Noninvasive Neuroprosthetic Control of Grasping by Amputees

Smooth coordination and fine temporal control of muscles by the brain allows us to effortlessly pre-shape our hand to grasp different objects. Correlates of motor control for grasping have been found across wide-spread cortical areas, with diverse signal features. These signals have been harnessed by implanting intracortical electrodes and used to control the motion of robotic hands by tetraplegics, using algorithms called brain-machine interfaces (BMIs). Signatures of motor control signal encoding mechanisms of the brain in macro-scale signals such as electroencephalography (EEG) are unknown, and could potentially be used to develop noninvasive brain-machine interfaces. Here we show that a) low frequency (0.1 – 1 Hz) time domain EEG contains information about grasp pre-shaping in able-bodies individuals, and b) This information can be used to control pre-shaping motion of a robotic hand by amputees. In the first study, we recorded simultaneous EEG and hand kinematics as 5 able-bodies individuals grasped various objects. Linear decoders using low delta band EEG amplitudes accurately predicted hand pre-shaping kinematics during grasping. Correlation coefficient between predicted and actual kinematics was r = 0.59 ± 0.04, 0.47 ± 0.06 and 0.32 ± 0.05 for the first 3 synergies. In the second study, two transradial amputees (A1 and A2) controlled a prosthetic hand to grasp two objects using a closed-loop BMI with low delta band EEG. This study was conducted longitudinally in 12 sessions spread over 38 days. A1 achieved a 63% success rate, with 11 sessions significantly above chance. A2 achieved a 32% success rate, with 2 sessions significantly above chance. Previous methods of EEG-based BMIs used frequency domain features, and were thought to have a low signal-to-noise ratio making them unsuitable for control of dexterous tasks like grasping. Our results demonstrate that time-domain EEG contains information about grasp pre-shaping, which can be harnessed for neuroprosthetic control.Electrical and Computer Engineering, Department o

ReHand - a portable assistive rehabilitation hand exoskeleton

This dissertation presents a synthesis of a novel underactuated exoskeleton (namely ReHand2) thought and designed for a task-oriented rehabilitation and/or for empower the human hand. The first part of this dissertation shows the current context about the robotic rehabilitation with a focus on hand pathologies, which influence the hand capability. The chapter is concluded with the presentation of ReHand2. The second chapter describes the human hand biomechanics. Starting from the definition of human hand anatomy, passing through anthropometric data, to taxonomy on hand grasps and finger constraints, both from static and dynamic point of view. In addition, some information about the hand capability are given. The third chapter analyze the current state of the art in hand exoskeleton for rehabilitation and empower tasks. In particular, the chapter presents exoskeleton technologies, from mechanisms to sensors, passing though transmission and actuators. Finally, the current state of the art in terms of prototype and commercial products is presented. The fourth chapter introduces the concepts of underactuation with the basic explanation and the classical notation used typically in the prosthetic field. In addition, the chapter describe also the most used differential elements in the prosthetic, follow by a statical analysis. Moreover typical transmission tree at inter-finger level as well as the intra- finger underactuation are explained . The fifth chapter presents the prototype called ReHand summarizing the device description and explanation of the working principle. It describes also the kinetostatic analysis for both, inter- and the intra-finger modules. in the last section preliminary results obtained with the exoskeleton are shown and discussed, attention is pointed out on prototype’s problems that have carry out at the second version of the device. The sixth chapter describes the evolution of ReHand, describing the kinematics and dynamics behaviors. In particular, for the mathematical description is introduced the notation used in order to analyze and optimize the geometry of the entire device. The introduced model is also implemented in Matlab Simulink environment. Finally, the chapter presents the new features. The seventh chapter describes the test bench and the methodologies used to evaluate the device statical, and dynamical performances. The chapter presents and discuss the experimental results and compare them with simulated one. Finally in the last chapter the conclusion about the ReHand project are proposed as well as the future development. In particular, the idea to test de device in relevant environments. In addition some preliminary considerations about the thumb and the wrist are introduced, exploiting the possibility to modify the entire layout of the device, for instance changing the actuator location

