Myoelectric forearm prostheses: State of the art from a user-centered perspective

User acceptance of myoelectric forearm prostheses is currently low. Awkward control, lack of feedback, and difficult training are cited as primary reasons. Recently, researchers have focused on exploiting the new possibilities offered by advancements in prosthetic technology. Alternatively, researchers could focus on prosthesis acceptance by developing functional requirements based on activities users are likely to perform. In this article, we describe the process of determining such requirements and then the application of these requirements to evaluating the state of the art in myoelectric forearm prosthesis research. As part of a needs assessment, a workshop was organized involving clinicians (representing end users), academics, and engineers. The resulting needs included an increased number of functions, lower reaction and execution times, and intuitiveness of both control and feedback systems. Reviewing the state of the art of research in the main prosthetic subsystems (electromyographic [EMG] sensing, control, and feedback) showed that modern research prototypes only partly fulfill the requirements. We found that focus should be on validating EMG-sensing results with patients, improving simultaneous control of wrist movements and grasps, deriving optimal parameters for force and position feedback, and taking into account the psychophysical aspects of feedback, such as intensity perception and spatial acuity

Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

In the recent years, muscle synergies have been utilised to provide simultaneous and proportional myoelectric control systems. All of the proposed synergy-based systems relies on matrix factorisation methods to extract the muscle synergies which is limited in terms of task-dimensionality. Here, we seek to demonstrate and discuss the potential of higher-order tensor decompositions as a framework to estimate muscle synergies for proportional myoelectric control. We proposed synergy-based myoelectric control model by utilising muscle synergies extracted by a novel \ac{ctd} technique. Our approach is compared with \ac{NMF} \ac{SNMF}, the current state-of-the-art matrix factorisation models for synergy-based myoelectric control systems. Synergies extracted from three techniques where used to estimate control signals for wrist's \ac{dof} through regression. The reconstructed control signals where evaluated by real glove data that capture the wrist's kinematics. The proposed \ac{ctd} model results was slightly better than matrix factorisation methods. The three models where compared against random generated synergies and all of them were able to reject the null hypothesis. This study provides demonstrate the use of higher-order tensor decomposition in proportional myoelectric control and highlight the potential applications and advantages of using higher-order tensor decomposition in muscle synergy extraction

New developments in prosthetic arm systems

Absence of an upper limb leads to severe impairments in everyday life, which can further influence the social and mental state. For these reasons, early developments in cosmetic and body-driven prostheses date some centuries ago, and they have been evolving ever since. Following the end of the Second World War, rapid developments in technology resulted in powered myoelectric hand prosthetics. In the years to come, these devices were common on the market, though they still suffered high user abandonment rates. The reasons for rejection were trifold - insufficient functionality of the hardware, fragile design, and cumbersome control. In the last decade, both academia and industry have reached major improvements concerning technical features of upper limb prosthetics and methods for their interfacing and control. Advanced robotic hands are offered by several vendors and research groups, with a variety of active and passive wrist options that can be articulated across several degrees of freedom. Nowadays, elbow joint designs include active solutions with different weight and power options. Control features are getting progressively more sophisticated, offering options for multiple sensor integration and multi-joint articulation. Latest developments in socket designs are capable of facilitating implantable and multiple surface electromyography sensors in both traditional and osseointegration-based systems. Novel surgical techniques in combination with modern, sophisticated hardware are enabling restoration of dexterous upper limb functionality. This article is aimed at reviewing the latest state of the upper limb prosthetic market, offering insights on the accompanying technologies and techniques. We also examine the capabilities and features of some of academia’s flagship solutions and methods

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 \u27prediction\u27 interval between the EMG/kinematic input and the model\u27s estimate of future ankle angle to characterize the trade-off between model error, sampling window and prediction interval. Main results. 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\u27s predictive capability could prove particularly useful for overcoming delays in signal processing and actuation of the prosthesis, providing a more biomimetic ankle response

Preliminary Investigation of Residual Limb Plantarflexion and Dorsiflexion Muscle Activity During Treadmill Walking for Trans-tibial Amputees

Background: Novel powered prosthetic ankles currently incorporate finite state control, using kinematic and kinetic sensors to differentiate stance and swing phases/sub-phases and control joint impedance and position or torque. For more intuitive control, myoelectric control of the ankle using the remnant residual limb dorsiflexors and plantarflexors, perhaps in concert with kinetic and kinematic sensors, may be possible. Objective: The specific research objective was to assess the feasibility of using myoelectric control of future active or powered prosthetic ankle joints for trans-tibial amputees. Study Design: The project involved human subject trials to determine whether current techniques of myoelectric control of upper extremity prostheses might be readily adapted for lower extremity prosthetic control. Methods: Gait analysis was conducted for three unilateral trans-tibial amputee subjects during ambulation on an instrumented split belt treadmill. Data included ankle plantarflexor and dorsiflexor activity for the residual limb, as well as lower limb kinematics and ground reaction forces and moments of both the sound and prosthetic limbs. Results: These data indicate that: 1) trans-tibial amputees retain some independent ankle plantarflexor and dorsiflexor muscle activity of their residual limb; 2) it is possible to position surface electromyographic electrodes within a trans-tibial socket that maintain contact during ambulation; 3) both the plantarflexors and dorsiflexors of the residual limb are active during gait; 4) plantarflexor and dorsiflexor activity is consistent during multiple gait cycles; and 5) with minimal training, trans-tibial amputees may be able to activate their plantarflexors during push-off. Conclusions: These observations demonstrate the potential for future myoelectric control of active prosthetic ankles. Clinical relevance This study demonstrated the feasibility of applying upper extremity prosthetic myoelectric signal acquisition, processing and control techniques to future myoelectric control of active prosthetic ankles for trans-tibial amputees

Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control

In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms(DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions