuals with a transfemoral amputation is challenging. Previous attempts have relied on empirical tuning of all control parameters. This paper describes modified stance phase control strategies - which mimic the behavior of biological joints or depend on the instantaneous loads within the prosthesis - developed to reduce the number of control parameters that require individual tuning. Three individuals with unilateral transfemoral amputations walked with a powered knee and ankle prosthesis across five ambulation modes (level ground walking, ramp ascent/descent, and stair ascent/descent). Starting with a nominal set of impedance parameters, the modified control strategies were applied and the devices were individually tuned such that all subjects achieved comfortable and safe ambulation. The control strategies drastically reduced the number of independent parameters that needed to be tuned for each subject (i.e., to 21 parameters instead of a possible 140 or approximately 4 parameters per mode) while relative amplitudes and timing of kinematic and kinetic data remained similar to those previously reported and to those of non-amputee subjects. Reducing the time necessary to configure a powered device across multiple ambulation modes may allow users to more quickly realize the benefits such powered devices can provide.
Recently developed lower-limb prostheses are capable of actuating the knee and ankle joints, allowing amputees to perform advanced locomotion modes such as stepover- step stair ascent and walking on sloped surfaces. However, transitions between these locomotion modes and walking are neither automatic nor seamless. This study describes methods for construction and training of a high-level intent recognition system for a lower-limb prosthesis that provides natural transitions between walking, stair ascent, stair descent, ramp ascent, and ramp descent. Using mechanical sensors onboard a powered prosthesis, we collected steady-state and transition data from six transfemoral amputees while the five locomotion modes were performed. An intent recognition system built using only mechanical sensor data was 84.5% accurate using only steady-state training data. Including training data collected while amputees performed seamless transitions between locomotion modes improved the overall accuracy rate to 93.9%. Training using a single analysis window at heel contact and toe off provided higher recognition accuracy than training with multiple analysis windows. This study demonstrates the capability of an intent recognition system to provide automatic, natural, and seamless transitions between five locomotion modes for transfemoral amputees using powered lower limb prostheses.
Pattern recognition myoelectric control shows great promise as an alternative to conventional amplitude based control to control multiple degree of freedom prosthetic limbs. Many studies have reported pattern recognition classification error performances of less than 10% during offline tests; however, it remains unclear how this translates to real-time control performance. In this contribution, we compare the real-time control performances between pattern recognition and direct myoelectric control (a popular form of conventional amplitude control) for participants who had received targeted muscle reinnervation. The real-time performance was evaluated during three tasks; 1) a box and blocks task, 2) a clothespin relocation task, and 3) a block stacking task. Our results found that pattern recognition significantly outperformed direct control for all three performance tasks. Furthermore, it was found that pattern recognition was configured much quicker. The classification error of the pattern recognition systems used by the patients was found to be 16% ±(1.6%) suggesting that systems with this error rate may still provide excellent control. Finally, patients qualitatively preferred using pattern recognition control and reported the resulting control to be smoother and more consistent.
Powered lower limb prostheses, capable of multiple locomotion modes, are being developed for transfemoral amputees. Current devices do not seamlessly transition between modes such as level walking, stairs and slopes. The purpose of this study was to develop an intent recognition system and test its performance across five different modes. A Dynamic Bayesian Network (DBN) was used for classification of neural and mechanical signals while four amputees completed a circuit containing level-walking, ramp ascent, ramp descent, stair ascent and stair descent. Our results indicate that transitional and steady-state stair steps had a high recognition rate (>99%), while ramp steps were significantly more difficult to classify (p<0.01) (13.7% error on transition steps and 1.3% on steady-state steps). With all five modes trained into the same system, the transitional error rate was 11.3%. Transitional error could be reduced by 31% by training the ramp ascent mode as level walking, and 92% by training both ramp ascent and descent as level walking. This is a viable solution when the level-walking mode can accommodate ramp modes which is currently the case with the ramp ascent. The high recognition rates for recognizing stairs shown in this study demonstrates the potential for an intent recognition system using neural information to allow amputees to naturally transition between locomotion modes on powered prostheses.
The clinical application of robotic technology to powered prosthetic knees and ankles is limited by the lack of a robust control strategy. We found that the use of electromyographic (EMG) signals from natively innervated and surgically reinnervated residual thigh muscles in a patient who had undergone knee amputation improved control of a robotic leg prosthesis. EMG signals were decoded with a pattern-recognition algorithm and combined with data from sensors on the prosthesis to interpret the patients intended movements. This provided robust and intuitive control of ambulation--with seamless transitions between walking on level ground, stairs, and ramps--and of the ability to reposition the leg while the patient was seated.
