Proportional EMG Control of Ankle Plantar Flexion in a Powered Transtibial Prosthesis

The human calf muscle generates 80% of the mechanical work to walk throughout stance-phase, powered plantar flexion. Powered plantar flexion is not only important for walking energetics, but also to minimize the impact on the leading leg at heel-strike. For unilateral transtibial amputees, it has recently been shown that knee load on the leading, intact limb decreases as powered plantar flexion in the trailing prosthetic ankle increases. Not surprisingly, excessive loads on the leading, intact knee are believed to be causative of knee osteoarthritis, a leading secondary impairment in lowerextremity amputees. In this study, we hypothesize that a transtibial amputee can learn how to control a powered anklefoot prosthesis using a volitional electromyographic (EMG) control to directly modulate ankle powered plantar flexion. We here present preliminary data, and find that an amputee participant is able to modulate toe-off angle, net ankle work and peak power across a broad range of walking speeds by volitionally modulating calf EMG activity. The modulation of these key gait parameters is shown to be comparable to the dynamical response of the same powered prosthesis controlled intrinsically (No EMG), suggesting that transtibial amputees can achieve an adequate level of powered plantar flexion controllability using direct volitional EMG control.United States. Dept. of Defense (award number 6920559)United States. Dept. of Defense (award number 6920877)Swiss National Science Foundation (grant PBELP3_140656

Continuous Proportional Myoelectric Control of an Experimental Powered Lower Limb Prosthesis During Walking Using Residual Muscles.

Current robotic lower limb prostheses rely on intrinsic sensing and finite state machines to control ankle mechanics during walking. State-based controllers are suitable for stereotypical cyclic locomotor tasks (e.g. walking on level ground) where joint mechanics are well defined at specific gait phases (i.e. states) and state transitions are easily detected. However, state-based controllers are not ideal for non-stereotypical acyclic tasks (e.g. freestyle dancing) where joint mechanics cannot be predefined and transitions are unpredictable. An alternative to state-based control is to utilize the amputee's nervous system for myoelectric control. A robotic lower limb prosthesis that uses continuous proportional myoelectric control would allow the amputee to adapt their ankle mechanics freely. One potential source for myoelectric control is the amputee’s residual muscles. I conducted four studies to examine the feasibility of using residual muscles for continuous myoelectric control during walking. In my first study, I demonstrated that it is possible to record residual electromyography from amputees during walking that are viable for continuous myoelectric control. My results showed that the stride-to-stride variability of residual and intact muscle activation patterns was similar. However, residual muscle activation patterns were significantly different across amputee subjects and significantly different than corresponding muscles in intact subjects. In my second study, I built and tested an experimental powered transtibial prosthesis and demonstrated that an amputee subject was able to walk using continuous proportional myoelectric control to alter prosthetic ankle mechanics. In my third study, I showed that five amputee subjects were able to adapt their residual muscles to walk using continuous proportional myoelectric control. With visual feedback of their control signal, amputees were able to generate higher peak ankle power walking with the experimental powered prosthesis compared to their prescribed prosthesis. In my fourth study, I conducted a user experience study and found that despite challenges with the device user interface, walking with continuous proportional myoelectric control gave amputees a sense of empowerment and embodiment. The results of my studies demonstrated the advantages and disadvantages of using continuous proportional myoelectric control for a powered transtibial prosthesis and suggest how next generation prostheses can build upon these findings.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110412/1/shuangz_1.pd

