A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses

Outdoor Overground Gait Biomechanics and Energetics in Individuals With Transtibial Amputation Walking With a Prescribed Passive Prosthesis and a Bionic Myoelectric Prosthesis

The metabolic cost of walking for individuals with transtibial amputation is generally greater compared with able-bodied individuals. One aim of powered prostheses is to reduce metabolic deficits by replicating biological ankle function. Individuals with transtibial amputation can activate their residual limb muscles to volitionally control bionic ankle prostheses for walking; however, it is unknown how myoelectric control performs outside the laboratory. We recruited 6 individuals with transtibial amputation to walk an outdoor course with the Open Source Leg prosthesis under continuous proportional myoelectric control and compared it with their passive device. There were no significant differences (P = .142) in cost of transport between prostheses. Participants significantly increased residual limb vastus lateralis (P = .042) and rectus femoris (P = .029) muscle activity during early and midstance phase of walking with the powered prosthesis compared with their passive device. All but one participant preferred walking with myoelectric control compared with their passive prosthesis. The additional mass of the powered ankle prosthesis coupled with increased residual quadriceps activity could explain why the energy cost of walking was not lower compared with a passive prosthesis. This study demonstrates participants can volitionally control a bionic ankle prosthesis to navigate real-world environments.

Mode-Unified Intent Estimation of a Robotic Prosthesis using Deep-Learning

Traditional robotic knee-ankle prostheses categorize ambulation modes such as level walking, ramps, and stairs. However, human movement scales continuously across various states rather than discretely, making traditional mode classifiers inadequate for accurate intent recognition. This paper proposes a mode-unified intent recognition strategy that continuously estimates terrain slopes across five modes: level ground, ramp ascent/descent, and stair ascent/descent. Locomotion data from 16 individuals with transfemoral amputation were utilized to train slope estimation and mode classification models based on deep temporal convolutional networks. The proposed method was compared to the traditional mode classifier via offline test, using leave-one-subject-out validations for the user-independent performance. The mode-unified slope estimator achieved an MAE of 1.68 ± 0.60 degrees, outperforming the mode classifier's MAE of 1.94 ± 0.97 degrees (p<0.05). The lower slope estimation errors resulted in higher accuracy in replicating knee kinematics of able-bodied subjects, with the proposed system achieving an average MAE of 5.13 ± 2.00 degrees for knee clearance and 6.74 ± 2.97 degrees for knee contact angle, compared to the traditional classifier's 12.10 ± 5.20 degrees and 13.80 ± 3.28 degrees (p<0.01), respectively, in stair ascent. These results suggest that our mode-unified approach can enable continuous adjustment to terrains without mode classification.

A novel approach to assess coordination in people with transtibial amputations using continuous and event relative phase

Continuous relative phase (CRP) quantifies coordination for cyclic motions as the difference in the phase portrait locations between its constituent coordinates and has been widely used in populations with neuromuscular impairments. Continuous analyses, like statistical parameter mapping (SPM), provide greater resolution than traditional techniques that first compress CRP across a section of the cycle to a single point, like mean average relative phase (MARP). However, both analyses neglect the effect of intermediate event timing (e.g. toe-off), on coordination. Given this deficit and the notion that some people with transtibial amputations (PwTA) may not benefit from powered prostheses due to altered coordination, we computed lower extremity CRPs from 5 PwTA walking with their own passive prostheses and a powered device on a treadmill, as well as 5 matched able-bodied individuals (ABI). We then compared results from non-parametric SPMs to those from MARP using a 10-40-10-40 gait phase decomposition and extracted relative phase at the events that theoretically delineate the decomposition. We found continuous, discrete analyses matched well, particularly near ankle "push-off" (∼55 % gait cycle) with all methods identifying differences in shank-foot coordination between the ABI group and PwTA group walking with the powered device. Although it is unclear why the powered prosthesis promotes more in-phase shank-foot CRP, potential covariates include limb posture and device control. In tandem with altered event timing, these factors may not only influence coordination, but also illuminate why some PwTA do not reduce their energy expenditure when walking in powered ankle prostheses.

A type-2 fuzzy inference-based approach enables walking speed estimation that adapts to inter-individual gait patterns

OBJECTIVES

Individuals change walking speed by regulating step frequency (SF), stride length (SL), or a combination of both (FL combinations). However, existing methods of walking speed estimation ignore this regulatory mechanism. This paper aims to achieve accurate walking speed estimation while enabling adaptation to inter-individual speed regulation strategies.

