Understanding patient preference in prosthetic ankle stiffness.

BACKGROUND: User preference has the potential to facilitate the design, control, and prescription of prostheses, but we do not yet understand which physiological factors drive preference, or if preference is associated with clinical benefits. METHODS: Subjects with unilateral below-knee amputation walked on a custom variable-stiffness prosthetic ankle and manipulated a dial to determine their preferred prosthetic ankle stiffness at three walking speeds. We evaluated anthropomorphic, metabolic, biomechanical, and performance-based descriptors at stiffness levels surrounding each subject's preferred stiffness. RESULTS: Subjects preferred lower stiffness values at their self-selected treadmill walking speed, and elected to walk faster overground with ankle stiffness at or above their preferred stiffness. Preferred stiffness maximized the kinematic symmetry between prosthetic and unaffected joints, but was not significantly correlated with body mass or metabolic rate. CONCLUSION: These results imply that some physiological factors are weighted more heavily when determining preferred stiffness, and that preference may be associated with clinically relevant improvements in gait.

Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors.

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user's steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the 'ground truth' energy cost for each activity. Evaluating Pearson's correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4-5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

Choosing appropriate prosthetic ankle work to reduce the metabolic cost of individuals with transtibial amputation.

Powered ankle prostheses have been designed to reduce the energetic burden that individuals with transtibial amputation experience during ambulation. There is an open question regarding how much power the prosthesis should provide, and whether approximating biological ankle kinetics is optimal to reduce the metabolic cost of users. We tested 10 individuals with transtibial amputation walking on a treadmill wearing the BiOM powered ankle prosthesis programmed with 6 different power settings (0-100%), including a prosthetist-chosen setting, chosen to approximate biological ankle kinetics. We measured subjects' metabolic cost of transport (COT) and the BiOM's net ankle work during each condition. Across participants, power settings greater than 50% resulted in lower COT than 0% or 25%. The relationship between power setting, COT, and net ankle work varied considerably between subjects, possibly due to individual adaptation and exploitation of the BiOM's reflexive controller. For all subjects, the best tested power setting was higher than the prosthetist-chosen setting, resulting in a statistically significant and meaningful difference in COT between the best tested and prosthetist-chosen power settings. The results of this study demonstrate that individuals with transtibial amputation may benefit from prescribed prosthetic ankle push-off work that exceeds biological norms.

Using wearable physiological sensors to predict energy expenditure.

Lower-limb assistive robotic devices are often evaluated by measuring a reduction in the user's energy cost. Using indirect calorimetry to estimate energy cost is poorly suited for real-time estimation and long-term collection. The goal of this study was to use data from wearable sensors to predict energy cost with better temporal resolution and less variability than breath measurements. We collected physiological data (heart rate, electrodermal activity, skin temperature) and mechanical data (EMG, accelerometry) from three healthy subjects walking on a treadmill at various speeds on level ground, inclined, and backwards. Ground truth energy cost was established by averaging steady-state breath measurements. Raw physiological signals correlated well with ground truth energy cost, but raw mechanical signals did not. Correlation of mechanical signals was improved by calculating accelerometer magnitudes and linear envelope EMG signals, and further improved by averaging the signals over several seconds. A multiple linear regression including physiological and mechanical data accurately predicted ground truth energy cost across all subjects and activities tested, with less variability and better temporal resolution than breath measurements. The sensors used in this study were fully portable, and such algorithms could be used to estimate energy cost of users in the real world. This could greatly improve the design, control, and evaluation of lower-limb assistive robotic devices.

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.

Assessing the Relative Contributions of Active Ankle and Knee Assistance to the Walking Mechanics of Transfemoral Amputees Using a Powered Prosthesis.

Powered knee-ankle prostheses are capable of providing net-positive mechanical energy to amputees. Yet, there are limitless ways to deliver this energy throughout the gait cycle. It remains largely unknown how different combinations of active knee and ankle assistance affect the walking mechanics of transfemoral amputees. This study assessed the relative contributions of stance phase knee swing initiation, increasing ankle stiffness and powered plantarflexion as three unilateral transfemoral amputees walked overground at their self-selected walking speed. Five combinations of knee and ankle conditions were evaluated regarding the kinematics and kinetics of the amputated and intact legs using repeated measures analyses of variance. We found eliminating active knee swing initiation or powered plantarflexion was linked to increased compensations of the ipsilateral hip joint during the subsequent swing phase. The elimination of knee swing initiation or powered plantarflexion also led to reduced braking ground reaction forces of the amputated and intact legs, and influenced both sagittal and frontal plane loading of the intact knee joint. Gradually increasing prosthetic ankle stiffness influenced the shape of the prosthetic ankle plantarflexion moment, more closely mirroring the intact ankle moment. Increasing ankle stiffness also corresponded to increased prosthetic ankle power generation (despite a similar maximum stiffness value across conditions) and increased braking ground reaction forces of the amputated leg. These findings further our understanding of how to deliver assistance with powered knee-ankle prostheses and the compensations that occur when specific aspects of assistance are added/removed.

