EMG control of a bionic knee prosthesis: exploiting muscle co-contractions for improved locomotor function

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.

Intuitive and versatile bionic legs: a perspective on volitional control

Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.

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.

Electromyography-Based Control of Lower Limb Prostheses: A Systematic Review

Most amputations occur in lower limbs and despite improvements in prosthetic technology, no commercially available prosthetic leg uses electromyography (EMG) information as an input for control. Efforts to integrate EMG signals as part of the control strategy have increased in the last decade. In this systematic review, we summarize the research in the field of lower limb prosthetic control using EMG. Four different online databases were searched until June 2022: Web of Science, Scopus, PubMed, and Science Direct. We included articles that reported systems for controlling a prosthetic leg (with an ankle and/or knee actuator) by decoding gait intent using EMG signals alone or in combination with other sensors. A total of 1,331 papers were initially assessed and 121 were finally included in this systematic review. The literature showed that despite the burgeoning interest in research, controlling a leg prosthesis using EMG signals remains challenging. Specifically, regarding EMG signal quality and stability, electrode placement, prosthetic hardware, and control algorithms, all of which need to be more robust for everyday use. In the studies that were investigated, large variations were found between the control methodologies, type of research participant, recording protocols, assessments, and prosthetic hardware.

The use of nonnormalized surface EMG and feature inputs for LSTM-based powered ankle prosthesis control algorithm development

Advancements in instrumentation support improved powered ankle prostheses hardware development. However, control algorithms have limitations regarding number and type of sensors utilized and achieving autonomous adaptation, which is key to a natural ambulation. Surface electromyogram (sEMG) sensors are promising. With a minimized number of sEMG inputs an economic control algorithm can be developed, whereas limiting the use of lower leg muscles will provide a practical algorithm for both ankle disarticulation and transtibial amputation. To determine appropriate sensor combinations, a systematic assessment of the predictive success of variations of multiple sEMG inputs in estimating ankle position and moment has to conducted. More importantly, tackling the use of nonnormalized sEMG data in such algorithm development to overcome processing complexities in real-time is essential, but lacking. We used healthy population level walking data to (1) develop sagittal ankle position and moment predicting algorithms using nonnormalized sEMG, and (2) rank all muscle combinations based on success to determine economic and practical algorithms. Eight lower extremity muscles were studied as sEMG inputs to a long-short-term memory (LSTM) neural network architecture: tibialis anterior (TA), soleus (SO), medial gastrocnemius (MG), peroneus longus (PL), rectus femoris (RF), vastus medialis (VM), biceps femoris (BF) and gluteus maximus (GMax). Five features extracted from nonnormalized sEMG amplitudes were used: integrated EMG (IEMG), mean absolute value (MAV), Willison amplitude (WAMP), root mean square (RMS) and waveform length (WL). Muscle and feature combination variations were ranked using Pearson's correlation coefficient (r > 0.90 indicates successful correlations), the root-mean-square error and one-dimensional statistical parametric mapping between the original data and LSTM response. The results showed that IEMG+WL yields the best feature combination performance. The best performing variation was MG + RF + VM (r_position = 0.9099 and r_moment = 0.9707) whereas, PL (r_position = 0.9001, r_moment = 0.9703) and GMax+VM (r_position = 0.9010, r_moment = 0.9718) were distinguished as the economic and practical variations, respectively. The study established for the first time the use of nonnormalized sEMG in control algorithm development for level walking.

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.

EMG-driven control in lower limb prostheses: a topic-based systematic review

BACKGROUND

The inability of users to directly and intuitively control their state-of-the-art commercial prosthesis contributes to a low device acceptance rate. Since Electromyography (EMG)-based control has the potential to address those inabilities, research has flourished on investigating its incorporation in microprocessor-controlled lower limb prostheses (MLLPs). However, despite the proposed benefits of doing so, there is no clear explanation regarding the absence of a commercial product, in contrast to their upper limb counterparts.