METHODS

We first extracted thigh features closely related to individual speed regulation based on a single thigh mounted IMU. Next, an interval type-2 fuzzy inference system was used to infer and quantify the individuals' speed regulation intentions, enabling speed estimation independent of inter-individual gait patterns. Experiments with five subjects walking on a treadmill at different speeds and with different gait patterns validated our method.

RESULTS

The overall root mean square error (RMSE) for speed estimation was 0.0704 ± 0.0087 m/s, and the RMSE for different gait patterns was no more than 0.074 ± 0.005 m/s.

CONCLUSIONS

The proposed method provides high-accuracy speed estimation. Moreover, our method can be adapted to different FL combinations without the need for individualised tuning or training of individuals with varying limb lengths and gait habits. We anticipate that the proposed method will help provide more intuitive speed adaptive control for rehabilitation robots, especially intelligent lower limb prostheses.

Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model

The volitional control of powered assistive devices is commonly performed by mapping the electromyographic (EMG) activity of the lower limb to joints' angular kinematics, which are then used as the input for regulation. However, during walking, the ground reaction force (GRF) plays a central role in the modulation of the gait, providing dynamic stability and propulsion during the stance phase. Including this information within the control loop of prosthetic devices can improve the quality of the final output, providing more physiological walking dynamics that enhances the usability and patient comfort. In this work, we explored the feasibility of the estimation of the ground reaction force vertical component (VGRF) by using only the EMG activities of the thigh and shank muscles. We compared two deep learning models in three experiments that involved different muscular configurations. Overall, the outcomes show that the EMG signals could be leveraged to obtain a reliable estimation of the VGRF during walking, and the shank muscles alone represent a viable solution if a reduced recording setup is needed. On the other hand, the thigh muscles failed in providing performance enhancements, either when used alone or together with the shank muscles. The results outline the feasibility of including GRF information within an EMG-driven control scheme for prosthetic and assistive devices.

A Swing-Assist Controller for Enhancing Knee Flexion in a Semi-Powered Transfemoral Prosthesis

This work proposes a new swing controller for semi-powered low impedance transfemoral prostheses that resolves the issue of potentially competing inputs between artificial assistive power and user-sourced power. Rather than add power as an exogeneous input, the control approach uses power to modify the homogeneous portion of the shank dynamics, and therefore need not construct or curate an input that is coordinated with user input. The implemented controller requires a single control parameter at a given walking speed, where the value of that parameter is a function of walking speed, as determined by an adaptive algorithm, such that peak knee angles are commensurate with walking-speed-dependent behaviors of individuals without any negative gait pathologies. The controller and parameter selection algorithm are described in the paper, and subsequently validated in walking experiments with three participants with unilateral transfemoral amputation. The experiments demonstrate that the proposed controller increases peak knee angle and minimum toe clearance during swing phase without increasing hip compensatory actions, relative to the users' daily-use devices.

Controlling Powered Prosthesis Kinematics Over Continuous Inter-Leg Transitions Between Walking and Stair Ascent/Descent

Although powered prosthetic legs have enabled more biomimetic joint kinematics during steady-state activities like walking and stair climbing, transitions between these activities are usually handled by discretely switching controllers without considering biomimicry or the distinct role of the leading leg. This study introduces two data-driven, phase-based kinematic control approaches for seamless inter-leg transitions (i.e., initiated by either the prosthetic or intact leg) between walking and stair ascent/descent, assuming high-level knowledge of the upcoming activity. One approach employs a novel continuously-varying kinematic model that interpolates between steady-state activities as an approximate convex combination, and the other approach employs a simple switching-based model with optimized switching timing and tunable smoothing of kinematic discontinuities. Data-driven analysis indicates the continuously-varying controller remains beneficial over the switching controller for a range of classification delays. Experimental validation with a powered knee-ankle prosthesis used by two high-functioning transfemoral amputees demonstrates the continuous controller can provide more biomimetic and uninterrupted kinematic trajectories for both joints during transitions, irrespective of the initiating leg. This research underscores the potential for enabling more natural locomotion for high-functioning prosthetic leg users.

