Using Lower Extremity Muscle Activations to Estimate Human Ankle Impedance in the External-Internal Direction

For millions of people, mobility has been afflicted by lower limb amputation. Lower extremity prostheses have been used to improve the mobility of an amputee; however, they often require additional compensation from other joints and do not allow for natural maneuverability. To improve upon the functionality of ankle-foot prostheses, it is necessary to understand the role of different muscle activations in the modulation of mechanical impedance of a healthy human ankle. This report presents the results of using artificial neural networks (ANN) to determine the functional relationship between lower extremity electromyography (EMG) signals and ankle impedance in the transverse plane. The Anklebot was used to apply pseudo-random perturbations to the human ankle in the transverse plane, while motion of the ankle in the sagittal and frontal planes was constrained. Using a stochastic system identification method, the mechanical impedance of the ankle in external-internal (EI) direction was determined as a function of the applied torque and corresponding ankle motion. The impedance of the ankle and muscle EMG signals were determined for three muscle activation levels, including with relaxed muscles, and with muscles activated and 10% and 20% of the subject’s maximum voluntary contraction (MVC). This information was used as the input and target matrices to train an ANN for each subject. The resulting ankle impedance from the proposed ANN was effectively predicted within 85% accuracy for nine out of ten subjects, and was within ±5 Nm/rad of the target impedance for all subjects. This work provides more understanding of the neuromuscular characteristics of the ankle and provides insight toward future design and control of ankle-foot prostheses

Adaptive Compliance Shaping with Human Impedance Estimation

Human impedance parameters play an integral role in the dynamics of strength amplification exoskeletons. Many methods are used to estimate the stiffness of human muscles, but few are used to improve the performance of strength amplification controllers for these devices. We propose a compliance shaping amplification controller incorporating an accurate online human stiffness estimation from surface electromyography (sEMG) sensors and stretch sensors connected to the forearm and upper arm of the human. These sensor values along with exoskeleton position and velocity are used to train a random forest regression model that accurately predicts a person's stiffness despite varying movement, relaxation, and muscle co-contraction. Our model's accuracy is verified using experimental test data and the model is implemented into the compliance shaping controller. Ultimately we show that the online estimation of stiffness can improve the bandwidth and amplification of the controller while remaining robustly stable.Comment: 8 pages, 9 figures, Accepted for publication at the 2020 American Control Conference. Copyright IEEE 202

Equilibrium-point control of human elbow-joint movement under isometric environment by using multichannel functional electrical stimulation

Functional electrical stimulation (FES) is considered an effective technique for aiding quadriplegic persons. However, the human musculoskeletal system has highly nonlinearity and redundancy. It is thus difficult to stably and accurately control limbs using FES. In this paper, we propose a simple FES method that is consistent with the motion-control mechanism observed in humans. We focus on joint motion by a pair of agonist-antagonist muscles of the musculoskeletal system, and define theelectrical agonist-antagonist muscle ratio (EAA ratio) and electrical agonist-antagonist muscle activity (EAA activity) in light of the agonist-antagonist muscle ratio and agonist-antagonist muscle activity, respectively, to extract the equilibrium point and joint stiffness from electromyography (EMG) signals. These notions, the agonist-antagonist muscle ratio and agonist-antagonist muscle activity, are based on the hypothesis that the equilibrium point and stiffness of the agonist-antagonist motion system are controlled by the central nervous system. We derived the transfer function between the input EAA ratio and force output of the end-point. We performed some experiments in an isometric environment using six subjects. This transfer-function model is expressed as a cascade-coupled dead time element and a second-order system. High-speed, high-precision, smooth control of the hand force were achieved through the agonist-antagonist muscle stimulation pattern determined by this transfer function model

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer

Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control

In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait. Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal. Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities. The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes. From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait. The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait

肘関節粘弾性特性分析に基づいた可変粘弾性握手マニピュレータの開発

【学位授与の要件】中央大学学位規則第4条第1項【論文審査委員主査】中村　太郎　（中央大学理工学部教授）【論文審査委員副査】平岡　弘之（中央大学理工学部教授）、新妻　実保子（中央大学理工学部准教授）、諸麥　俊司（中央大学理工学部准教授）、万　偉偉（大阪大学准教授）博士(工学)中央大

