Mechanism State Matrices for Spatial Reconfigurable Mechanisms

This paper improves augmented mechanism state matrices by replacing joint code with screw system notation. The proposed substitution allows for a more specific description of the joints in the mechanism and the capability to describe both spatial and planar mechanisms. Examples are provided which elucidate the proposed approach

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

Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions

A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.e., transitions to/from staircase). Prediction performance was characterized as a function of the time between current EMG/angle/moment inputs and future angle/moment model predictions (prediction interval), the number of past EMG/angle/moment input values over time (sampling window), and the number of units in the network hidden layer that minimized error between experimentally measured values (targets) and model predictions of ankle angle and moment. Ankle angle and moment predictions were robust across ambulation conditions with root mean squared errors less than 1° and 0.04 Nm/kg, respectively, and cross-correlations (R2) greater than 0.99 for prediction intervals of 58 ms. Model predictions at critical points of trip-related fall risk fell within the variability of the ankle angle and moment targets (Benjamini-Hochberg adjusted p \u3e 0.065). EMG contribution to ankle angle and moment predictions occurred consistently across ambulation conditions and model outputs. EMG signals had the greatest impact on noncyclic regions of gait such as double limb support, transitions between terrains, and around plantarflexion and moment peaks. The use of natural muscle activation patterns to continuously predict variations in normal gait and the model’s predictive capabilities to counteract electromechanical inherent delays suggest that this approach could provide robust and intuitive user-driven real-time control of a wide variety of lower limb robotic devices, including active powered ankle-foot prostheses

Dynamic Model of a Weld Breaking Mechanism for Automatic Circuit Recloser Applications

Automatic circuit reclosers protect electrical distribution systems by breaking the circuit should the current levels exceed an acceptable range. In the process of opening and closing the circuit, welds are formed between the contacts, making it difficult to separate the contacts again. In order to ensure that the contacts can be separated in the event of a fault, a mechanism has been constructed to impart an impact load on the weld. This mechanism has been designed and used for many years with little understanding of how the mechanism components affect the performance of the mechanism. In order to gain this understanding, a dynamic model of the mechanism was created

Powered Transtibial Prosthetic Device Control System Design, Implementation, and Bench Testing

This article outlines the controller design for a specific active transtibial prosthesis. The controller governs the power output of a DC motor attached to a four-bar mechanism and torsional spring. Active power reinforcement is used to assist the push off at later stages of the stance phase and achieve ground clearance during the swing phase. A two level control algorithm which includes a higher level finite state controller and lower level proportional-integral-derivative (PID) controllers is applied. To implement this control algorithm, a digital signal processor (DSP) control board was used to realize the higher level control and an off-the-shelf motor controller was used to realize the lower level PID control. Sensors were selected to provide the desired feedback. A dynamic simulation was performed to obtain the proper PID parameters which were then utilized in a bench test to verify the approach

Dynamic Simulation of Human Gait Using a Combination of Model Predictive and PID Control

Human gait studies have not been applied frequently to the prediction of the performance of medical devices such as prostheses and orthoses. The reason is most biomechanics simulations require experimental data such as muscle activity or joint moment information a priori. In addition, biomechanical models are normally too complicated to be adjusted and these simulations normally take a long period of time to be performed which makes testing of various possibilities time consuming; therefore they are not suitable for prediction purpose. The objective of this research is to develop a control oriented human gait model that is able to predict the performance of prostheses and orthoses before they are experimentally tested. This model is composed of two parts. The first part is a seven link nine degree-of-freedom (DOF) plant to represent the forward dynamics of human gait. The second part is a control system which is a combination of Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. The purpose of this control system is to simulate the central nervous system (CNS). This model is sufficiently simple that it can be simulated and adjusted in a reasonable time, while still representing the essential principles of human gait

Design of an Active Ankle-Foot Prosthesis Utilizing a Four-Bar Mechanism

Thisarticle discusses the design and testing of a powered ankleprosthesis. This new prosthesis mimics nonamputee (normal) ankle moments duringthe stance phase of gait through the use of anoptimized spring loaded four-bar mechanism. A prototype prosthesis based onthe optimization was designed, fabricated, and tested. The experimental resultsachieved 93.3% of the simulated theoretical ankle moment giving substantialevidence that this approach is a viable in designing poweredankle prostheses