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

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.

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.

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.