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

EMG-based prediction of step direction for a better control of lower limb wearable devices

BACKGROUND AND OBJECTIVES

Lower-limb wearable devices can significantly improve the quality of life of subjects suffering from debilitating conditions, such as amputations, neurodegenerative disorders, and stroke-related impairments. Current control approaches, limited to forward walking, fall short of replicating the complexity of human locomotion in complex environments, such as uneven terrains or crowded places. Here we propose a high-level controller based on two Support Vector Machines exploiting four surface electromyography (EMG) signals of the thigh muscles to detect the onset (Toe-off intention decoder) and the direction (Directional EMG decoder) of the upcoming step.

METHODS AND MATERIALS

We validated a preliminary version of the approach by acquiring EMG signals from ten healthy subjects, performing steps in four directions (forward, backward, right, and left), in three different settings (ground-level walking, stairs, and ramps), and in both steady-state and static conditions. Both the Toe-off intention and Directional EMG decoders have been tested with a 5-fold cross-validation repeated five times, using linear and radial-basis-function kernels, and by changing the classification output timing, from 200 ms before to 50 ms after the toe-off.

RESULTS

The Toe-off intention decoder reached a median accuracy of 83.34 % (interquartile range (IQR): 6.48) and specificity of 92.72 % (IQR: 3.62) in its radial-basis-function version, while the Directional EMG decoder's median accuracy ranged between 73.92 % (IQR: 5.8), 200 ms before the toe-off, to 92.91 % (IQR: 4.11), 50 ms after the toe-off, with the radial-basis-function kernel implementation. For both the Toe-off intention and Directional EMG decoders the radial-basis-function version achieved better performances than the linear one (Wilcoxon signed rank test, p < 0.05).

CONCLUSIONS AND SIGNIFICANCE

The combination of the two decoders proved to be a promising solution to detect the step initiation and classify its direction, paving the way for wearable devices with a broader range of movements and more degrees of freedom, ultimately promoting usability in uncontrolled settings and better reactions to external perturbations. Additionally, the encumbrance of the setup is limited to the thigh of the leg of interest, which simplifies the implementation in compact devices, concurrently limiting the sensors worn by the subject.

Continuous neural control of a bionic limb restores biomimetic gait after amputation

For centuries scientists and technologists have sought artificial leg replacements that fully capture the versatility of their intact biological counterparts. However, biological gait requires coordinated volitional and reflexive motor control by complex afferent and efferent neural interplay, making its neuroprosthetic emulation challenging after limb amputation. Here we hypothesize that continuous neural control of a bionic limb can restore biomimetic gait after below-knee amputation when residual muscle afferents are augmented. To test this hypothesis, we present a neuroprosthetic interface consisting of surgically connected, agonist-antagonist muscles including muscle-sensing electrodes. In a cohort of seven leg amputees, the interface is shown to augment residual muscle afferents by 18% of biologically intact values. Compared with a matched amputee cohort without the afferent augmentation, the maximum neuroprosthetic walking speed is increased by 41%, enabling equivalent peak speeds to persons without leg amputation. Further, this level of afferent augmentation enables biomimetic adaptation to various walking speeds and real-world environments, including slopes, stairs and obstructed pathways. Our results suggest that even a small augmentation of residual muscle afferents restores biomimetic gait under continuous neuromodulation in individuals with leg amputation.

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.

Toward the Development of User-Centered Neurointegrated Lower Limb Prostheses

The last few years witnessed radical improvements in lower-limb prostheses. Researchers have presented innovative solutions to overcome the limits of the first generation of prostheses, refining specific aspects which could be implemented in future prostheses designs. Each aspect of lower-limb prostheses has been upgraded, but despite these advances, a number of deficiencies remain and the most capable limb prostheses fall far short of the capabilities of the healthy limb. This article describes the current state of prosthesis technology; identifies a number of deficiencies across the spectrum of lower limb prosthetic components with respect to users' needs; and discusses research opportunities in design and control that would substantially improve functionality concerning each deficiency. In doing so, the authors present a roadmap of patients related issues that should be addressed in order to fulfill the vision of a next-generation, neurally-integrated, highly-functional lower limb prosthesis.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks

OBJECTIVE

Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation.

