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

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

Application of Artificial Intelligence (AI) in Prosthetic and Orthotic Rehabilitation

Technological integration of Artificial Intelligence (AI) and machine learning in the Prosthetic and Orthotic industry and in the field of assistive technology has become boon for the Persons with Disabilities. The concept of neural network has been used by the leading manufacturers of rehabilitation aids for simulating various anatomical and biomechanical functions of the lost parts of the human body. The involvement of human interaction with various agents’ i.e. electronic circuitry, software, robotics, etc. has made a revolutionary impact in the rehabilitation field to develop devices like Bionic leg, mind or thought control prosthesis and exoskeletons. Application of Artificial Intelligence and robotics technology has a huge impact in achieving independent mobility and enhances the quality of life in Persons with Disabilities (PwDs)

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

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

Optimizing User Integration for Individualized Rehabilitation

User integration with assistive devices or rehabilitation protocols to improve movement function is a key principle to consider for developers to truly optimize performance gains. Better integration may entail customizing operation of devices and training programs according to several user characteristics during execution of functional tasks. These characteristics may be physical dimensions, residual capabilities, restored sensory feedback, cognitive perception, or stereotypical actions

Using Artificial Intelligence To Improve The Control Of Prosthetic Legs

For as long as people have been able to survive limb threatening injuries prostheses have been created. Modern lower limb prostheses are primarily controlled by adjusting the amount of damping in the knee to bend in a suitable manner for walking and running. Often the choice of walking state or running state has to be controlled manually by pressing a button. While this simple tuning strategy can work for many users it can be limiting and there is the tendency that controlling the leg is not intuitive and the wearer has to learn how to use leg. This thesis examines how this control can be improved using Artificial Intelligence (AI) to allow the system to be tuned for each individual. A wearable gait lab was developed consisting of a number of sensors attached to the limbs of eight volunteers. The signals from the sensors were analysed and features were extracted from them which were then passed through 2 separate Artificial Neural Networks (ANN). One network attempted to classify whether the wearer was standing still, walking or running. The other network attempted to estimate the wearer’s movement speed. A Genetic Algorithm (GA) was used to tune the ANNs parameters for each individual. The results showed that each individual needed different parameters to tune the features presented to the ANN. It was also found that different features were needed for each of the two problems presented to the ANN. Two new features are presented which identify the movement states of standing, walking and running and the movement speed of the volunteer. The results suggest that the control of the prosthetic limb can be improved