Suzanne B. Finucane

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.

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.

Intuitive Control of a Powered Prosthetic Leg During Ambulation: A Randomized Clinical Trial

IMPORTANCE Some patients with lower leg amputations may be candidates for motorized prosthetic limbs. Optimal control of such devices requires accurate classification of the patients ambulation mode (eg, on level ground or ascending stairs) and natural transitions between different ambulation modes. OBJECTIVE To determine the effect of including electromyographic (EMG) data and historical information from prior gait strides in a real-time control system for a powered prosthetic leg capable of level-ground walking, stair ascent and descent, ramp ascent and descent, and natural transitions between these ambulation modes. DESIGN, SETTING, AND PARTICIPANTS Blinded, randomized crossover clinical trial conducted between August 2012 and November 2013 in a research laboratory at the Rehabilitation Institute of Chicago. Participants were 7 patients with unilateral above-knee (n = 6) or knee-disarticulation (n = 1) amputations. All patients were capable of ambulation within their home and community using a passive prosthesis (ie, one that does not provide external power). INTERVENTIONS Electrodes were placed over 9 residual limb muscles and EMG signals were recorded as patients ambulated and completed 20 circuit trials involving level-ground walking, ramp ascent and descent, and stair ascent and descent. Data were acquired simultaneously from 13 mechanical sensors embedded on the prosthesis. Two real-time pattern recognition algorithms, using either (1) mechanical sensor data alone or (2) mechanical sensor data in combination with EMG data and historical information from earlier in the gait cycle, were evaluated. The order in which patients used each configuration was randomized (1:1 blocked randomization) and double-blinded so patients and experimenters did not know which control configuration was being used. MAIN OUTCOMES AND MEASURES The main outcome of the study was classification error for each real-time control system. Classification error is defined as the percentage of steps incorrectly predicted by the control system. RESULTS Including EMG signals and historical information in the real-time control system resulted in significantly lower classification error (mean, 7.9% [95% CI, 6.1%-9.7%]) across a mean of 683 steps (range, 640-756 steps) compared with using mechanical sensor data only (mean, 14.1% [95% CI, 9.3%-18.9%]) across a mean of 692 steps (range, 631-775 steps), with a mean difference between groups of 6.2% (95% CI, 2.7%-9.7%] (P = .01). CONCLUSIONS AND RELEVANCE In this study of 7 patients with lower limb amputations, inclusion of EMG signals and temporal gait information reduced classification error across ambulation modes and during transitions between ambulation modes. These preliminary findings, if confirmed, have the potential to improve the control of powered leg prostheses.

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.

Strategies to reduce the configuration time for a powered knee and ankle prosthesis across multiple ambulation modes

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.

Myoelectric neural interface enables accurate control of a virtual multiple degree-of-freedom foot-ankle prosthesis

Technological advances have enabled clinical use of powered foot-ankle prostheses. Although the fundamental purposes of such devices are to restore natural gait and reduce energy expenditure by amputees during walking, these powered prostheses enable further restoration of ankle function through possible voluntary control of the powered joints. Such control would greatly assist amputees in daily tasks such as reaching, dressing, or simple limb repositioning for comfort. A myoelectric interface between an amputee and the powered foot-ankle prostheses may provide the required control signals for accurate control of multiple degrees of freedom of the ankle joint. Using a pattern recognition classifier we compared the error rates of predicting up to 7 different ankle-joint movements using electromyographic (EMG) signals collected from below-knee, as well as below-knee combined with above-knee muscles of 12 trans-tibial amputee and 5 control subjects. Our findings suggest very accurate (5.3±0.5%SE mean error) real-time control of a 1 degree of freedom (DOF) of ankle joint can be achieved by amputees using EMG from as few as 4 below-knee muscles. Reliable control (9.8±0.7%SE mean error) of 3 DOFs can be achieved using EMG from 8 below-knee and above-knee muscles.

Delaying Ambulation Mode Transition Decisions Improves Accuracy of a Flexible Control System for Powered Knee-Ankle Prosthesis

Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study’s goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis’ mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable intent recognition system for a powered knee-ankle prosthesis.

Improved Weight-Bearing Symmetry for Transfemoral Amputees During Standing Up and Sitting Down With a Powered Knee-Ankle Prosthesis

OBJECTIVE To test a new user-modulated control strategy that enables improved control of a powered knee-ankle prosthesis during sit-to-stand and stand-to-sit movements. DESIGN Within-subject comparison study. SETTING Gait laboratory. PARTICIPANTS Unilateral transfemoral amputees (N=7; 4 men, 3 women) capable of community ambulation. INTERVENTIONS Subjects performed 10 repetitions of sit-to-stand and stand-to-sit with a powered knee-ankle prosthesis and with their prescribed passive prosthesis in a randomized order. With the powered prosthesis, knee and ankle power generation were controlled as a function of weight transferred onto the prosthesis. MAIN OUTCOME MEASURES Vertical ground reaction force limb asymmetry and durations of movement were compared statistically (Wilcoxon signed-rank test, α=.05). RESULTS For sit-to-stand, peak vertical ground reaction forces were significantly less asymmetric using the powered prosthesis (mean, 19.3%±11.8%) than the prescribed prosthesis (57.9%±13.5%; P=.018), where positive asymmetry values represented greater force through the intact limb. For stand-to-sit, peak vertical ground reaction forces were also significantly less asymmetric using the powered prosthesis (28.06%±11.6%) than the prescribed prosthesis (48.2%±16%; P=.028). Duration of movement was not significantly different between devices (sit-to-stand: P=.18; stand-to-sit: P=.063). CONCLUSIONS Allowing transfemoral amputees more control over the timing and rate of knee and ankle power generation enabled users to stand up and sit down with their weight distributed more equally between their lower limbs. Increased weight bearing on the prosthetic limb may make such activities of daily living easier for transfemoral amputees.

Online adaptive neural control of a robotic lower limb prosthesis

OBJECTIVE The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputees neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. APPROACH We present a powered lower limb prosthesis that was configured to acquire the users neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the users intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the users model of neural data during ambulation. MAIN RESULTS Our proposed algorithm accurately and consistently identified the users intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate. SIGNIFICANCE This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the users intent with low error rates.