METHODS

In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network.

RESULTS

Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively.

CONCLUSION AND SIGNIFICANCE

This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

Design and optimization of a new integrated hip and knee prosthesis structure

BACKGROUND

Conventional hip disarticulation prostheses (HDPs) are passive devices with separate joint structures, limiting amputees' ability to control and resulting in abnormal gait patterns. This study introduces a new HDP integrating the hip and knee joints for amputees' natural gait.

METHODS

The new HDP restores the physiological rotation center of the hip with a remote center of motion (RCM) structure, and simulates the knee motion with a four-bar structure. Nonlinear programming was employed to optimize the hip-knee joint structure. A hybrid multi-objective drive structure with a series-parallel connection was also designed to ensure motion synergy between the hip and knee joints. Finally, a prototype of the prosthesis was tested using the HDP test system.

RESULTS

The optimization results demonstrate that the new HDP accurately restores the rotation center of the femur in amputees, with the knee's instantaneous center of rotation (ICR) trajectory closely resembling that of the human knee (Pearson correlation coefficient is 0.999). The study shows that the new HDP achieves a motion reproduction accuracy of over 95% for the human hip joint at walking speeds above 1.5 km/h, 38% higher than conventional prosthesis. Similarly, at the same walking speed, the new HDP replicates the motion of the human knee at 82.89%, surpassing conventional prosthesis by 57.85%.

CONCLUSIONS

The new HDP restores symmetry and replicates synergistic movement in amputees' lower limbs, exhibiting superior movement characteristics compared to conventional prostheses. This innovative HDP has the potential to enhance the quality of life for amputees.

A Unified Controller for Natural Ambulation on Stairs and Level Ground with a Powered Robotic Knee Prosthesis

Powered lower-limb prostheses have the potential to improve amputee mobility by closely imitating the biomechanical function of the missing biological leg. To accomplish this goal, powered prostheses need controllers that can seamlessly adapt to the ambulation activity intended by the user. Most powered prosthesis control architectures address this issue by switching between specific controllers for each activity. This approach requires online classification of the intended ambulation activity. Unfortunately, any misclassification can cause the prosthesis to perform a different movement than the user expects, increasing the likelihood of falls and injuries. Therefore, classification approaches require near-perfect accuracy to be used safely in real life. In this paper, we propose a unified controller for powered knee prostheses which allows for walking, stair ascent, and stair descent without the need for explicit activity classification. Experiments with one individual with an above-knee amputation show that the proposed controller enables seamless transitions between activities. Moreover, transition between activities is possible while leading with either the sound-side or the prosthesis. A controller with these characteristics has the potential to improve amputee mobility.

Powered Knee and Ankle Prosthesis Control for Adaptive Ambulation at Variable Speeds, Inclines, and Uneven Terrains

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

A new approach to a powered knee prosthesis: Layering powered assistance onto strictly passive prosthesis behavior

This article describes a novel approach to the control of a powered knee prosthesis where the control system provides passive behavior for most activities and then provides powered assistance only for those activities that require them. The control approach presented here is based on the categorization of knee joint function during activities into four behaviors: resistive stance behavior, active stance behavior, ballistic swing, and non-ballistic swing. The approach is further premised on the assumption that healthy non-perturbed swing-phase is characterized by a ballistic swing motion, and therefore, a replacement of that function should be similarly ballistic. The control system utilizes a six-state finite-state machine, where each state provides different constitutive behaviors (concomitant with the four aforementioned knee behaviors) which are appropriate for a range of activities. Transitions between states and torque control within states is controlled by user motion, such that the control system provides, to the extent possible, knee torque behavior as a reaction to user motion, including for powered behaviors. The control system is demonstrated on a novel device that provides a sufficiently low impedance to enable a strictly passive ballistic swing-phase, while also providing sufficiently high torque to offer powered stance-phase knee-extension during activities such as step-over stair ascent. Experiments employing the knee and control system on an individual with transfemoral amputation are presented that compare the functionality of the power-supplemented nominally passive system with that of a conventional passive microprocessor-controlled knee prosthesis.

Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Adaptive Speed and Incline Walking

Most impedance-based walking controllers for powered knee-ankle prostheses use a finite state machine with dozens of user-specific parameters that require manual tuning by technical experts. These parameters are only appropriate near the task (e.g., walking speed and incline) at which they were tuned, necessitating many different parameter sets for variable-task walking. In contrast, this paper presents a data-driven, phase-based controller for variable-task walking that uses continuously-variable impedance control during stance and kinematic control during swing to enable biomimetic locomotion. After generating a data-driven model of variable joint impedance with convex optimization, we implement a novel task-invariant phase variable and real-time estimates of speed and incline to enable autonomous task adaptation. Experiments with above-knee amputee participants (N=2) show that our data-driven controller 1) features highly-linear phase estimates and accurate task estimates, 2) produces biomimetic kinematic and kinetic trends as task varies, leading to low errors relative to able-bodied references, and 3) produces biomimetic joint work and cadence trends as task varies. We show that the presented controller meets and often exceeds the performance of a benchmark finite state machine controller for our two participants, without requiring manual impedance tuning.

Can a powered knee-ankle prosthesis improve weight-bearing symmetry during stand-to-sit transitions in individuals with above-knee amputations?

BACKGROUND

After above-knee amputation, the missing biological knee and ankle are replaced with passive prosthetic devices. Passive prostheses are able to dissipate limited amounts of energy using resistive damper systems during "negative energy" tasks like sit-down. However, passive prosthetic knees are not able to provide high levels of resistance at the end of the sit-down movement when the knee is flexed, and users need the most support. Consequently, users are forced to over-compensate with their upper body, residual hip, and intact leg, and/or sit down with a ballistic and uncontrolled movement. Powered prostheses have the potential to solve this problem. Powered prosthetic joints are controlled by motors, which can produce higher levels of resistance at a larger range of joint positions than passive damper systems. Therefore, powered prostheses have the potential to make sitting down more controlled and less difficult for above-knee amputees, improving their functional mobility.

METHODS

Ten individuals with above-knee amputations sat down using their prescribed passive prosthesis and a research powered knee-ankle prosthesis. Subjects performed three sit-downs with each prosthesis while we recorded joint angles, forces, and muscle activity from the intact quadricep muscle. Our main outcome measures were weight-bearing symmetry and muscle effort of the intact quadricep muscle. We performed paired t-tests on these outcome measures to test for significant differences between passive and powered prostheses.

RESULTS

We found that the average weight-bearing symmetry improved by 42.1% when subjects sat down with the powered prosthesis compared to their passive prostheses. This difference was significant (p = 0.0012), and every subject's weight-bearing symmetry improved when using the powered prosthesis. Although the intact quadricep muscle contraction differed in shape, neither the integral nor the peak of the signal was significantly different between conditions (integral p > 0.01, peak p > 0.01).

CONCLUSIONS

In this study, we found that a powered knee-ankle prosthesis significantly improved weight-bearing symmetry during sit-down compared to passive prostheses. However, we did not observe a corresponding decrease in intact-limb muscle effort. These results indicate that powered prosthetic devices have the potential to improve balance during sit-down for individuals with above-knee amputation and provide insight for future development of powered prosthetics.

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

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge - the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user. In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: high-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.