Muscle recruitment during gait in individuals with unilateral transfemoral amputation due to trauma compared to able-bodied controls

Individuals with transfemoral lower limb amputations walk with adapted gait. These kinetic and kinematic compensatory strategies will manifest as differences in muscle recruitment patterns. It is important to characterize these differences to understand the reduced endurance, reduced functionality, and progression of co-morbidities in this population. This study aims to characterize muscle recruitment during gait of highly functional individuals with traumatic transfemoral amputations donning state-of-the-art prosthetics compared to able-bodied controls. Inverse dynamic and static optimisation methods of musculoskeletal modelling were used to quantify muscle forces of the residual and intact limb over a gait cycle for 11 individuals with traumatic transfemoral amputation and for 11 able-bodied controls. Estimates of peak muscle activation and impulse were calculated to assess contraction intensity and energy expenditure. The generalized estimation equation method was used to compare the maximum values of force, peak activation, and impulse of the major muscles. The force exhibited by the residual limb's iliacus, psoas major, adductor longus, tensor fasciae latae and pectineus is significantly higher than the forces in these muscles of the intact contralateral limb group and the able-bodied control group (p < 0.001). These muscles appear to be recruited for their flexor moment arm, indicative of the increased demand due to the loss of the plantar flexors. The major hip extensors are recruited to a lesser degree in the residual limb group compared to the intact limb group (p < 0.001). The plantar flexors of the intact limb appear to compensate for the amputated limb with significantly higher forces compared to the able-bodied controls (p = 0.01). Significant differences found in impulse and peak activation consisted of higher values for the limbs (residual and/or intact) of individuals with transfemoral lower limb amputations compared to the able-bodied controls, demonstrating an elevated cost of gait. This study highlights asymmetry in hip muscle recruitment between the residual and the intact limb of individuals with transfemoral lower limb amputations. Overall elevated impulse and peak activation in the limbs of individuals with transfemoral amputation, compared to able-bodied controls, may manifest in the reduced walking endurance of this population. This demand should be minimised in rehabilitation protocols.

Phasor-Based Myoelectric Synergy Features: A Fast Hand-Crafted Feature Extraction Scheme for Boosting Performance in Gait Phase Recognition

Gait phase recognition systems based on surface electromyographic signals (EMGs) are crucial for developing advanced myoelectric control schemes that enhance the interaction between humans and lower limb assistive devices. However, machine learning models used in this context, such as Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), typically experience performance degradation when modeling the gait cycle with more than just stance and swing phases. This study introduces a generalized phasor-based feature extraction approach (PHASOR) that captures spatial myoelectric features to improve the performance of LDA and SVM in gait phase recognition. A publicly available dataset of 40 subjects was used to evaluate PHASOR against state-of-the-art feature sets in a five-phase gait recognition problem. Additionally, fully data-driven deep learning architectures, such as Rocket and Mini-Rocket, were included for comparison. The separability index (SI) and mean semi-principal axis (MSA) analyses showed mean SI and MSA metrics of 7.7 and 0.5, respectively, indicating the proposed approach's ability to effectively decode gait phases through EMG activity. The SVM classifier demonstrated the highest accuracy of 82% using a five-fold leave-one-trial-out testing approach, outperforming Rocket and Mini-Rocket. This study confirms that in gait phase recognition based on EMG signals, novel and efficient muscle synergy information feature extraction schemes, such as PHASOR, can compete with deep learning approaches that require greater processing time for feature extraction and classification.

Walking Ankle Biomechanics of Individuals With Transtibial Amputations Using a Prescribed Prosthesis and a Portable Bionic Prosthesis Under Myoelectric Control

Individuals with transtibial amputation can activate residual limb muscles to volitionally control robotic ankle prostheses for walking and postural control. Most continuous myoelectric ankle prostheses have used a tethered, pneumatic device. The Open Source Leg allows for myoelectric control on an untethered electromechanically actuated ankle. To evaluate continuous proportional myoelectric control on the Open Source Ankle, we recruited five individuals with transtibial amputation. Participants walked over ground with an experimental powered prosthesis and their prescribed passive prosthesis before and after multiple powered device practice sessions. Participants averaged five hours of total walking time. After the final testing session, participants indicated their prosthesis preference via questionnaire. Participants tended to increase peak ankle power after practice (powered 0.80 ± 1.02 W/kg and passive 0.39 ± 0.31 W/kg). Additionally, participants tended to generate greater ankle work with the powered prosthesis compared to their passive device ( 0.13 ± .15 J/kg increase). Although work and peak power generation were not statistically different between the two prostheses, participants preferred walking with the prosthesis under myoelectric control compared to the passive prosthesis. These results indicate individuals with transtibial amputation learned to walk with an untethered powered prosthesis under continuous myoelectric control. Four out 5 participants generated larger magnitudes in peak power compared to their passive prosthesis after practice sessions. An additional important finding was participants chose to walk with peak ankle powers about half of what the powered prosthesis was capable of based on mechanical testing.