OBJECTIVE AND METHODOLOGIES

This manuscript aims to provide a comparative overview of EMG-driven control methods for MLLPs, to identify their prospects and limitations, and to formulate suggestions on future research and development. This is done by systematically reviewing academical studies on EMG MLLPs. In particular, this review is structured by considering four major topics: (1) type of neuro-control, which discusses methods that allow the nervous system to control prosthetic devices through the muscles; (2) type of EMG-driven controllers, which defines the different classes of EMG controllers proposed in the literature; (3) type of neural input and processing, which describes how EMG-driven controllers are implemented; (4) type of performance assessment, which reports the performance of the current state of the art controllers.

RESULTS AND CONCLUSIONS

The obtained results show that the lack of quantitative and standardized measures hinders the possibility to analytically compare the performances of different EMG-driven controllers. In relation to this issue, the real efficacy of EMG-driven controllers for MLLPs have yet to be validated. Nevertheless, in anticipation of the development of a standardized approach for validating EMG MLLPs, the literature suggests that combining multiple neuro-controller types has the potential to develop a more seamless and reliable EMG-driven control. This solution has the promise to retain the high performance of the currently employed non-EMG-driven controllers for rhythmic activities such as walking, whilst improving the performance of volitional activities such as task switching or non-repetitive movements. Although EMG-driven controllers suffer from many drawbacks, such as high sensitivity to noise, recent progress in invasive neural interfaces for prosthetic control (bionics) will allow to build a more reliable connection between the user and the MLLPs. Therefore, advancements in powered MLLPs with integrated EMG-driven control have the potential to strongly reduce the effects of psychosomatic conditions and musculoskeletal degenerative pathologies that are currently affecting lower limb amputees.

Direct continuous electromyographic control of a powered prosthetic ankle for improved postural control after guided physical training: A case study

Despite the promise of powered lower limb prostheses, existing controllers do not assist many daily activities that require continuous control of prosthetic joints according to human states and environments. The objective of this case study was to investigate the feasibility of direct, continuous electromyographic (dEMG) control of a powered ankle prosthesis, combined with physical therapist-guided training, for improved standing postural control in an individual with transtibial amputation. Specifically, EMG signals of the residual antagonistic muscles (i.e. lateral gastrocnemius and tibialis anterior) were used to proportionally drive pneumatical artificial muscles to move a prosthetic ankle. Clinical-based activities were used in the training and evaluation protocol of the control paradigm. We quantified the EMG signals in the bilateral shank muscles as well as measures of postural control and stability. Compared to the participant's daily passive prosthesis, the dEMG-controlled ankle, combined with the training, yielded improved clinical balance scores and reduced compensation from intact joints. Cross-correlation coefficient of bilateral center of pressure excursions, a metric for quantifying standing postural control, increased to .83(±.07) when using dEMG ankle control (passive device: .39(±.29)). We observed synchronized activation of homologous muscles, rapid improvement in performance on the first day of the training for load transfer tasks, and further improvement in performance across training days (p = .006). This case study showed the feasibility of this dEMG control paradigm of a powered prosthetic ankle to assist postural control. This study lays the foundation for future study to extend these results through the inclusion of more participants and activities.

Myoelectric control of robotic lower limb prostheses: a review of electromyography interfaces, control paradigms, challenges and future directions

Objective.Advanced robotic lower limb prostheses are mainly controlled autonomously. Although the existing control can assist cyclic movements during locomotion of amputee users, the function of these modern devices is still limited due to the lack of neuromuscular control (i.e. control based on human efferent neural signals from the central nervous system to peripheral muscles for movement production). Neuromuscular control signals can be recorded from muscles, called electromyographic (EMG) or myoelectric signals. In fact, using EMG signals for robotic lower limb prostheses control has been an emerging research topic in the field for the past decade to address novel prosthesis functionality and adaptability to different environments and task contexts. The objective of this paper is to review robotic lower limb Prosthesis control via EMG signals recorded from residual muscles in individuals with lower limb amputations.Approach.We performed a literature review on surgical techniques for enhanced EMG interfaces, EMG sensors, decoding algorithms, and control paradigms for robotic lower limb prostheses.Main results.This review highlights the promise of EMG control for enabling new functionalities in robotic lower limb prostheses, as well as the existing challenges, knowledge gaps, and opportunities on this research topic from human motor control and clinical practice perspectives.Significance.This review may guide the future collaborations among researchers in neuromechanics, neural engineering, assistive technologies, and amputee clinics in order to build and translate true bionic lower limbs to individuals with lower limb amputations for improved motor function.