A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics Via an Artificial Neural Network

Lower-limb powered prostheses can provide users with volitional control of ambulation. To accomplish this goal, they require a sensing modality that reliably interprets user intention to move. Surface electromyography (EMG) has been previously proposed to measure muscle excitation and provide volitional control to upper- and lower-limb powered prosthesis users. Unfortunately, EMG suffers from a low signal to noise ratio and crosstalk between neighboring muscles, often limiting the performance of EMG-based controllers. Ultrasound has been shown to have better resolution and specificity than surface EMG. However, this technology has yet to be integrated into lower-limb prostheses. Here we show that A-mode ultrasound sensing can reliably predict the prosthesis walking kinematics of individuals with a transfemoral amputation. Ultrasound features from the residual limb of 9 transfemoral amputee subjects were recorded with A-mode ultrasound during walking with their passive prosthesis. The ultrasound features were mapped to joint kinematics through a regression neural network. Testing of the trained model against untrained kinematics from an altered walking speed show accurate predictions of knee position, knee velocity, ankle position, and ankle velocity, with a normalized RMSE of 9.0 ± 3.1%, 7.3 ± 1.6%, 8.3 ± 2.3%, and 10.0 ± 2.5% respectively. This ultrasound-based prediction suggests that A-mode ultrasound is a viable sensing technology for recognizing user intent. This study is the first necessary step towards implementation of volitional prosthesis controller based on A-mode ultrasound for individuals with transfemoral amputation.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review

A few years ago, powered prostheses triggered new technological advances in diverse areas such as mobility, comfort, and design, which have been essential to improving the quality of life of individuals with lower limb disability. The human body is a complex system involving mental and physical health, meaning a dependant relationship between its organs and lifestyle. The elements used in the design of these prostheses are critical and related to lower limb amputation level, user morphology and human-prosthetic interaction. Hence, several technologies have been employed to accomplish the end user's needs, for example, advanced materials, control systems, electronics, energy management, signal processing, and artificial intelligence. This paper presents a systematic literature review on such technologies, to identify the latest advances, challenges, and opportunities in developing lower limb prostheses with the analysis on the most significant papers. Powered prostheses for walking in different terrains were illustrated and examined, with the kind of movement the device should perform by considering the electronics, automatic control, and energy efficiency. Results show a lack of a specific and generalised structure to be followed by new developments, gaps in energy management and improved smoother patient interaction. Additionally, Human Prosthetic Interaction (HPI) is a term introduced in this paper since no other research has integrated this interaction in communication between the artificial limb and the end-user. The main goal of this paper is to provide, with the found evidence, a set of steps and components to be followed by new researchers and experts looking to improve knowledge in this field.

Recent advancements in digital health management using multi-modal signal monitoring

Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term "Internet of Medical Things" (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.

Design, fabrication and experiments of a hydraulic active-passive hybrid prosthesis knee

BACKGROUND

Due to low friction, passive mechanical prostheses move compliantly followed by the stump and are used widely. Advanced semi-active prostheses can both move passively like passive prostheses and provide active torque under specific conditions. However, the current mechanical-hydraulic coupling driven semi-active prostheses, in order to meet the low passive friction requirements with a low active transmission ratio, lead to a significant problem of insufficient active torque.

OBJECTIVE

A hybrid active and passive prosthesis was developed to solve the incompatibility problem of low passive friction and high active driving torque of semi-active prostheses.

METHODS

The mechanical structure and control strategy of the prosthesis were demonstrated. The performance of the prosthesis was tested by bench and human tests.

RESULTS

Passive subsystem damping adjustment ranges from 0.4 N⋅(mm/s)-1 to 300 N⋅(mm/s)-1. The switching time between the damping and the active subsystem is 32 ± 2 ms. The continuous active torque output is more than 24 Nm. In level walking, the peak torque is about 28 Nm.

CONCLUSION

The proposed active-passive hybrid hydraulic prosthesis could satisfy both low passive friction and high active actuation.

Human-prosthesis cooperation: combining adaptive prosthesis control with visual feedback guided gait

BACKGROUND

Personalizing prosthesis control is often structured as human-in-the-loop optimization. However, gait performance is influenced by both human control and intelligent prosthesis control. Hence, we need to consider both human and prosthesis control, and their cooperation, to achieve desired gait patterns. In this study, we developed a novel paradigm that engages human gait control via user-fed visual feedback (FB) of stance time to cooperate with automatic prosthesis control tuning. Three initial questions were studied: (1) does user control of gait timing (via visual FB) help the prosthesis tuning algorithm to converge faster? (2) in turn, does the prosthesis control influence the user's ability to reach and maintain the target stance time defined by the feedback? and (3) does the prosthesis control parameters tuned with extended stance time on prosthesis side allow the user to maintain this potentially beneficial behavior even after feedback is removed (short- and long-term retention)?