Automatic Stub Avoidance for a Powered Prosthetic Leg Over Stairs and Obstacles

Passive prosthetic legs require undesirable compensations from amputee users to avoid stubbing obstacles and stairsteps. Powered prostheses can reduce those compensations by restoring normative joint biomechanics, but the absence of user proprioception and volitional control combined with the absence of environmental awareness by the prosthesis increases the risk of collisions. This article presents a novel stub avoidance controller that automatically adjusts prosthetic knee/ankle kinematics based on suprasensory measurements of environmental distance from a small, lightweight, low-power, low-cost ultrasonic sensor mounted above the prosthetic ankle. In a case study with two transfemoral amputee participants, this control method reduced the stub rate during stair ascent by 89.95% and demonstrated an 87.50% avoidance rate for crossing different obstacles on level ground. No thigh kinematic compensation was required to achieve these results. These findings demonstrate a practical perception solution for powered prostheses to avoid collisions with stairs and obstacles while restoring normative biomechanics during daily activities.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Prosthetic Limb Attachment via Electromagnetic Attraction Through a Closed Skin Envelope

OBJECTIVE

Current socket-based methods of prosthetic limb attachment are responsible for many of the dominant problems reported by persons with amputation. In this work, we introduce a new paradigm for attachment via electromagnetic attraction between a bone-anchored ferromagnetic implant and an external electromagnet. Our objective was to develop a design framework for electromagnetic attachment, and to evaluate this framework in the context of transfemoral amputation.

METHODS

We first used inverse dynamics to calculate the forces required to suspend a knee-ankle-foot prosthesis during gait. We then conducted cadaveric dissections to inform implant geometry and design a surgical methodology for covering the implant. We also developed an in silico framework to investigate how electromagnet design affects system performance. Simulations were validated against benchtop testing of a custom-built electromagnet.

RESULTS

The physical electromagnet matched simulations, with a root-mean-square percentage error of 4.2% between measured and predicted forces. Using this electromagnet, we estimate that suspension of a prosthesis during gait would require 33 W of average power. After 200 and 1000 steps of simulated walking, the temperature at the skin would increase 2.3 °C and 15.4 °C relative to ambient, respectively.

CONCLUSION

Our design framework produced an implant and electromagnet that could feasibly suspend a knee-ankle-foot prosthesis during short walking bouts. Future work will focus on optimization of this system to reduce heating during longer bouts.

SIGNIFICANCE

This work demonstrates the initial feasibility of an electromagnetic prosthetic attachment paradigm that has the potential to increase comfort and improve residual limb health for persons with amputation.

Joint iterative state and parameter estimation for bilinear systems with autoregressive noises via the data filtering

This paper proposes a novel iterative algorithm for the joint state and parameter estimation of bilinear state-space systems disturbed by colored noise. Estimating the states and parameters of such systems is challenging due to their nonlinearity and greater number of parameters compared to linear systems. Our method is to modify the Kalman filtering appropriately to estimate the unknown states of bilinear systems. Once the unknown states are estimated, we develop the Kalman filtering-based multi-innovation gradient-based iterative (KF-MIGI) algorithm for parameter estimation. To further improve estimation accuracy and cope with colored noises, we introduce a data filtering-based KF-MIGI algorithm that uses an adaptive filter to filter input-output data. Additionally, we compare the gradient-based iterative algorithm and the stochastic gradient algorithm. The effectiveness of the proposed algorithm is demonstrated through numerical examples.