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.

Intent Prediction of Multi-axial Ankle Motion Using Limited EMG Signals

Background: In this study, different intent prediction strategies were explored with the objective of determining the best approach to predicting continuous multi-axial user motion based solely on surface EMG (electromyography) data. These strategies were explored as the first step to better facilitating control of a multi-axis transtibial powered prosthesis. Methods: Based on data acquired from gait experiments, different data sets, prediction approaches and classification algorithms were explored. The effect of varying EMG electrode positioning was also tested. EMG data measured from three lower leg muscles was the sole data type used for making intent predictions. The motions to be predicted were along both the sagittal plane (foot dorsiflexion and plantarflexion) and the frontal plane (foot eversion and inversion). Results: The deviation of EMG data from its optimal pattern led to a decrease in prediction accuracy of up to 23%. However, using features that were calculated based on a participant's specific walking pattern limited this loss of prediction accuracy as a result of EMG electrode placement. A decoupled data set, one wherein the terrain type was accounted for beforehand, yielded the highest intent prediction accuracy of 77.2%. Conclusions: The results of this study highlighted the challenges faced when using very limited EMG data to predict multi-axial ankle motion. They also indicated that approaches that are more user-centric by design could led to more accurate motion predictions, possibly enabling more intuitive control.

Online adaptive neural control of a robotic lower limb prosthesis

OBJECTIVE

The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis.

APPROACH

We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation.

MAIN RESULTS

Our proposed algorithm accurately and consistently identified the user's intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate.

SIGNIFICANCE

This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's intent with low error rates.

Wearable Ankle Robots in Post-stroke Rehabilitation of Gait: A Systematic Review

Background: Stroke causes weak functional mobility in survivors and affects the ability to perform activities of daily living. Wearable ankle robots are a potential intervention for gait rehabilitation post-stroke. Objective: The aim of this study is to provide a systematic review of wearable ankle robots, focusing on the overview, classification and comparison of actuators, gait event detection, control strategies, and performance evaluation. Method: Only English-language studies published from December 1995 to July 2018 were searched in the following databases: PubMed, EMBASE, Web of Science, Scopus, IEEE Xplore, Science Direct, SAGE journals. Result: A total of 48 articles were selected and 97 stroke survivors participated in these trials. Findings showed that few comparative trials were conducted among different actuators or control strategies. Moreover, mixed sensing technology which combines kinematic with kinetic information was effective in detecting motion intention of stroke survivors. Furthermore, all the selected clinical studies showed an improvement in the peak dorsiflexion degree of the swing phase, propulsion on the paretic side during push-off, and further enhanced walking speed after a period of robot-assisted ankle rehabilitation training. Conclusions: Preliminary findings suggest that wearable ankle robots have certain clinical benefits for the treatment of hemiplegic gait post-stroke. In the near future, a multicenter randomized controlled clinical trial is extremely necessary to enhance the clinical effectiveness of wearable ankle robots.

Proportional Myoelectric Control of a Virtual Inverted Pendulum Using Residual Antagonistic Muscles: Toward Voluntary Postural Control

