Design, implementation, and evaluation of a variable stiffness transradial hand prosthesis

We present the design, implementation, and experimental evaluation of a low-cost, customizable, easy-to-use transradial hand prosthesis capable of adapting its compliance. Variable stiffness actuation (VSA) of the prosthesis is based on antagonistically arranged tendons coupled to nonlinear springs driven through a Bowden cable based power transmission. Bowden cable based antagonistic VSA can, not only regulate the stiffness and the position of the prosthetic hand but also enables a light-weight and low-cost design, by the opportunistic placement of motors, batteries, and controllers on any convenient location on the human body, while nonlinear springs are conveniently integrated inside the forearm. The transradial hand prosthesis also features tendon driven underactuated compliant fingers that allow natural adaption of the hand shape to wrap around a wide variety of object geometries, while the modulation of the stiffness of their drive tendons enables the prosthesis to perform various tasks with high dexterity. The compliant fingers of the prosthesis add inherent robustness and flexibility, even under impacts. The control of the variable stiffness transradial hand prosthesis is achieved by an sEMG based natural human-machine interface

User Intent Detection and Control of a Soft Poly-Limb

abstract: This work presents the integration of user intent detection and control in the development of the fluid-driven, wearable, and continuum, Soft Poly-Limb (SPL). The SPL utilizes the numerous traits of soft robotics to enable a novel approach to provide safe and compliant mobile manipulation assistance to healthy and impaired users. This wearable system equips the user with an additional limb made of soft materials that can be controlled to produce complex three-dimensional motion in space, like its biological counterparts with hydrostatic muscles. Similar to the elephant trunk, the SPL is able to manipulate objects using various end effectors, such as suction adhesion or a soft grasper, and can also wrap its entire length around objects for manipulation. User control of the limb is demonstrated using multiple user intent detection modalities. Further, the performance of the SPL studied by testing its capability to interact safely and closely around a user through a spatial mobility test. Finally, the limb’s ability to assist the user is explored through multitasking scenarios and pick and place tests with varying mounting locations of the arm around the user’s body. The results of these assessments demonstrate the SPL’s ability to safely interact with the user while exhibiting promising performance in assisting the user with a wide variety of tasks, in both work and general living scenarios.Dissertation/ThesisMasters Thesis Biomedical Engineering 201

Study and development of sensorimotor interfaces for robotic human augmentation

This thesis presents my research contribution to robotics and haptics in the context of human augmentation. In particular, in this document, we are interested in bodily or sensorimotor augmentation, thus the augmentation of humans by supernumerary robotic limbs (SRL). The field of sensorimotor augmentation is new in robotics and thanks to the combination with neuroscience, great leaps forward have already been made in the past 10 years. All of the research work I produced during my Ph.D. focused on the development and study of fundamental technology for human augmentation by robotics: the sensorimotor interface. This new concept is born to indicate a wearable device which has two main purposes, the first is to extract the input generated by the movement of the user's body, and the second to provide the somatosensory system of the user with an haptic feedback. This thesis starts with an exploratory study of integration between robotic and haptic devices, intending to combine state-of-the-art devices. This allowed us to realize that we still need to understand how to improve the interface that will allow us to feel the agency when using an augmentative robot. At this point, the path of this thesis forks into two alternative ways that have been adopted to improve the interaction between the human and the robot. In this regard, the first path we presented tackles two aspects conerning the haptic feedback of sensorimotor interfaces, which are the choice of the positioning and the effectiveness of the discrete haptic feedback. In the second way we attempted to lighten a supernumerary finger, focusing on the agility of use and the lightness of the device. One of the main findings of this thesis is that haptic feedback is considered to be helpful by stroke patients, but this does not mitigate the fact that the cumbersomeness of the devices is a deterrent to their use. Preliminary results here presented show that both the path we chose to improve sensorimotor augmentation worked: the presence of the haptic feedback improves the performance of sensorimotor interfaces, the co-positioning of haptic feedback and the input taken from the human body can improve the effectiveness of these interfaces, and creating a lightweight version of a SRL is a viable solution for recovering the grasping function

Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control

A Control Architecture for Grasp Strength Regulation in Myocontrolled Robotic Hands Using Vibrotactile Feedback: Preliminary Results

Nowadays, electric-powered hand prostheses do not provide adequate sensory instrumentation and artificial feedback to allow users voluntarily and finely modulate the grasp strength applied to the objects. In this work, the design of a control architecture for a myocontrol-based regulation of the grasp strength for a robotic hand equipped with contact force sensors is presented. The goal of the study was to provide the user with the capability of modulating the grasping force according to target required levels by exploiting a vibrotactile feedback. In particular, the whole human-robot control system is concerned (i.e. myocontrol, robotic hand controller, vibrotactile feedback.) In order to evaluate the intuitiveness and force tracking performance provided by the proposed control architecture, an experiment was carried out involving four naïve able-bodied subjects in a grasping strength regulation task with a myocontrolled robotic hand (the University of Bologna Hand), requiring for grasping different objects with specific target force levels. The reported results show that the control architecture successfully allowed all subjects to achieve all grasping strength levels exploiting the vibrotactile feedback information. This preliminary demonstrates that, potentially, the proposed control interface can be profitably exploited in upper-limb prosthetic applications, as well as for non-rehabilitation uses, e.g. in ultra-light teleoperation for grasping devices

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