Studies of the relationship between the surface electromyogram, joint torque and impedance

This compendium-format dissertation (i.e., comprised mostly of published and in-process articles) primarily reports on system identification methods that relate the surface electromyogram (EMG)—the electrical activity of skeletal muscles—to mechanical kinetics. The methods focus on activities of the elbow and hand-wrist. The relationship between the surface EMG and joint impedance was initially studied. My work provided a complete second-order EMG-based impedance characterization of stiffness, viscosity and inertia over a complete range of nominal torques, from a single perturbation trial with slowly varied torque. A single perturbation trial provides a more convenient method for impedance evaluation. The RMS errors of the EMG-based method were 20.01% for stiffness and 7.05% for viscosity, compared with the traditional mechanical measurement. Three projects studied the relationship between EMG and force/torque, a topic that has been studied for a number of years. Optimal models use whitened EMG amplitude, combining multiple EMG channels and a polynomial equation to describe this relationship. First, we used three techniques to improve current models at the elbow joint. Three more features were extracted from the EMG (waveform length, slope sign change rate and zero crossing rate), in addition to EMG amplitude. Each EMG channel was used separately, compared to previous studies which combined multiple channels from biceps and, separately, from triceps muscles. Finally, an exponential power law model was used. Each of these improvement techniques showed better performance (P\u3c0.05 and ~0.7 percent maximum voluntary contraction (%MVC) error reduction from a nominal error of 5.5%MVC) than the current “optimal” model. However, the combination of pairs of these techniques did not further improve results. Second, traditional prostheses only control 1 degree of freedom (DoF) at a time. My work provided evidence for the feasibility of controlling 2-DoF wrist movements simultaneously, with a minimum number of electrodes. Results suggested that as few as four conventional electrodes, optimally located about the forearm, could provide 2-DoF simultaneous, independent and proportional control with error ranging from 9.0–10.4 %MVC, which is similar to the 1-DoF approach (error from 8.8–9.8 %MVC) currently used for commercial prosthesis control. The third project was similar to the second, except that this project studied controlling a 1-DoF wrist with one hand DoF simultaneously. It also demonstrated good performance with the error ranging from 7.8-8.7 %MVC, compared with 1-DoF control. Additionally, I participated in two team projects—EMG decomposition and static wrist EMG to torque—which are described herein

Surface EMG pattern recognition for real-time control of a wrist exoskeleton

<p>Abstract</p> <p>Background</p> <p>Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work.</p> <p>Methods</p> <p>Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme.</p> <p>Results</p> <p>The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms.</p> <p>Conclusions</p> <p>The results of this study showed that classification of EMG signals by separating different levels of torque is possible for wrist motion and the use of only four EMG channels is suitable. The study also showed that SVM classification technique is suitable for real-time classification of sEMG signals and can be effectively implemented for controlling an exoskeleton device for assisting the wrist.</p

Teleimpedance Control of a Synergy-Driven Anthropomorphic Hand

In this paper, a novel synergy driven teleimpedance controller for the Pisa–IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa–IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user’s postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardwarebased synergies is a viable alternative to traditional myoelectric prostheses

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

A Biomimetic Approach to Controlling Restorative Robotics

Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control. Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands. Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques. Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury. Movement is the only way a person can interact with the world around them. When trauma to the neuromuscular systems disrupts the control of movement, quality of life suffers. To restore limb functionality, active robotic interventions and/or rehabilitation are required. Unfortunately, the primary obstacle in a person’s recovery is the limited robustness of the human-machine interfaces. Current systems rely on control approaches that rely on the person to learn how the system works instead of the system being more intuitive and working with the person naturally. My research goal is to design intuitive control mechanisms based on biological processes termed the biomimetic approach. I have applied this control scheme to problems with restorative robotics focused on the upper and lower limb control.Operating an advanced active prosthetic hand is a two-pronged problem of actuating a high-dimensional mechanism and controlling it with an intuitive interface. Our approach attempts to solve these problems by going from muscle activity, electromyography (EMG), to limb kinematics calculated through dynamic simulation of a musculoskeletal model. This control is more intuitive to the user because they attempt to move their hand naturally, and the prosthetic hand performs that movement. The key to this approach was validating simulated muscle paths using both experimental measurements and anatomical constraints where data is missing. After the validation, simulated muscle paths and forces are used to decipher the intended movement. After we have calculated the intended movement, we can move a prosthetic hand to match. This approach required minimal training to give an amputee the ability to control prosthetic hand movements, such as grasping. A more intuitive controller has the potential to improve how people interact and use their prosthetic hands.Similarly, the rehabilitation of the locomotor system in people with damaged motor pathways or missing limbs require appropriate interventions. The problem of decoding human motor intent in a treadmill walking task can be solved with a biomimetic approach. Estimated limb speed is essential for this approach according to the theoretical input-output computation performed by spinal central pattern generators (CPGs), which represents neural circuitry responsible for autonomous control of stepping. The system used the locomotor phases, swing and stance, to estimate leg speeds and enable self-paced walking as well as steering in virtual reality with congruent visual flow. The unique advantage of this system over the previous state-of-art is the independent leg speed control, which is required for multidirectional movement in VR. This system has the potential to contribute to VR gait rehab techniques.Creating biologically-inspired controllers has the potential to improve restorative robotics and allow people a better opportunity to recover lost functionality post-injury

The "Federica" hand: a simple, very efficient prothesis

Hand prostheses partially restore hand appearance and functionalities. Not everyone can afford expensive prostheses and many low-cost prostheses have been proposed. In particular, 3D printers have provided great opportunities by simplifying the manufacturing process and reducing costs. Generally, active prostheses use multiple motors for fingers movement and are controlled by electromyographic (EMG) signals. The "Federica" hand is a single motor prosthesis, equipped with an adaptive grasp and controlled by a force-myographic signal. The "Federica" hand is 3D printed and has an anthropomorphic morphology with five fingers, each consisting of three phalanges. The movement generated by a single servomotor is transmitted to the fingers by inextensible tendons that form a closed chain; practically, no springs are used for passive hand opening. A differential mechanical system simultaneously distributes the motor force in predefined portions on each finger, regardless of their actual positions. Proportional control of hand closure is achieved by measuring the contraction of residual limb muscles by means of a force sensor, replacing the EMG. The electrical current of the servomotor is monitored to provide the user with a sensory feedback of the grip force, through a small vibration motor. A simple Arduino board was adopted as processing unit. The differential mechanism guarantees an efficient transfer of mechanical energy from the motor to the fingers and a secure grasp of any object, regardless of its shape and deformability. The force sensor, being extremely thin, can be easily embedded into the prosthesis socket and positioned on both muscles and tendons; it offers some advantages over the EMG as it does not require any electrical contact or signal processing to extract information about the muscle contraction intensity. The grip speed is high enough to allow the user to grab objects on the fly: from the muscle trigger until to the complete hand closure, "Federica" takes about half a second. The cost of the device is about 100 US$. Preliminary tests carried out on a patient with transcarpal amputation, showed high performances in controlling the prosthesis, after a very rapid training session. The "Federica" hand turned out to be a lightweight, low-cost and extremely efficient prosthesis. The project is intended to be open-source: all the information needed to produce the prosthesis (e.g. CAD files, circuit schematics, software) can be downloaded from a public repository. Thus, allowing everyone to use the "Federica" hand and customize or improve it