This paper aims to investigate whether transtibial amputees are capable of coordinating the descending neural commands to antagonistic residual ankle muscles for performing dynamic tasks that require continuous, precise control. To achieve this goal, we developed a virtual inverted pendulum that was inherently unstable and mimicked human-like dynamics in a standing posture. Balancing this dynamic system requires continuous inputs, proportional to electromyography (EMG) magnitudes recorded from (residual) tibialis anterior (TA) and lateral gastrocnemius muscles (GAS), respectively. The six able-bodied and six transtibial amputees were recruited and asked to balance the inverted pendulum for ten 90-s trials. The results showed that the amputees were capable of controlling this unstable dynamic system with a proportional myoelectric control; however, they underperformed the able-bodied subjects, who maintained the pendulum closer to center ( p = 0.041 ). Compared to the performance in the initial two trials, amputees improved the performance by significantly reducing the number of pendulum falls ( p = 0.0329 ) and sway size ( p = 0.048 ) in the final two trials. However, the amount of improvement varied across amputee subjects. Amputee subjects demonstrated different task adaptation strategies, including reduction of erroneous residual muscle contractions, development of an appropriate state-action (pendulum state-EMG activation) relationship for the task, and/or reduction of muscle control variability with the improved task performance efficiency (i.e., increased inactivity and sway minimization). The results suggest that after the training of transtibial amputees in coordinating antagonistic residual muscles in dynamic systems, it may be feasible to implement the proportional myoelectric control of the powered ankle prostheses in order to assist the postural control mechanisms, such as anticipatory and compensatory postural adjustments.

A Comparison of Control Strategies in Commercial and Research Knee Prostheses

GOAL

To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs).

METHODS

Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well.

RESULTS

Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope.

CONCLUSION

The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori.

SIGNIFICANCE

This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.

Voluntary Control of Residual Antagonistic Muscles in Transtibial Amputees: Reciprocal Activation, Coactivation, and Implications for Direct Neural Control of Powered Lower Limb Prostheses

Residual ankle muscles (i.e., previously antagonistic ankle muscles) of transtibial amputees are a potential source for continuous feedforward control of powered ankle prostheses using proportional myoelectric control. The ability for transtibial amputees to use their residual ankle muscles for two control input degrees of freedom (i.e., two independent myoelectric control input sources) for direct neural control depends on the ability for amputees to generate varying magnitudes of reciprocal activation and coactivation using their residual ankle muscles, which is not well understood. In this paper, we aimed to fill this knowledge gap. We asked 12 transtibial amputees to control the 2-D movement of a computer cursor using continuous proportional myoelectric control via their residual plantar flexor and residual dorsiflexor muscles to define their reachable 2-D control input space. The x-y position of the computer cursor was directly proportional to the independent continuous myoelectric control signals from the residual lateral gastrocnemius (x-axis) and the residual tibialis anterior (y-axis) where the limits of each axis were 0%-100% maximum voluntary activation of the corresponding residual muscle. Our results show that the reachable control input space varied widely across amputee subjects ranging from 38% to 81% of the maximum possible control input space. The cumulative time for the amputee subjects to saturate their reachable control input space ranged from 1.95 to 6.85 min. The amputee subjects used different residual muscle activation patterns and coordination strategies to expand their reachable control input space depending on their ability to perform coactivation and reciprocal activation using their residual plantar flexor and dorsiflexor muscles. The future development of powered lower limb prostheses using direct continuous proportional myoelectric control via residual muscles (e.g., for direct voluntary control of prosthesis joint impedance) should consider how an amputee user's immediately accessible residual muscle activation patterns and reachable 2-D control input space may affect their learning and performance.

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.

Active lower limb prosthetics: a systematic review of design issues and solutions

This paper presents a review on design issues and solutions found in active lower limb prostheses. This review is based on a systematic literature search with a methodical search strategy. The search was carried out across four major technical databases and the retrieved records were screened for their relevance. A total of 21 different active prostheses, including 8 above-knee, 9 below-knee and 4 combined knee-ankle prostheses were identified. While an active prosthesis may help to restore the functional performance of an amputee, the requirements regarding the actuation unit as well as for the control system are high and the development becomes a challenging task. Regarding mechanical design and the actuation unit high force/torque delivery, high efficiency, low size and low weight are conflicting goals. The actuation principle and variable impedance actuators are discussed. The control system is paramount for a “natural functioning” of the prosthesis. The control system has to enable locomotion and should react to the amputee’s intent. For this, multi-level control approaches are reviewed.