A Minimal and Multi-Source Recording Setup for Ankle Joint Kinematics Estimation During Walking Using Only Proximal Information From Lower Limb

In this study, a minimal setup for the ankle joint kinematics estimation is proposed relying only on proximal information of the lower-limb, i.e. thigh muscles activity and joint kinematics. To this purpose, myoelectric activity of Rectus Femoris (RF), Biceps Femoris (BF), and Vastus Medialis (VM) were recorded by surface electromyography (sEMG) from six healthy subjects during unconstrained walking task. For each subject, the angular kinematics of hip and ankle joints were synchronously recorded with sEMG signal for a total of 288 gait cycles. Two feature sets were extracted from sEMG signals, i.e. time domain (TD) and wavelet (WT) and compared to have a compromise between the reliability and computational capacity, they were used for feeding three regression models, i.e. Artificial Neural Networks, Random Forest, and Least Squares - Support Vector Machine (LS-SVM). BF together with LS-SVM provided the best ankle angle estimation in both TD and WT domains (RMSE < 5.6 deg). The inclusion of Hip joint trajectory significantly enhanced the regression performances of the model (RMSE < 4.5 deg). Results showed the feasibility of estimating the ankle trajectory using only proximal and limited information from the lower limb which would maximize a potential transfemoral amputee user's comfortability while facing the challenge of having a small amount of information thus requiring robust data-driven models. These findings represent a significant step towards the development of a minimal setup useful for the control design of ankle active prosthetics and rehabilitative solutions.

Data-Driven Phase-Based Control of a Powered Knee-Ankle Prosthesis for Variable-Incline Stair Ascent and Descent

Powered knee-ankle prostheses can offer benefits over conventional passive devices during stair locomotion by providing biomimetic net-positive work and active control of joint angles. However, many modern control approaches for stair ascent and descent are often limited by time-consuming hand-tuning of user/task-specific parameters, predefined trajectories that remove user volition, or heuristic approaches that cannot be applied to both stair ascent and descent. This work presents a phase-based hybrid kinematic and impedance controller (HKIC) that allows for semi-volitional, biomimetic stair ascent and descent at a variety of step heights. We define a unified phase variable for both stair ascent and descent that utilizes lower-limb geometry to adjust to different users and step heights. We extend our prior data-driven impedance model for variable-incline walking, modifying the cost function and constraints to create a continuously-varying impedance parameter model for stair ascent and descent over a continuum of step heights. Experiments with above-knee amputee participants (N=2) validate that our HKIC controller produces biomimetic ascent and descent joint kinematics, kinetics, and work across four step height configurations. We also show improved kinematic performance with our HKIC controller in comparison to a passive microprocessor-controlled device during stair locomotion.

Ankle Torque Estimation With Motor Unit Discharges in Residual Muscles Following Lower-Limb Amputation

There has been increased interest in using residual muscle activity for neural control of powered lower-limb prostheses. However, only surface electromyography (EMG)-based decoders have been investigated. This study aims to investigate the potential of using motor unit (MU)-based decoding methods as an alternative to EMG-based intent recognition for ankle torque estimation. Eight people without amputation (NON) and seven people with amputation (AMP) participated in the experiments. Subjects conducted isometric dorsi- and plantarflexion with their intact limb by tracing desired muscle activity of the tibialis anterior (TA) and gastrocnemius (GA) while ankle torque was recorded. To match phantom limb and intact limb activity, AMP mirrored muscle activation with their residual TA and GA. We compared neuromuscular decoders (linear regression) for ankle joint torque estimation based on 1) EMG amplitude (aEMG), 2) MU firing frequencies representing neural drive (ND), and 3) MU firings convolved with modeled twitch forces (MUDrive). In addition, sensitivity analysis and dimensionality reduction of optimization were performed on the MUDrive method to further improve its practical value. Our results suggest MUDrive significantly outperforms (lower root-mean-square error) EMG and ND methods in muscles of NON, as well as both intact and residual muscles of AMP. Reducing the number of optimized MUDrive parameters degraded performance. Even so, optimization computational time was reduced and MUDrive still outperformed aEMG. Our outcomes indicate integrating MU discharges with modeled biomechanical outputs may provide a more accurate torque control signal than direct EMG control of assistive, lower-limb devices, such as exoskeletons and powered prostheses.

