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

A Scientometric Analysis and Visualization of Prosthetic Foot Research Work: 2000 to 2022

This study aims to highlight recent research work on topics around prosthetic feet through a scientometric analysis and historical review. The most cited publications from the Clarivate Analytics Web of Science Core Collection database were identified and analyzed from 1 January 2000 to 31 October 2022. Original articles, reviews with full manuscripts, conference proceedings, early access documents, and meeting abstracts were included. A scientometric visualization analysis of the bibliometric information related to the publications, including the countries, institutions, journals, references, and keywords, was conducted. A total of 1827 publications met the search criteria in this study. The related publications grouped by year show an overall trend of increase during the two decades from 2000 to 2022. The United States is ranked first in terms of overall influence in this field (n = 774). The Northwestern University has published the most papers on prosthetic feet (n = 84). Prosthetics and Orthotics International has published the largest number of studies on prosthetic feet (n = 151). During recent years, a number of studies with citation bursts and burst keywords (e.g., diabetes, gait, pain, and sensor) have provided clues on the hotspots of prosthetic feet and prosthetic foot trends. The findings of this study are based on a comprehensive analysis of the literature and highlight the research topics on prosthetic feet that have been primarily explored. The data provide guidance to clinicians and researchers to further studies in this field.

Predicting Individualized Joint Kinematics Over Continuous Variations of Walking, Running, and Stair Climbing

Goal: Accounting for gait individuality is important to positive outcomes with wearable robots, but manually tuning multi-activity models is time-consuming and not viable in a clinic. Generalizations can possibly be made to predict gait individuality in unobserved conditions. Methods: Kinematic individuality-how one person's joint angles differ from the group-is quantified for every subject, joint, ambulation mode (walking, running, stair ascent, and stair descent), and intramodal task (speed, incline) in an open-access dataset with 10 able-bodied subjects. Four N-way ANOVAs test how prediction methods affect the fit to experimental data between and within ambulation modes. We test whether walking individuality (measured at a single speed on level ground) carries across modes, or whether a mode-specific prediction (based on a single task for each mode) is significantly more effective. Results: Kinematic individualization improves fit across joint and task if we consider each mode separately. Across all modes, tasks, and joints, modal individualization improved the fit in 81% of trials, improving the fit on average by 4.3[Formula: see text] across the gait cycle. This was statistically significant at all joints for walking and running, and half the joints for stair ascent/descent. Conclusions: For walking and running, kinematic individuality can be easily generalized within mode, but the trends are mixed on stairs depending on joint.

Ambulation Control System Design for a Hybrid Knee Prosthesis

Prosthetic knees available to individuals with transfemoral amputation seek to restore functional ability to the user. Passive prosthetic knees are lightweight but can restore only limited, dissipative ambulation activities whereas active knees can provide energy to restore additional ambulation activities such as stair climbing and standing up from a chair. Semi-active prosthetic devices aim to only power a subset of activities and use passive components and control when that power is not necessary. Here, we outline an ambulation control system for a lightweight Hybrid Knee prosthesis that is powered for climbing stairs and passive for other ambulation activities (level-ground walking, walking on an incline, and stair descent). We include preliminary offline and online intent recognition system results for one able-bodied user and one individual with a transfemoral amputation demonstrating low error rates in predicting between active and passive control.

Powered Knee and Ankle Prosthesis with Adaptive Control Enables Climbing Stairs with Different Stair Heights, Cadences, and Gait Patterns

Powered prostheses can enable individuals with above-knee amputations to ascend stairs step-over-step. To accomplish this task, available stair ascent controllers impose a pre-defined joint impedance behavior or follow a pre-programmed position trajectory. These control approaches have proved successful in the laboratory. However, they are not robust to changes in stair height or cadence, which is essential for real-world ambulation. Here we present an adaptive stair ascent controller that enables individuals with above-knee amputations to climb stairs of varying stair heights at their preferred cadence and with their preferred gait pattern. We found that modulating the prosthesis knee and ankle position as a function of the user's thigh in swing provides toe clearance for varying stair heights. In stance, modulating the torque-angle relationship as a function of the prosthesis knee position at foot contact provides sufficient torque assistance for climbing stairs of different heights. Furthermore, the proposed controller enables individuals to climb stairs at their preferred cadence and gait pattern, such as step-by-step, step-over-step, and two-steps. The proposed adaptive stair controller may improve the robustness of powered prostheses to environmental and human variance, enabling powered prostheses to more easily move from the lab to the real-world.