New computerized and powered lower limb prostheses are being developed that enable amputees to perform multiple locomotion modes. However, current lower limb prosthesis controllers are not capable of transitioning these devices automatically and seamlessly between locomotion modes such as level-ground walking, stairs and slopes. The focus of this study was to evaluate different intent recognition interfaces, which if configured properly, may be capable of providing more natural transitions between locomotion modes. Intent recognition can be accomplished using a multitude of different signals from mechanical sensors on the prosthesis. Since these signals are non-stationary over any given stride, and gait is cyclical, time history information may improve locomotion mode recognition. The authors propose a dynamic Bayesian network classification strategy to incorporate prior sensor information over the gait cycle with current sensor information. Six transfemoral amputees performed locomotion circuits comprising level-ground walking and ascending/descending stairs and ramps using a powered knee and ankle prosthesis. Using time history reduced steady-state misclassifications by over half (p < 0.01), when compared to strategies that did not use time history, without reducing intent recognition performance during transitions. These results suggest that including time history information across the gait cycle can enhance locomotion mode intent recognition performance.
Lower limb prostheses have traditionally been mechanically passive devices without electronic control systems. Microprocessor-controlled passive and powered devices have recently received much interest from the clinical and research communities. The control systems for these devices typically use finite-state controllers to interpret data measured from mechanical sensors embedded within the prosthesis. In this paper we investigated a control system that relied on information extracted from myoelectric signals to control a lower limb prosthesis while amputee patients were seated. Sagittal plane motions of the knee and ankle can be accurately (>90%) recognized and controlled in both a virtual environment and on an actuated transfemoral prosthesis using only myoelectric signals measured from nine residual thigh muscles. Patients also demonstrated accurate (~90%) control of both the femoral and tibial rotation degrees of freedom within the virtual environment. A channel subset investigation was completed and the results showed that only five residual thigh muscles are required to achieve accurate control. This research is the first step in our long-term goal of implementing myoelectric control of lower limb prostheses during both weight-bearing and non-weight-bearing activities for individuals with transfemoral amputation.
Despite high classification accuracies (~95%) of myoelectric control systems based on pattern recognition, how well offline measures translate to real-time closed-loop control is unclear. Recently, a real-time virtual test analyzed how well subjects completed arm motions using a multiple-degree of freedom (DOF) classifier. Although this test provided real-time performance metrics, the required task was oversimplified: motion speeds were normalized and unintended movements were ignored. We included these considerations in a new, more challenging virtual test called the Target Achievement Control Test (TAC Test). Five subjects with transradial amputation attempted to move a virtual arm into a target posture using myoelectric pattern recognition, performing the test with various classifier (1- vs 3-DOF) and task complexities (one vs three required motions per posture). We found no significant difference in classification accuracy between the 1- and 3-DOF classifiers (97.2% +/- 2.0% and 94.1% +/- 3.1%, respectively; p = 0.14). Subjects completed 31% fewer trials in significantly more time using the 3-DOF classifier and took 3.6 +/- 0.8 times longer to reach a three-motion posture compared with a one-motion posture. These results highlight the need for closed-loop performance measures and demonstrate that the TAC Test is a useful and more challenging tool to test real-time pattern-recognition performance.
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.
Few studies have focused on proportional control with multi-channel electromyographic (EMG) pattern recognition systems. In a simple proportional control algorithm, movement speed is often calculated by averaging the mean absolute values of all EMG channels. The aim of our study was to compare the performance of two types of pattern recognition control (simple proportional and binary on/off) to direct proportional control. Six EMG channels were collected from non-targeted forearm muscles of four healthy subjects. Subjects were prompted to perform eight medium force isometric repetitions of the following contractions: wrist flexion/extension, wrist pronation/supination, hand open/close, and no movement (rest). Control performances were measured during a one-dimensional position-tracking task using a custom-made graphical user interface. The results show that a simple proportional control algorithm for the pattern recognition system outperformed binary on/off control and was comparable to the performance achieved with direct proportional control.