GAIT PERFORMANCE AND CONTROL OF A PROSTHETIC ANKLE JOINT FOR BELOW-KNEE AMPUTEES

Traumatic events such as accidents or vascular and circulatory disorders often lead to amputation of the lower limb below the knee joint. The surgery is followed by fitting of a prosthetic device and rehabilitation process to help the individual recover mobility. The recovered gait of the individual depends to a large extent on his/her health, the amputation technique, and the functional level of the prosthesis. Prior research in amputee gait has focused mostly on assessing gait symmetry, movement of the healthy joints, activities of the unaffected muscles, and the metabolic energy consumption in individuals who had undergone traditional amputation. Very little research has been carried out on the performance of individuals with non-traditional amputation procedures designed to maximize the ability of the residual limb to support body weight at the extremity and to maintain the ability of the affected muscles. Moreover, majority of the studies were limited to gait tests in laboratory environments which restricted the mobility of the individuals. Current ankle/foot prostheses for people with below-knee amputation are primarily passive devices whose performance cannot be adapted or optimized to meet the requirements of different users. The adverse consequences of wearing poorly functioning prosthetic feet include asymmetric gait, increased metabolic consumption, limited blood flow, instability, and pain. Over the long term, the amputees, especially ones with diabetes, might have to undergo hip replacement procedure and use wheel-chair on a daily basis. There exists a high and increasing demand for an advanced prosthetic foot that is comfortable and able to replicate the function of the biological foot. Some of the factors hindering the development and performance validation of such an actively controlled foot are the lack of complete understanding of the gait, the interaction between the residual limb and the controller, presence of human in the control loop, unknown interaction between the terrain and the foot, and stringent requirements on the mechanical power and rigidity of the foot. This dissertation aims to address these shortcomings in a systematic fashion in order to develop an intelligent ankle/foot prosthesis system. The following are the key steps in the process adopted in this dissertation. • First, a gait monitoring device and algorithms for gait analysis will be developed to study the gait of people with below-knee amputation in real time during work-related activities. Experimental protocols are then designed to collect gait data from individuals with below-knee amputation in order to understand the activity of the residual muscles and the ability of the prosthetic device to support body weight during gait. • The dependence of the interfacial socket forces and electromyography signals from the muscles in the residual limb on the type of the gait and gait-related events will then be studied. The use of this dependence to recognize user gait and the corresponding ankle displacement pattern for the controlled prosthetic foot will be investigated. • Finally, hierarchical learning-based control strategies will be developed to adaptively compensate for the unknown, changing ankle dynamics and drive the prosthetic ankle joint along the desired trajectories. It is anticipated that the learning capabilities of these control strategies will enable the prosthetic ankle joint to not only replicate the movement of the healthy ankle, but also improve the stability of the gait and optimize the performance. The above approaches are demonstrated in this dissertation in two parts. The analysis of the gait of a group of otherwise healthy men with non-traditional amputation technique called transtibial osteomyoplastic amputation (TOA) is considered in the first part of the dissertation. The TOA procedure is prescribed for healthy, young individuals who desire a very active lifestyle. TOA offers stable bony residuum capable of bearing the weight of the individual and residual muscles that are active throughout the gait cycle. The gait study carried out in this dissertation is shown to confirm loading at the distal end-bearing area of the residual limb and active contraction of the residual muscles below the knee during gait of all participants. The interfacial forces in the socket and the activity of the residual muscles in subjects with TOA are shown to be related to and dependent on the type of gait, as well as the type of prosthetic feet used. In addition, the potential of residuum socket interface forces in recognition of the gait is also demonstrated. Learning-based control of the prosthetic ankle joint is addressed in the second part of the dissertation. Two hierarchical learning-based control algorithms that take into account the ankle dynamics, foot-ground interaction, and the movement of upper body are considered. The first strategy uses an artificial neural network-based feedback linearization controller to learn the unknown and changing dynamics of the ankle joint and to track a desired ankle displacement profile. In the second strategy, a neural dynamic programming-based controller that can track an ankle displacement profile while optimizing a cost function based on the tracking error is considered. Actual gait data obtained from the subjects in the first part of this dissertation is used to study the effectiveness of the control strategy. For the first time, an adaptive controller has been demonstrated that can address changes in terrain and in user requirements to provide consistent and stable functioning of the prosthetic ankle. It is anticipated that the strategy developed in this dissertation will help build an intelligent prosthetic foot that can significantly improve the mobility and long-term health of people with amputation of the lower limb. Keyword: Gait Analysis, Prosthetic Foot, Intelligent Contro