Shared Autonomy Locomotion Synthesis With a Virtual Powered Prosthetic Ankle

Virtual environments provide a safe and accessible way to test innovative technologies for controlling wearable robotic devices. However, to simulate devices that support walking, such as powered prosthetic legs, it is not enough to model the hardware without its user. Predictive locomotion synthesizers can generate the movements of a virtual user, with whom the simulated device can be trained or evaluated. We implemented a Deep Reinforcement Learning based motion controller in the MuJoCo physics engine, where autonomy over the humanoid model was shared between the simulated user and the control policy of an active prosthesis. Despite not optimising the controller to match experimental dynamics, realistic torque profiles and ground reaction force curves were produced by the agent. A data-driven and continuous representation of user intent was used to simulate a Human Machine Interface that controlled a transtibial prosthesis in a non-steady state walking setting. The continuous intent representation was shown to mitigate the need for compensatory gait patterns from their virtual users and halve the rate of tripping. Co-adaptation was identified as a potential challenge for training human-in-the-loop prosthesis control policies. The proposed framework outlines a way to explore the complex design space of robot-assisted gait, promoting the transfer of the next generation of intent driven controllers from the lab to real-life scenarios.

Investigating the Association of Quantitative Gait Stability Metrics With User Perception of Gait Interruption Due to Control Faults During Human-Prosthesis Interaction

This study aims to compare the association of different gait stability metrics with the prosthesis users' perception of their own gait stability. Lack of perceived confidence on the device functionality can influence the gait pattern, level of daily activities, and overall quality of life for individuals with lower limb motor deficits. However, the perception of gait stability is subjective and difficult to acquire online. The quantitative gait stability metrics can be objectively measured and monitored using wearable sensors; however, objective measurements of gait stability associated with human's perception of their own gait stability has rarely been reported. By identifying quantitative measurements that associate with users' perceptions, we can gain a more accurate and comprehensive understanding of an individual's perceived functional outcomes of assistive devices such as prostheses. To achieve our research goal, experiments were conducted to artificially apply internal disturbances in the powered prosthesis while the prosthetic users performed level ground walking. We monitored and compared multiple gait stability metrics and a local measurement to the users' reported perception of their own gait stability. The results showed that the center of pressure progression in the sagittal plane and knee momentum (i.e., residual thigh and prosthesis shank angular momentum about prosthetic knee joint) can potentially estimate the users' perceptions of gait stability when experiencing disturbances. The findings of this study can help improve the development and evaluation of gait stability control algorithms in robotic prosthetic devices.

Artificial intelligence meets medical robotics

Artificial intelligence (AI) applications in medical robots are bringing a new era to medicine. Advanced medical robots can perform diagnostic and surgical procedures, aid rehabilitation, and provide symbiotic prosthetics to replace limbs. The technology used in these devices, including computer vision, medical image analysis, haptics, navigation, precise manipulation, and machine learning (ML) , could allow autonomous robots to carry out diagnostic imaging, remote surgery, surgical subtasks, or even entire surgical procedures. Moreover, AI in rehabilitation devices and advanced prosthetics can provide individualized support, as well as improved functionality and mobility (see the figure). The combination of extraordinary advances in robotics, medicine, materials science, and computing could bring safer, more efficient, and more widely available patient care in the future. -Gemma K. Alderton.

Powered knee and ankle prosthesis use with a K2 level ambulator: a case report

Powered prosthetic knees and ankles have the capability of restoring power to the missing joints and potential to provide increased functional mobility to users. Nearly all development with these advanced prostheses is with individuals who are high functioning community level ambulators even though limited community ambulators may also receive great benefit from these devices. We trained a 70 year old male participant with a unilateral transfemoral amputation to use a powered knee and powered ankle prosthesis. He participated in eight hours of therapist led in-lab training (two hours per week for four weeks). Sessions included static and dynamic balance activities for improved stability and comfort with the powered prosthesis and ambulation training on level ground, inclines, and stairs. Assessments were taken with both the powered prosthesis and his prescribed, passive prosthesis post-training. Outcome measures showed similarities in velocity between devices for level-ground walking and ascending a ramp. During ramp descent, the participant had a slightly faster velocity and more symmetrical stance and step times with the powered prosthesis compared to his prescribed prosthesis. For stairs, he was able to climb with reciprocal stepping for both ascent and descent, a stepping strategy he is unable to do with his prescribed prosthesis. More research with limited community ambulators is necessary to understand if further functional improvements are possible with either additional training, longer accommodation periods, and/or changes in powered prosthesis control strategies.