Movement misclassifications often occur during real-time pattern recognition control. Majority vote and a decision-based velocity ramp are two different post-processing methods that have been suggested to improve real-time control. With majority vote, spurious misclassifications are removed at the expense of an additional controller delay. With a decision-based velocity ramp, the effect of misclassifications is minimized by attenuating movement speed following a change in decision from the classifier. The goal of the study was to determine which, if any, post-processing method improved real-time control above a baseline condition that did not involve post-processing. Five non-amputee subjects controlled a virtual prosthesis in real time using pattern recognition. While performing a challenging target achievement test in a virtual environment, subjects had significantly higher completion rates (p < 0.04) and more direct paths to the target (p < 0.02) while using the velocity ramp than while using majority vote or the control condition. There were no significant differences in completion rate or path efficiency between the majority vote conditions and the control condition (p > 0.6). The benefits of removing misclassifications through majority vote may be offset by the added controller delay. These results highlight the need for real-time performance measures, as methods that have been shown to reduce errors during offline analysis may not improve real-time control.
Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Non-amputee and amputee subjects controlled a prosthesis in real-time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate (p < 0.05) and a more direct path (p < 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1 cubes (p < 0.05) in three minutes with the velocity ramp than without it (76% more blocks for non-amputees; 89% more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a post-processing step, it has the potential to be used with a variety of classifiers for many applications.
The Intracardiac Echocardiography Guided Cardioversion Helps Interventional Procedures study evaluated the concordance of intracardiac echocardiography (ICE) with transesophageal echocardiography (TEE) in patients with atrial fibrillation (AF).
Pattern recognition myoelectric control in combination with targeted muscle reinnervation (TMR) may provide better real-time control of upper limb prostheses. Current pattern recognition algorithms can classify movements with an off-line accuracy of approximately 95%. When amputees use these systems to control prostheses, motion misclassifications may hinder their performance. This study investigated the use of a decision based velocity profile that limited movement speed when there was a change in classifier decision. The goal of this velocity ramp was to improve prosthesis positioning by minimizing the effect of unintended movements. Two patients who had undergone TMR surgery controlled either a virtual or physical prosthesis. They completed a Target Achievement Control Test where they commanded a virtual prosthesis into a target posture. Participants showed improved performance metrics of 34% increase in completion rate and 13% faster overall time with the velocity ramp compared to without the velocity ramp. One participant controlled a physical prosthesis and in three minutes was able to create a tower of 1" cubes seven blocks tall with the velocity ramp compared to a tower of only two blocks tall in the control condition. These results suggest that using a pattern recognition system with a decision based velocity profile may improve user performance.
We tested a novel control strategy for robotic rehabilitation devices used by individuals with post-stroke hemiparesis. Symmetry-based resistance increases resistance when limb forces become more asymmetric during bilateral exercise. The underlying rationales for the control mode are that it will guide patients to increase paretic limb activation while teaching them to accurately gauge paretic limb force production relative to the non-paretic limb. During a one day training session, seven subjects post-stroke performed lower limb extensions in symmetry-based resistance mode on a robotic exercise machine. Subjects improved lower limb symmetry from 28.6%+/-3.9% to 36.2%+/-4.3% while under symmetry-based resistance training (ANOVA, P = 0.03), but did not maintain the improved lower limb symmetry during a constant resistance post-test. Two subjects that showed the large improvements in symmetry during the one day session performed additional days of training. Those results suggest that some patients demonstrate long lasting benefits with symmetry-based resistance training.
This studys purpose was to determine if individuals who have had a stroke primarily use sense of effort to gauge force production during static and dynamic lower limb contractions. If relying on sense of effort while attempting to generate equal limb forces, participants should produce equal percentages of their maximum voluntary strength rather than equal absolute forces in their limbs.
Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the prosthesis itself. The movement of the prosthesis provides the cues of when to perform the muscle contractions. This study compared the training data obtained from SGT and PGT and evaluated user performance after training pattern recognition classifiers with each method. Although the inclusion of transient EMG signal in PGT data led to decreased accuracy of the classifier, subjects completed a performance task faster than when compared to using a classifier built from SGT data. This may indicate that training data collected using PGT that includes both steady state and transient EMG signals generates a classifier that more accurately reflects muscle activity during real-time use of a pattern recognition-controlled myoelectric prosthesis.
Pattern recognition control systems have the potential to provide better, more reliable myoelectric prosthesis control for individuals with an upper-limb amputation. However, proper patient training is essential. We begin user training by teaching the concepts of pattern recognition control and progress to teaching how to control, use, and maintain prostheses with one or many degrees of freedom. Here we describe the training stages, with relevant case studies, and highlight several tools that can be used throughout the training process, including prosthesis-guided training (PGT)-a self-initiated, simple method of recalibrating a pattern recognition-controlled prosthesis. PGT may lengthen functional use times, potentially increasing prosthesis wear time. Using this training approach, we anticipate advancing pattern recognition control from the laboratory to the home environment and finally realizing the full potential of these control systems.
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