GAIT ANALYSIS OF PEOPLE WITH TRANSTIBIAL AMPUTATION

Causes of transtibial (below knee) amputation vary from traumatic events such as accidents to vascular and circulatory disorders. Following amputation, the individual is fitted with a prosthetic device in order to regain mobility. However, the extent of recovery depends on factors such as prosthetic socket fit, functionality of the prosthetic device, the type of amputation and the individual’s health. Prior research was undertaken to study the asymmetry in gait, energy consumption and muscle activity in intact muscles of traditional amputees. Recently, research into residual muscle activity and residuum socket interface force in amputees with transtibial osteomyoplastic amputation has been undertaken. However, very little research has been undertaken to compare the residual muscle activation in unilateral transtibial traditional amputees and in those with unilateral transtibial osteomyoplastic amputation. Furthermore, the correlation between residual muscle activation, residuum socket interface force and the type of gait in individuals with unilateral transtibial osteomyoplastic amputation has never been studied. Study of residuum socket interface forces and residual muscle activity is important because these two parameters have been linked to factors that increase the risk of injury to the residual limb. Furthermore, study of residuum socket interface force helps a prosthetist better understand the loading in the prosthetic socket during gait and helps in achieving better prosthetic socket fit. Study of residual muscle activity patterns also helps in determining the effect of amputation on residual muscles and in turn on the health of the individual. Transtibial osteomyoplastic amputation is known to provide the amputees with distal end weight bearing and restoring the length tension relationship in the residual muscles and has been verified in the previous studies. However, a comparison between results observed after transtibial traditional amputation and transtibial osteomyoplastic amputation have never been studied. Such comparative studies provides insight into the type of prosthetic design that best suits each type of amputation and also provides a better understanding of advantages and disadvantages of each type of amputation. Gait is an important factor that affects residuum socket interface force and residual muscle activation. Study of the dependence of both these factors on the type of gait is of importance to develop better rehabilitation techniques and improve the performance of prosthetic devices. In this thesis, the residual muscle activation in transtibial osteomyoplastic amputees and transtibial traditional amputees is looked at. For this purpose, 10 transtibial osteomyoplastic amputees and 4 transtibial traditional amputees were recruited for the study. This would help in realizing the effect of amputation on residual muscle activity and lays the foundation for understanding the effect of amputation on residual limb health. We expect to see considerable residual muscle activity in the transtibial osteomyoplastic amputees and minimal or random muscle activity in transtibial traditional amputation. Furthermore, the co-relation between residuum socket interface (RSI) force and EMG to the type of gait is studied in transtibial osteomyoplastic amputees. For this purpose, 10 transtibial osteomyoplastic amputees were recruited for this study. We expect to see increasing RSI force and EMG activity while comparing self-selected gait, brisk gait and weight carry gait. This would help establish a co-relation between RSI force and EMG activity laying foundation in developing better rehabilitation techniques and prosthetic devices

ESTIMATION OF MULTI-DIRECTIONAL ANKLE IMPEDANCE AS A FUNCTION OF LOWER EXTREMITY MUSCLE ACTIVATION

The purpose of this research is to investigate the relationship between the mechanical impedance of the human ankle and the corresponding lower extremity muscle activity. Three experimental studies were performed to measure the ankle impedance about multiple degrees of freedom (DOF), while the ankle was subjected to different loading conditions and different levels of muscle activity. The first study determined the non-loaded ankle impedance in the sagittal, frontal, and transverse anatomical planes while the ankle was suspended above the ground. The subjects actively co-contracted their agonist and antagonistic muscles to various levels, measured using electromyography (EMG). An Artificial Neural Network (ANN) was implemented to characterize the relationship between the EMG and non-loaded ankle impedance in 3-DOF. The next two studies determined the ankle impedance and muscle activity during standing, while the foot and ankle were subjected to ground perturbations in the sagittal and frontal planes. These studies investigate the performance of subject-dependent models, aggregated models, and the feasibility of a generic, subject-independent model to predict ankle impedance based on the muscle activity of any person. Several regression models, including Least Square, Support Vector Machine, Gaussian Process Regression, and ANN, and EMG feature extraction techniques were explored. The resulting subject-dependent and aggregated models were able to predict ankle impedance with reasonable accuracy. Furthermore, preliminary efforts toward a subject-independent model showed promising results for the design of an EMG-impedance model that can predict ankle impedance using new subjects. This work contributes to understanding the relationship between the lower extremity muscles and the mechanical impedance of the ankle in multiple DOF. Applications of this work could be used to improve user intent recognition for the control of active ankle-foot prostheses