METHODS

A reinforcement learning algorithm was used to achieve prosthesis control to meet normative knee kinematics in walking. A visual FB system cued the user to control prosthesis-side stance time to facilitate the prosthesis tuning goal. Seven individuals without amputation (AB) and four individuals with transfemoral amputation (TFA) walked with a powered knee prosthesis on a treadmill. Participants completed prosthesis auto-tuning with three visual feedback conditions: no FB, self-selected stance time FB (SS FB), and increased stance time FB (Inc FB). The retention of FB effects was studied by comparing the gait performance across three different prosthesis controls, tuned with different visual FB.

RESULTS

(1) Human control of gait timing reduced the tuning duration in individuals without amputation, but not for individuals with TFA. (2) The change of prosthesis control did not influence users' ability to reach and maintain the visual FB goal. (3) All participants increased their prosthesis-side stance time with the feedback and maintain it right after feedback was removed. However, in the post-test, the prosthesis control parameters tuned with visual FB only supported a few participants with longer stance time and better stance time symmetry.

CONCLUSIONS

The study provides novel insights on human-prosthesis interaction when cooperating in walking, which may guide the future successful adoption of this paradigm in prosthesis control personalization or human-in-the-loop optimization to improve the prosthesis user's gait performance.

A lightweight robotic leg prosthesis replicating the biomechanics of the knee, ankle, and toe joint

Robotic leg prostheses promise to improve the mobility and quality of life of millions of individuals with lower-limb amputations by imitating the biomechanics of the missing biological leg. Unfortunately, existing powered prostheses are much heavier and bigger and have shorter battery life than conventional passive prostheses, severely limiting their clinical viability and utility in the daily life of amputees. Here, we present a robotic leg prosthesis that replicates the key biomechanical functions of the biological knee, ankle, and toe in the sagittal plane while matching the weight, size, and battery life of conventional microprocessor-controlled prostheses. The powered knee joint uses a unique torque-sensitive mechanism combining the benefits of elastic actuators with that of variable transmissions. A single actuator powers the ankle and toe joints through a compliant, underactuated mechanism. Because the biological toe dissipates energy while the biological ankle injects energy into the gait cycle, this underactuated system regenerates substantial mechanical energy and replicates the key biomechanical functions of the ankle/foot complex during walking. A compact prosthesis frame encloses all mechanical and electrical components for increased robustness and efficiency. Preclinical tests with three individuals with above-knee amputation show that the proposed robotic leg prosthesis allows for common ambulation activities with close to normative kinematics and kinetics. Using an optional passive mode, users can walk on level ground indefinitely without charging the battery, which has not been shown with any other powered or microprocessor-controlled prostheses. A prosthesis with these characteristics has the potential to improve real-world mobility in individuals with above-knee amputation.

Powered knee and ankle prostheses enable natural ambulation on level ground and stairs for individuals with bilateral above-knee amputation: a case study

Ambulation with existing prostheses is extremely difficult for individuals with bilateral above-knee amputations. Commonly prescribed prostheses are passive devices that cannot replace the biomechanical functions of the missing biological legs. As a result, most individuals with bilateral above-knee amputations can only walk for short distances, have a high risk of falling, and are unable to ascend stairs with a natural gait pattern. Powered prostheses have the potential to address this issue by replicating the movements of the biological leg. Previous studies with individuals with bilateral above-knee amputations have shown that walking with powered prostheses is possible. However, stair ambulation requires different kinematics, kinetics, and power than walking. Therefore, it is not known whether powered prostheses can restore natural ambulation on stairs for bilateral above knee individuals. Here we show a case study with an individual with bilateral above-knee amputations using a pair of lightweight powered knee and ankle prostheses for walking and stair ambulation. The kinematic analysis shows that powered prostheses can restore natural leg movements, enabling the individual to walk and climb stairs using different gait patterns, such as step-over-step or step-by-step, one step or two steps at a time. The kinetic analysis shows that the powered prostheses can restore natural ankle push-off in walking and positive knee power generation in stair ascent, which are fundamental biomechanical functions of the missing biological legs. This case study is a first step towards enhancing functional mobility and quality of life for individuals with bilateral above-knee amputations through powered knee and ankle prostheses.