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.

Delaying ambulation mode transitions in a powered knee-ankle prosthesis.

Powered knee and ankle prostheses have the potential to improve the mobility of individuals with a lower limb amputation. As the number of different ambulation modes the prosthesis can be configured for increases, so too does the challenge of how to best transition the prosthesis between these modes. Pattern recognition systems have been suggested as a means to provide seamless and natural transitions, although error rates need to be reduced for these systems to be clinical viable. Delaying mode transitions by a small window may be one way to reduce error rates and improve reliability. The goal of this study was to develop and test a system for powered lower limb prostheses that introduced a delay between mode transitions. Three transfemoral amputees used a knee-ankle prosthesis to stand, walk on level ground, ascend/descend a ramp, and ascend/descend stairs. On Day 1 mode transitions occurred at a gait event (e.g., heel contact), and on Day 2 mode transitions occurred 90 ms following a gait event. A mode-specific pattern recognition system was trained and tested on each day. The 90 ms transition delay did not negatively affect users' performance ambulating with the prosthesis. Offline classification error results showed that the 90 ms delay reduced overall classification errors from 1.30% [0.29%], mean [SD], for the non-delayed system to 0.42% [0.22%] for the delayed system. These results demonstrate that delaying mode transitions by a small window of time can reduce overall errors, which moves these systems one step closer to clinical viability.

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 patient's 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.

Nonlinear mappings between discrete and simultaneous motions to decrease training burden of simultaneous pattern recognition myoelectric control.

Real-time simultaneous pattern recognition (PR) control of multiple degrees of freedom (DOF) has been demonstrated using a set of parallel linear discriminant analysis (LDA) classifiers trained with both discrete (1-DOF) and simultaneous (2-DOF) motion data. However, this training method presents a clinical challenge, requiring large amounts of data necessary to re-train the system. This study presents a parallel classifier training method that aims to reduce the training burden. Artificial neural networks (ANNs) were used to determine a nonlinear mapping between surface EMG features of 2-DOF motions and their 1-DOF motion components. The mapping was then used to transform experimentally collected features of 1-DOF motions into simulated features of 2-DOF motions. A set of parallel LDA classifiers were trained using the novel training method and two previously reported training methods. The training methods evaluated were (1) using experimentally collected 1-DOF data and ANN-simulated 2-DOF data, (2) using only experimentally collected 1-DOF data and (3) using experimentally collected 1- and 2-DOF data. Using the novel training method resulted in significantly lower classification error overall (p<;0.01) and in predicting 2-DOF motions (p<;0.01) compared to training with experimental 1-DOF data only. These findings demonstrate that using a set of ANNs to predict 2-DOF data from 1-DOF data can improve system performance when only discrete training data are available, thus reducing the training burden of simultaneous PR control.

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.

Contributions of knee swing initiation and ankle plantar flexion to the walking mechanics of amputees using a powered prosthesis.

Recently developed powered prostheses are capable of producing near-physiological joint torque at the knee and/or ankle joints. Based on previous studies of biological joint impedance and the mechanics of able-bodied gait, an impedance-based controller has been developed for a powered knee and ankle prosthesis that integrates knee swing initiation and powered plantar flexion in late stance with increasing ankle stiffness throughout stance. In this study, five prosthesis configuration conditions were tested to investigate the individual contributions of each sub-strategy to the overall walking mechanics of four unilateral transfemoral amputees as they completed a clinical 10-m walk test using a powered knee and ankle prosthesis. The baseline condition featured constant ankle stiffness and no swing initiation or powered plantar flexion. The four remaining conditions featured knee swing initiation alone (SI) or in combination with powered plantar flexion (SI+PF), increasing ankle stiffness (SI+IK), or both (SI+PF+IK). Self-selected walking speed did not significantly change between conditions, although subjects tended to walk the slowest in the baseline condition compared to conditions with swing initiation. The addition of powered plantar flexion resulted in significantly higher ankle power generation in late stance irrespective of ankle stiffness. The inclusion of swing initiation resulted in a significantly more flexed knee at toe off and a significantly higher average extensor knee torque following toe off. Identifying individual contributions of intrinsic control strategies to prosthesis biomechanics could help inform the refinement of impedance-based prosthesis controllers and simplify future designs of prostheses and lower-limb assistive devices alike.