Combining Vibrotactile Feedback with Volitional Myoelectric Control for Robotic Transtibial Prostheses

In recent years, the development of myoelectric control for robotic lower-limb prostheses makes it possible for amputee users to volitionally control prosthetic joints. However, the human-centered control loop is not closed due to the lack of sufficient feedback of prosthetic joint movement, and it may result in poor control performance. In this research, we propose a vibrotactile stimulation system to provide the feedback of ankle joint position, and validate the necessity of combining it with volitional myoelectric control to achieve improved control performance. The stimulation system is wearable and consists of six vibrators. Three of the vibrators are placed on the anterior side of the thigh and the other three on the posterior side of the thigh. To explore the potential of applying the proposed vibrotactile feedback system for prosthetic ankle control, eight able-bodied subjects and two transtibial amputee subjects (TT1 and TT2) were recruited in this research, and several experiments were designed to investigate subjects’ sensitivities to discrete and continuous vibration stimulations applied on the thigh. Then, we proposed a stimulation controller to produce different stimulation patterns according to current ankle angle. Amputee subjects were asked to control a virtual ankle displayed on the computer screen to reach different target ankle angles with a myoelectric controller, and control performances under different feedback conditions were compared. Experimental results indicated that subjects were more sensitive to stimulation position changes (identification accuracies were 96.39 ± 0.86, 91.11, and 93.89% for able-bodied subjects, TT1, and TT2, respectively) than stimulation amplitude changes (identification accuracies were 89.89 ± 2.40, 87.04, and 85.19% for able-bodied subjects, TT1, and TT2, respectively). Response times of able-bodied subjects, TT1, and TT2 to stimulation pattern changes were 0.47 ± 0.02 s, 0.53 s, and 0.48 s, respectively. Furthermore, for both TT1 and TT2, the absolute error of virtual ankle control reduced by about 50% with the addition of vibrotactile feedback. These results suggest that it is promising to apply the vibrotactile feedback system for the control of robotic transtibial prostheses.

SEMG signal compression based on two-dimensional techniques

Background

Recently, two-dimensional techniques have been successfully employed for compressing surface electromyographic (SEMG) records as images, through the use of image and video encoders. Such schemes usually provide specific compressors, which are tuned for SEMG data, or employ preprocessing techniques, before the two-dimensional encoding procedure, in order to provide a suitable data organization, whose correlations can be better exploited by off-the-shelf encoders. Besides preprocessing input matrices, one may also depart from those approaches and employ an adaptive framework, which is able to directly tackle SEMG signals reassembled as images.

Methods

This paper proposes a new two-dimensional approach for SEMG signal compression, which is based on a recurrent pattern matching algorithm called multidimensional multiscale parser (MMP). The mentioned encoder was modified, in order to efficiently work with SEMG signals and exploit their inherent redundancies. Moreover, a new preprocessing technique, named as segmentation by similarity (SbS), which has the potential to enhance the exploitation of intra- and intersegment correlations, is introduced, the percentage difference sorting (PDS) algorithm is employed, with different image compressors, and results with the high efficiency video coding (HEVC), H.264/AVC, and JPEG2000 encoders are presented.

Results

Experiments were carried out with real isometric and dynamic records, acquired in laboratory. Dynamic signals compressed with H.264/AVC and HEVC, when combined with preprocessing techniques, resulted in good percent root-mean-square difference \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× compression factor figures, for low and high compression factors, respectively. Besides, regarding isometric signals, the modified two-dimensional MMP algorithm outperformed state-of-the-art schemes, for low compression factors, the combination between SbS and HEVC proved to be competitive, for high compression factors, and JPEG2000, combined with PDS, provided good performance allied to low computational complexity, all in terms of percent root-mean-square difference \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× compression factor.

Conclusion

The proposed schemes are effective and, specifically, the modified MMP algorithm can be considered as an interesting alternative for isometric signals, regarding traditional SEMG encoders. Besides, the approach based on off-the-shelf image encoders has the potential of fast implementation and dissemination, given that many embedded systems may already have such encoders available, in the underlying hardware/software architecture.