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.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

Toward Developing a Powered Ankle-Foot Prosthesis With Electromyographic Control to Enhance Functional Performance: A Case Study in a U.S. Service Member

The only commercially available ankle-foot prosthesis with powered propulsion lacks ruggedization and other capabilities for service members seeking to return to duty and/or other physically demanding activities. Here, we evaluated a ruggedized powered ankle-foot prosthesis with electromyographic control ("Warrior Ankle"; WA) in an experienced male user of the predicate (Empower) prosthesis. The participant (age = 56 years, mass = 86.8 kg, stature = 173 cm) completed a 650 m simulated hike with varying terrain at a fixed, self-selected speed in the WA and predicate prosthesis, with and without a 22.8 kg weighted vest ("loaded" and "unloaded," respectively). Peak dorsiflexion and plantarflexion angles were extracted from each gait cycle throughout the simulated hike (∼500 prosthetic-side steps). The participant walked faster with the WA (1.15 m/s) compared to predicate (0.80 m/s) prosthesis. On the prosthetic side, peak dorsiflexion angles were larger for the WA (loaded: 27.9°; unloaded: 26.9°) compared to the predicate (loaded: 19.4°; unloaded: 21.3°); peak plantarflexion angles were similar between prostheses and loading conditions [WA (loaded: 15.5°; unloaded: 14.9°), predicate (loaded: 16.9°; unloaded: 14.8°). The WA better accommodated the varying terrain profile, evidenced by greater peak dorsiflexion angles, as well as dorsiflexion and plantarflexion angles that more closely matched or exceeded those of the innate ankle [dorsiflexion (WA: 31.6°, predicate: 27.5°); plantarflexion (WA: 20.7°, predicate: 20.5°)]. Furthermore, the WA facilitated a faster walking speed, suggesting a greater functional capacity with the WA prosthesis. Although further design enhancements are needed, this case study demonstrated feasibility of a proof-of-concept, ruggedized powered ankle-foot prosthesis with electromyographic control.

Performance of Sonomyographic and Electromyographic Sensing for Continuous Estimation of Joint Torque During Ambulation on Multiple Terrains

Advances in powered assistive device technology, including the ability to provide net mechanical power to multiple joints within a single device, have the potential to dramatically improve the mobility and restore independence to their users. However, these devices rely on the ability of their users to continuously control multiple powered lower-limb joints simultaneously. Success of such approaches rely on robust sensing of user intent and accurate mapping to device control parameters. Here, we compare two non-invasive sensing modalities: surface electromyography and sonomyography, (i.e., ultrasound imaging of skeletal muscle), as inputs to Gaussian process regression models trained to estimate hip, knee and ankle joint moments during varying forms of ambulation. Experiments were performed with ten non-disabled individuals instrumented with surface electromyography and sonomyography sensors while completing trials of level, incline (10°) and decline (10°) walking. Results suggest sonomyography of muscles on the anterior and posterior thigh can be used to estimate hip, knee and ankle joint moments more accurately than surface electromyography. Furthermore, these results can be achieved by training Gaussian process regression models in a task-independent manner; i.e., incorporating features of level and ramp walking within the same predictive framework. These findings support the integration of sonomyographic and electromyographic sensing within powered assistive devices to continuously control joint torque.