Adaptive compliance shaping with human impedance estimation

Robotics has been a promising and popular research area for the past few decades. Among various applications of robotic, in many cases, human are involved in different manners. Therefore, as an important sub research area of robotics, human robot interaction has drawn decent attention recently. It has been deeply and widely studied. For human robot interaction, human play an important role. Undoubtedly, the more we know about human, the easier we can do human robot interaction and the better performance we can achieve in human robot interaction. One fascinating research topic of human robot interaction would be human in exoskeleton, where human play a key role in the mechanical design of exoskeleton as well as the control strategy design of exoskeleton. Among all those applications, the augmentation exoskeleton is especially interesting due to its ability to amplify human. As mentioned previously, human properties are important for the design of exoskeleton. Unfortunately, despite many inspiring and deep studies about human properties and various proposed human models, human remains to be a complicated system that is hard to predict and model. Furthermore, human is a dynamic system whose parameters keep changing with time, bringing more challenges. As we all know, limited understanding of the control plant will limit the performance of the controller and bring difficulties in the design of a controller. In fact, the performance of many existed controller for augmentation exoskeleton is limited by using conservative values of human property parameters. A straightforward way to solve this problem is to estimate human properties online. Under this circumstance, the main challenges are to develop a control strategy, whose performance can be exploited using the estimation of human properties and a reliable method to online estimate human properties. This thesis mainly presents an adaptive compliance shaping control strategy with human impedance estimation and a brief review of a newly proposed complex stiffness model of human.Mechanical Engineerin

Electromyography Based Human-Robot Interfaces for the Control of Artificial Hands and Wearable Devices

The design of robotic systems is currently facing human-inspired solutions as a road to replicate the human ability and flexibility in performing motor tasks. Especially for control and teleoperation purposes, the human-in-the-loop approach is a key element within the framework know as Human-Robot Interface. This thesis reports the research activity carried out for the design of Human-Robot Interfaces based on the detection of human motion intentions from surface electromyography. The main goal was to investigate intuitive and natural control solutions for the teleoperation of both robotic hands during grasping tasks and wearable devices during elbow assistive applications. The design solutions are based on the human motor control principles and surface electromyography interpretation, which are reviewed with emphasis on the concept of synergies. The electromyography based control strategies for the robotic hand grasping and the wearable device assistance are also reviewed. The contribution of this research for the control of artificial hands rely on the integration of different levels of the motor control synergistic organization, and on the combination of proportional control and machine learning approaches under the guideline of user-centred intuitiveness in the Human-Robot Interface design specifications. From the side of the wearable devices, the control of a novel upper limb assistive device based on the Twisted String Actuation concept is faced. The contribution regards the assistance of the elbow during load lifting tasks, exploring a simplification in the use of the surface electromyography within the design of the Human-Robot Interface. The aim is to work around complex subject-dependent algorithm calibrations required by joint torque estimation methods

Bio-inspired robotic joint and manipulator : from biomechanical experimentation and modeling to human-like compliant finger design and control

textOne of the greatest challenges in controlling robotic hands is grasping and manipulating objects in unstructured and uncertain environments. Robotic hands are typically too rigid to react against unexpected impacts and disturbances in order to prevent damage. The human hands have great versatility and robustness due, in part, to the passive compliance and damping. Designing mechanical elements that are inspired by the nonlinear joint compliance of human hands is a promising solution to achieve human-like grasping and manipulation. However, the exact role of biomechanical elements in realizing joint stiffness is unknown. We conducted a series of experiments to investigate nonlinear stiffness and damping of the metacarpophalangeal (MCP) joint at the index finger. We designed a custom-made mechanism to integrate electromyography sensors (EMGs) and a motion capture system to collect data from 19 subjects. We investigated the relative contributions of muscle-tendon units and the MCP capsule ligament complex to joint stiffness with subject-specific modeling. The results show that the muscle-tendon units provide limited contribution to the passive joint compliance. This findings indicate that the parallel compliance, in the form of the capsule-ligament complex, is significant in defining the passive properties of the hand. To identify the passive damping, we used the hysteresis loops to investigate the energy dissipation function. We used symbolic regression and principal component analysis to derive and interpret the damping models. The results show that the nonlinear viscous damping depends on the cyclic frequency, and fluid and structural types of damping also exist at the MCP joint. Inspired by the nonlinear stiffness of the MCP joint, we developed a miniaturized mechanism that uses pouring liquid plastic to design energy storing elements. The key innovations in this design are: a) a set of nonlinear elasticity of compliant materials, b) variable pulley configurations to tune the stiffness profile, and c) pretension mechanism to scale the stiffness profile. The design exhibits human-like passive compliance. By taking advantage of miniaturized joint size and additive manufacturing, we incorporated the novel joint design in a novel robotic manipulator with six series elastic actuators (SEA). The robotic manipulator has passive joint compliance with the intrinsic property of human hands. To validate the system, we investigated the Cartesian stiffness of grasping with low-level force control. The results show that that the overall system performs a great force tracking with position feedback. The parallel compliance decreases the motor efforts and can stabilize the system.Mechanical Engineerin