Combining human volitional control with intrinsic controller on robotic prosthesis: A case study on adaptive slope walking

Affording lower-limb amputees the ability to volitionally control robotic prostheses can improve the adaptability to terrain changes as well as enhancing proprioception. However, it also increases amputees' conscious burdens for prosthesis control. Therefore, in this paper, we aim to propose a hybrid controller which combines human volitional control with the intrinsic controller on the robotic transtibial prosthesis, enabling the amputee actively controlling prosthesis with little conscious attention. In this preliminary study, a hybrid controller for adaptive slope walking was designed. A slope estimator was embedded in the intrinsic controller to estimate the ground slope of the previous step using signals measured by prosthetic sensors. And a myoelectric controller allows the amputee subject to convey slope changes to prosthetic controller by volitionally contract his residual muscles, whose electromyography signals were mapped to the slope increment. The hybrid controller combined these two results to obtain the estimated slope. One male transtibial amputee subject was recruited in this research. Experiment results showed that the intrinsic slope estimator produced satisfactory estimation results with an average absolute error of 0.70 ± 0.54 degrees. By adding amputee's volitional control, the hybrid controller is able to predict the upcoming slope changes.

Promise of using surface EMG signals to volitionally control ankle joint position for powered transtibial prostheses

Improving the intuitiveness of the interaction between human and machine is an important issue for powered lower-limb prosthesis control. In this research, we aimed to evaluate the potential of using surface electromyography (EMG) signals measured from transtibial amputees' residual muscles to directly control the position of prosthetic ankle. In this research, one transtibial amputee subject and five able-bodied subjects were recruited. They were asked to control a virtual ankle to reach different target positions. The amputee subject finished these tasks in an average time of 1.29 seconds for different target positions with the residual limb, which was comparable with that using the amputee's sound limb and those with able-bodied subjects' dominant legs. Due to human's strong adaptability, the amputee subject was able to adapt to the control model trained one day before or trained in a posture which was different from that during performing control tasks. These results validate the promise of using surface EMG signals to volitionally control powered transtibial prostheses.

Control strategies for active lower extremity prosthetics and orthotics: a review

: Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user.This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user's sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies.As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os--not as independent devices, but as actors within an ecosystem--is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers.Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use.The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user's neuromusculoskeletal system and are practical for use in locomotive ADL.

Within-socket myoelectric prediction of continuous ankle kinematics for control of a powered transtibial prosthesis

OBJECTIVE

Powered robotic prostheses create a need for natural-feeling user interfaces and robust control schemes. Here, we examined the ability of a nonlinear autoregressive model to continuously map the kinematics of a transtibial prosthesis and electromyographic (EMG) activity recorded within socket to the future estimates of the prosthetic ankle angle in three transtibial amputees.

APPROACH

Model performance was examined across subjects during level treadmill ambulation as a function of the size of the EMG sampling window and the temporal 'prediction' interval between the EMG/kinematic input and the model's estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval.

MAIN RESULT

Across subjects, deviations in the estimated ankle angle from the actual movement were robust to variations in the EMG sampling window and increased systematically with prediction interval. For prediction intervals up to 150 ms, the average error in the model estimate of ankle angle across the gait cycle was less than 6°. EMG contributions to the model prediction varied across subjects but were consistently localized to the transitions to/from single to double limb support and captured variations from the typical ankle kinematics during level walking.

SIGNIFICANCE

The use of an autoregressive modeling approach to continuously predict joint kinematics using natural residual muscle activity provides opportunities for direct (transparent) control of a prosthetic joint by the user. The model's predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response.

A powered prosthetic intervention for bilateral transfemoral amputees

This paper presents the design and validation of a control system for a pair of powered knee and ankle prostheses to be used as a prosthetic intervention for bilateral transfemoral amputees. The control system leverages communication between the prostheses for enhanced awareness and stability, along with power generation at the knee and ankle joints to better restore biomechanical functionality in level ground walking. The control methodology employed is a combination of an impedance-based framework for weight-bearing portions of gait and a trajectory-based approach for the nonweight-bearing portions. The control system was implemented on a pair of self-contained powered knee and ankle prostheses, and the ability of the prostheses and control approach to provide walking functionality was assessed in a set of experimental trials with a bilateral transfemoral amputee subject. Specifically, experimental data from these trials indicate that the powered prostheses and bilateral control architecture provide gait kinematics that reproduce healthy gait kinematics to a greater extent than the subject's daily-use passive prostheses.