Taking Both Sides: Seeking Symbiosis Between Intelligent Prostheses and Human Motor Control during Locomotion

Robotic lower-limb prostheses aim to replicate the power-generating capability of biological joints during locomotion to empower individuals with lower-limb loss. However, recent clinical trials have not demonstrated clear advantages of these devices over traditional passive devices. We believe this is partly because the current designs of robotic prothesis controllers and clinical methods for fitting and training individuals to use them do not ensure good coordination between the prosthesis and user. Accordingly, we advocate for new holistic approaches in which human motor control and intelligent prosthesis control function as one system (defined as human-prosthesis symbiosis). We hope engineers and clinicians will work closely to achieve this symbiosis, thereby improving the functionality and acceptance of robotic prostheses and users' quality of life.

Phase-Variable Control of a Powered Knee-Ankle Prosthesis over Continuously Varying Speeds and Inclines

Most controllers for lower-limb robotic prostheses require individually tuned parameter sets for every combination of speed and incline that the device is designed for. Because ambulation occurs over a continuum of speeds and inclines, this design paradigm requires tuning of a potentially prohibitively large number of parameters. This limitation motivates an alternative control framework that enables walking over a range of speeds and inclines while requiring only a limited number of tunable parameters. In this work, we present the implementation of a continuously varying kinematic controller on a custom powered knee-ankle prosthesis. The controller uses a phase variable derived from the residual thigh angle, along with real-time estimates of ground inclination and walking speed, to compute the appropriate knee and ankle joint angles from a continuous model of able-bodied kinematic data. We modify an existing phase variable architecture to allow for changes in speeds and inclines, quantify the closed-loop accuracy of the speed and incline estimation algorithms for various references, and experimentally validate the controller by observing that it replicates kinematic trends seen in able-bodied gait as speed and incline vary.

Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review

The lower-limb robotic prostheses can provide assistance for amputees' daily activities by restoring the biomechanical functions of missing limb(s). To set proper control strategies and develop the corresponding controller for robotic prosthesis, a prosthesis user's intent must be acquired in time, which is still a major challenge and has attracted intensive attentions. This work focuses on the robotic prosthesis user's locomotion intent recognition based on the noninvasive sensing methods from the recognition task perspective (locomotion mode recognition, gait event detection, and continuous gait phase estimation) and reviews the state-of-the-art intent recognition techniques in a lower-limb prosthesis scope. The current research status, including recognition approach, progress, challenges, and future prospects in the human's intent recognition, has been reviewed. In particular for the recognition approach, the paper analyzes the recent studies and discusses the role of each element in locomotion intent recognition. This work summarizes the existing research results and problems and contributes a general framework for the intent recognition based on lower-limb prosthesis.

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.

Ultrasound Sensing Can Improve Continuous Classification of Discrete Ambulation Modes Compared to Surface Electromyography

Clinical translation of "intelligent" lower-limb assistive technologies relies on robust control interfaces capable of accurately detecting user intent. To date, mechanical sensors and surface electromyography (EMG) have been the primary sensing modalities used to classify ambulation. Ultrasound (US) imaging can be used to detect user-intent by characterizing structural changes of muscle. Our study evaluates wearable US imaging as a new sensing modality for continuous classification of five discrete ambulation modes: level, incline, decline, stair ascent, and stair descent ambulation, and benchmarks performance relative to EMG sensing. Ten able-bodied subjects were equipped with a wearable US scanner and eight unilateral EMG sensors. Time-intensity features were recorded from US images of three thigh muscles. Features from sliding windows of EMG signals were analyzed in two configurations: one including 5 EMG sensors on muscles around the thigh, and another with 3 additional sensors placed on the shank. Linear discriminate analysis was implemented to continuously classify these phase-dependent features of each sensing modality as one of five ambulation modes. US-based sensing statistically improved mean classification accuracy to 99.8% (99.5-100% CI) compared to 8-EMG sensors (85.8%; 84.0-87.6% CI) and 5-EMG sensors (75.3%; 74.5-76.1% CI). Further, separability analyses show the importance of superficial and deep US information for stair classification relative to other modes. These results are the first to demonstrate the ability of US-based sensing to classify discrete ambulation modes, highlighting the potential for improved assistive device control using less widespread, less superficial and higher resolution sensing of skeletal muscle.