Stand-Up, Squat, Lunge, and Walk With a Robotic Knee and Ankle Prosthesis Under Shared Neural Control

Emerging robotic knee and ankle prostheses present an opportunity to restore the biomechanical function of missing biological legs, which is not possible with conventional passive prostheses. However, challenges in coordinating the robotic prosthesis movements with the user's neuromuscular system and transitioning between activities limit the real-world viability of these devices. Here we show that a shared neural control approach combining neural signals from the user's residual limb with robot control improves functional mobility in individuals with above-knee amputation. The proposed shared neural controller enables subjects to stand up and sit down under a variety of conditions, squat, lunge, walk, and seamlessly transition between activities without explicit classification of the intended movement. No other available technology can enable individuals with above-knee amputations to achieve this level of mobility. Further, we show that compared to using a conventional passive prosthesis, the proposed shared neural controller significantly reduced muscle effort in both the intact limb (21-51% decrease) and the residual limb (38-48% decrease). We also found that the body weight lifted by the prosthesis side increased significantly while standing up with the robotic leg prosthesis (49%-68% increase), leading to better loading symmetry (43-46% of body weight on the prosthesis side). By decreasing muscle effort and improving symmetry, the proposed shared neural controller has the potential to improve amputee mobility and decrease the risk of falls compared to using conventional passive prostheses.

Powered knee and ankle prosthesis with indirect volitional swing control enables level-ground walking and crossing over obstacles

Powered prostheses aim to mimic the missing biological limb with controllers that are finely tuned to replicate the nominal gait pattern of non-amputee individuals. Unfortunately, this control approach poses a problem with real-world ambulation, which includes tasks such as crossing over obstacles, where the prosthesis trajectory must be modified to provide adequate foot clearance and ensure timely foot placement. Here, we show an indirect volitional control approach that enables prosthesis users to walk at different speeds while smoothly and continuously crossing over obstacles of different sizes without explicit classification of the environment. At the high level, the proposed controller relies on a heuristic algorithm to continuously change the maximum knee flexion angle and the swing duration in harmony with the user's residual limb. At the low level, minimum-jerk planning is used to continuously adapt the swing trajectory while maximizing smoothness. Experiments with three individuals with above-knee amputation show that the proposed control approach allows for volitional control of foot clearance, which is necessary to negotiate environmental barriers. Our study suggests that a powered prosthesis controller with intrinsic, volitional adaptability may provide prosthesis users with functionality that is not currently available, facilitating real-world ambulation.

Design, development, and testing of a lightweight hybrid robotic knee prosthesis

We present a lightweight robotic knee prosthesis with a novel hybrid actuation system that enables passive and active operation modes. The proposed hybrid knee uses a spring-damper system in combination with an electric motor and transmission system, which can be engaged to provide a stair ambulation capability. In comparison to fully powered prostheses that power all ambulation activities, a hybrid knee prosthesis can achieve significant weight reduction by focusing the design of the actuator on a subset of activities without losing the ability to produce equivalent torque and mechanical power in the active mode. The hybrid knee prototype weighs 1.7 kg, including battery and control, and can provide up to 125 Nm of repetitive torque. Experiments with two transfemoral amputee subjects show that the proposed hybrid knee prosthesis can support walking on level ground in the passive mode, as well as stair ambulation with a reciprocal gait pattern in the active mode.

Real-Time Continuous Gait Phase and Speed Estimation from a Single Sensor

Human gait involves a repetitive cycle of movements, and the phase of gait represents the location in this cycle. Gait phase is measured across many areas of study (e.g., for analyzing gait and controlling powered lower-limb prosthetic and orthotic devices). Current gait phase detection methods measure discrete gait events (e.g., heel strike, flat foot, toe off, etc.) by placing multiple sensors on the subject's lower-limbs. Using multiple sensors can create difficulty in experimental setup and real-time data processing. In addition, detecting only discrete events during the gait cycle limits the amount of information available during locomotion. In this paper we propose a real-time and continuous measurement of gait phase parameterized by a mechanical variable (i.e., phase variable) from a single sensor measuring the human thigh motion. Human subject experiments demonstrate the ability of the phase variable to accurately parameterize gait progression for different walking/running speeds (1 to 9 miles/hour). Our results show that this real-time method can also estimate gait speed from the same sensor.

A Unified Parameterization of Human Gait Across Ambulation Modes

This paper introduces a novel gait parameterization method that models gait kinematics as a continuous function of gait cycle phase, walking speed, and ground slope. Kinematic data was recorded from seven able-bodied subjects walking on a treadmill at twenty-seven combinations of walking speed and ground slope. Convex optimization was used to determine the parameters of a function of three variables that fits this experimental data. This function may be able to provide desired trajectories to a virtual constraint controller over a continuum of gait phases and ambulation modes. This could allow for a single, non-switching controller to control a prosthetic leg for a variety of tasks, avoiding many of the problems associated with the ubiquitous use of finite state machines in prosthesis control.

Considering passive mechanical properties and patient user motor performance in lower limb prosthesis design optimization to enhance rehabilitation outcomes

Background

Selection of prosthesis mechanical characteristics to restore function of persons with lower-limb loss can be framed as an optimization problem to satisfy a given performance objective. However, the choice of a particular objective is critical, and considering only device and generalizable outcomes across users without accounting for inherent motor performance likely restricts a given patient from fully realizing the benefits of a prosthetic intervention.

Objectives

This review presents methods for optimizing passive below-knee prosthesis designs to maximize rehabilitation outcomes and how considerations on patient motor performance may enhance these outcomes.

Major Findings

Available literature supports that considering patient-specific variables pertaining to motor performance permits a multidimensional landscape relating device characteristics and user function, which may yield more accurate predictions of rehabilitation outcomes for individual patients. Moreover, the addition of targeted physical therapeutic interventions that encourage user self-organization may further improve these outcomes. We note the potential of existing paradigms to address these additional dimensions, and we encourage investigators to consider the many different performance objectives available for prosthesis optimization.

Conclusions

By considering user motor performance in combination with prosthesis mechanical characteristics, a staged optimization approach can be formulated which acknowledges that device modifications may only improve outcomes to a certain extent and user self-organization is a critical component to complete rehabilitation. An iterative process that can be integrated within existing rehabilitative practices accounts for changes in patient status through combined targeted prosthetic solutions and physical therapeutic techniques, and embodies the concept of personalized intervention for patients with lower limb-loss.

Knee stiffness estimation in physiological gait

During physiological gait, humans continuously modulate their knee stiffness, depending on the demands of the activity and the terrain. A similar functionality could be provided by modern actuators in transfemoral prosthesis. However, quantitative data on how knee stiffness is modulated during physiological gait is still missing. This is likely due to the experimental difficulties associated with identifying knee stiffness by applying perturbations during gait. It is our goal to quantify such stiffness modulation during gait without the need to apply perturbations. Therefore, we have recently presented an approach to quantify knee stiffness from kinematic, kinetic and electromyographic (EMG) measurements, and have validated it in isometric conditions. The goal of this paper is to extend this approach to non-isometric conditions by combining inverse dynamics and EMG measurements, and to quantify physiological stiffness modulation in the example of level-ground walking. We show that stiffness varies substantially throughout a gait cycle, with a stiffness of around 100 Nm/rad during swing phase, and a peak of 450 Nm/rad in stance phase. These quantitative results may be beneficial for design and control of transfemoral prostheses and orthoses that aim to restore physiological function.

Minimum jerk swing control allows variable cadence in powered transfemoral prostheses

We present a novel swing phase controller for powered transfemoral prostheses based on minimum jerk theory. The proposed controller allows physiologically appropriate swing movement at any walking speed, regardless of the stance controller action. Preliminary validation in a transfemoral amputee subject demonstrates that the proposed controller provides physiological swing timing, without speed-or patient-specific tuning.

SIMULTANEOUS CONTROL OF AN ANKLE-FOOT PROSTHESIS MODEL USING A VIRTUAL CONSTRAINT

Amputee locomotion can benefit from recent advances in robotic prostheses, but their control systems design poses challenges. Prosthesis control typically discretizes the nonlinear gait cycle into phases, with each phase controlled by different linear controllers. Unfortunately, real-time identification of gait phases and tuning of controller parameters limit implementation. Recently, biped robots have used phase variables and virtual constraints to characterize the gait cycle as a whole. Although phase variables and virtual constraints could solve issues with discretizing the gait cycle, the virtual constraints method from robotics does not readily translate to prosthetics because of hard-to-measure quantities, like the interaction forces between the user and prosthesis socket, and prosthesis parameters which are often altered by a clinician even for a known patient. We use the simultaneous stabilization approach to design a low-order, linear time-invariant controller for ankle prostheses independent of such quantities to enforce a virtual constraint. We show in simulation that this controller produces suitable walking gaits for a simplified amputee model.

Virtual Constraint Control of a Powered Prosthetic Leg: From Simulation to Experiments with Transfemoral Amputees

Recent powered (or robotic) prosthetic legs independently control different joints and time periods of the gait cycle, resulting in control parameters and switching rules that can be difficult to tune by clinicians. This challenge might be addressed by a unifying control model used by recent bipedal robots, in which virtual constraints define joint patterns as functions of a monotonic variable that continuously represents the gait cycle phase. In the first application of virtual constraints to amputee locomotion, this paper derives exact and approximate control laws for a partial feedback linearization to enforce virtual constraints on a prosthetic leg. We then encode a human-inspired invariance property called effective shape into virtual constraints for the stance period. After simulating the robustness of the partial feedback linearization to clinically meaningful conditions, we experimentally implement this control strategy on a powered transfemoral leg. We report the results of three amputee subjects walking overground and at variable cadences on a treadmill, demonstrating the clinical viability of this novel control approach.