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

A model of muscle-tendon function in human walking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-113).In order to motivate the design of legged machines that walk as humans do, this thesis investigates how leg muscles and tendons work mechanically during level-ground human walking at self-selected speeds. I hypothesize that quasi- passive, series-elastic clutch units spanning the knee joint in a musculoskeletal arrangement can capture the dominant mechanical behaviors of the human knee in level-ground walking. As a preliminary evaluation of this hypothesis, I develop an under-actuated model of the human leg in walking where each muscle-tendon unit spanning the knee joint is represented as a simple linear spring in series with a clutch. I vary model parameters, or spring constants and clutch engagement times, using an optimization scheme that minimizes ankle and hip actuator work while still maintaining human-like knee mechanics. For model evaluation, kinetic and kinematic gait data are employed from nine participants walking across a level-ground surface at self-selected gait speeds. With this under-actuated leg model, I find good agreement between model quasi-passive knee torque and experimental knee values, suggesting that a knee actuator is not necessary for level-ground robotic ambulation at self-selected gait speeds. As a further evaluation of the hypothesis of spring-like muscle-tendon behavior about the knee joint, a forward dynamics control scheme for the under-actuated model is developed. Hill-type muscle models are employed to model the ankle soleus and hip monoarticular muscles. Further, the model's series-elastic clutches are engaged with a simple state machine based on electromyography (EMG) data from the literature. Muscles are controlled with simple feedback controls representing the reflexive architecture of the human neuromuscular system. Following an optimization procedure, the model is shown to predict joint and muscle biomechanics, as well as the metabolism of walking humans, supporting the idea that muscle-tendon units spanning the human knee joint mainly operate as spring elements during neural activation, affording the relatively high metabolic walking economy of humans.by Ken Endo.Ph.D

Sensor-Based Adaptive Control and Optimization of Lower-Limb Prosthesis.

Recent developments in prosthetics have enabled the development of powered prosthetic ankles (PPA). The advent of such technologies drastically improved impaired gait by increasing balance and reducing metabolic energy consumption by providing net positive power. However, control challenges limit performance and feasibility of today’s devices. With addition of sensors and motors, PPA systems should continuously make control decisions and adapt the system by manipulating control parameters of the prostheses. There are multiple challenges in optimization and control of PPAs. A prominent challenge is the objective setup of the system and calibration parameters to fit each subject. Another is whether it is possible to detect changes in intention and terrain before prosthetic use and how the system should react and adapt to it. In the first part of this study, a model for energy expenditure was proposed using electromyogram (EMG) signals from the residual lower-limbs PPA users. The proposed model was optimized to minimize energy expenditure. Optimization was performed using a modified Nelder-Mead approach with a Latin Hypercube sampling. Results of the proposed method were compared to expert values and it was shown to be a feasible alternative for tuning in a shorter time. In the second part of the study, the control challenges regarding lack of adaptivity for PPAs was investigated. The current PPA system used is enhanced with impedance-controlled parameters that allow the system to provide different assistance. However, current systems are set to a fixed value and fail to acknowledge various terrain and intentions throughout the day. In this study, a pseudo-real-time adaptive control system was proposed to predict the changes in the gait and provide a smoother gait. The proposed control system used physiological, kinetic, and kinematic data and fused them to predict the change. The prediction was done using machine learning-based methods. Results of the study showed an accuracy of up to 89.7 percent for prediction of change for four different cases