Ann M. Simon

Robotic leg control with EMG decoding in an amputee with nerve transfers

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.

Target Achievement Control Test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses

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.

A Decision-Based Velocity Ramp for Minimizing the Effect of Misclassifications During Real-Time Pattern Recognition 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. Nonamputee 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-in cubes (p <; 0.05) in 3 min with the velocity ramp than without it (76% more blocks for nonamputees; 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 postprocessing step, it has the potential to be used with a variety of classifiers for many applications.

Real-Time Myoelectric Control of Knee and Ankle Motions for Transfemoral Amputees

33.3% of candesartan patients received 76% or more of the target dose vs 78.0% of losartan patients. For the model using 150 mg of losartan as the target dose, 33.3% of candesartan patients received 25% or more of the target dose vs 0.2% of losartan patients. The actual mean (SD) dose of candesartan was 18 (11) mg (56% [36%] of the target dose of 32 mg) and of losartan, 53 (26) mg (106% [52%] of the target dose of 50 mg and 35% [17%] of the target dose of 150 mg). Candesartan was associated with less mortality than losartan in all models, with adjustment for dose with a target of 50 mg or 150 mg, and in multivariate models with and without propensity scores. There was no interaction with dose, regardless of whether the target losartan dose was 50 mg or 150 mg. This was a retrospective analysis and not a trial, but we agree that patients were likely titrated toward 50 mg prior to the HEAAL study and 150 mg after, if it was tolerated. Our findings should be confirmed in other studies, but the suggestion that candesartan is associated with lower mortality than losartan in HF remains.

Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes

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.

Patient training for functional use of pattern recognition-controlled prostheses.

ABSTRACT 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—a self-initiated, simple method of recalibrating a pattern recognition–controlled prosthesis. Prosthesis-guided training 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.

A Training Method for Locomotion Mode Prediction Using Powered Lower Limb Prostheses

Recently developed lower-limb prostheses are capable of actuating the knee and ankle joints, allowing amputees to perform advanced locomotion modes such as step-over-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.

Sense of Effort Determines Lower Limb Force Production During Dynamic Movement in Individuals with Poststroke Hemiparesis

Objective. This study’s 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. Methods. Ten stroke participants performed isometric and isotonic lower limb extensions on an exercise machine. Results. When participants attempted to produce equal bilateral isometric forces, there was a significant difference in absolute force between limbs (ANOVA, P < .0001) but no significant difference when force was normalized to each limb’s maximum voluntary contraction (MVC) force (P = .5129). During bilateral isotonic contractions, participants produced less absolute force in their paretic limb (P = .0005) and less relative force in their paretic limb (normalized to MVC force) when participants were given no instructions on how to perform the extension (P = .0002). When participants were instructed to produce equal forces, there was no significant difference between relative forces in the 2 limbs (P = .2111). Conclusions. For both isometric and isotonic conditions hemiparetic participants relied primarily on sense of effort, rather than proprioceptive feedback, for gauging lower limb force production. This outcome indicates that sense of effort is the major factor determining force production during movements. Lower limb rehabilitation therapies should not only train strength in the paretic limb but should also train patients to recalibrate force-scaling abilities to improve function.

Pattern recognition control outperforms conventional myoelectric control in upper limb patients with targeted muscle reinnervation

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.

Non-weight-bearing neural control of a powered transfemoral prosthesis

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.