On the development of a cybernetic prosthetic hand

The human hand is the end organ of the upper limb, which in humans serves the important function of prehension, as well as being an important organ for sensation and communication. It is a marvellous example of how a complex mechanism can be implemented, capable of realizing very complex and useful tasks using a very effective combination of mechanisms, sensing, actuation and control functions. In this thesis, the road towards the realization of a cybernetic hand has been presented. After a detailed analysis of the model, the human hand, a deep review of the state of the art of artificial hands has been carried out. In particular, the performance of prosthetic hands used in clinical practice has been compared with the research prototypes, both for prosthetic and for robotic applications. By following a biomechatronic approach, i.e. by comparing the characteristics of these hands with the natural model, the human hand, the limitations of current artificial devices will be put in evidence, thus outlining the design goals for a new cybernetic device. Three hand prototypes with a high number of degrees of freedom have been realized and tested: the first one uses microactuators embedded inside the structure of the fingers, and the second and third prototypes exploit the concept of microactuation in order to increase the dexterity of the hand while maintaining the simplicity for the control. In particular, a framework for the definition and realization of the closed-loop electromyographic control of these devices has been presented and implemented. The results were quite promising, putting in evidence that, in the future, there could be two different approaches for the realization of artificial devices. On one side there could be the EMG-controlled hands, with compliant fingers but only one active degree of freedom. On the other side, more performing artificial hands could be directly interfaced with the peripheral nervous system, thus establishing a bi-directional communication with the human brain

Transcutaneous Nerve Bundle Stimulation for Dexterous Hand Grasp Patterns: Development and Exploration of an Alternative Stimulation Method

Impairment of the hand following a neurological injury such as stroke is a major contributing factor to the loss of independence and self-sufficiency. Neuromuscular Electrical Stimulation (NMES) is a widely utilized technique to help alleviate lost muscle strength by electrically eliciting muscle contractions. However, conventional NMES applied directly over the muscle belly often faces various limitations, which prevent long-term use and efficacy. Traditional NMES techniques induce rapid muscle fatigue due to non-physiological activation of fibers resulting in a decline of muscle force. For the hand, stimulation at the skin surface typically only activates the superficial extrinsic hand muscles, leading to limited multi-joint control. To overcome these limitations, we sought to develop an alternative stimulation technique that used a high-density surface electrode array to directly target major nerve bundles at a location more proximal to the muscles. First, we designed an automated stimulation paradigm to characterize the different patterns of finger flexion elicitable via the nerve stimulation method. Randomized pairs in the electrode array were used to search for the best stimulation locations. We demonstrated that the nerve stimulation can generate a variety of single and multi-finger flexion patterns, with selective sets of nerve fiber activation and high activation redundancy. Secondly, we compared the force sustainability of the proximal nerve stimulation with conventional muscle belly stimulation. We found that, with prolonged force-matched stimulations, the proximal nerve stimulation technique can significantly delay the decline of force production over time, which allowed us to elicit sustained muscle force output. Lastly, we investigated the ability of the proximal nerve stimulation to activate both the superficial and deep extrinsic finger flexors. We obtained ultrasound images of the cross section of the flexor muscles in the forearm, and image deformation was used as a surrogate measure of muscle contraction. We found that superficial and deep muscles could be separately or concurrently activated. Overall, this work demonstrated the appealing features of our nerve stimulation method in selectively recruiting different finger flexor muscles with sustained activation. The outcomes also lay the theoretical foundation for further development of proximal nerve stimulation as an alternative approach for effective hand rehabilitation.Doctor of Philosoph

EMG-based decoding of grasp gestures in reaching-to-grasping motions

Predicting the grasping function during reach-to-grasp motions is essential for controlling a prosthetic hand or a robotic assistive device. An early accurate prediction increases the usability and the comfort of a prosthetic device. This work proposes an electromyographic-based learning approach that decodes the grasping intention at an early stage of reach-to-grasp motion, i.e. before the final grasp/hand pre-shape takes place. Superficial electrodes and a Cyberglove were used to record the arm muscle activity and the finger joints during reach-to-grasp motions. Our results showed a 90% accuracy for the detection of the final grasp about 0.5 s after motion onset. This paper also examines the effect of different objects’ distances and different motion speeds on the detection time and accuracy of the classifier. The use of our learning approach to control a 16-degrees of freedom robotic hand confirmed the usability of our approach for the real-time control of robotic devices

Development and validation of haptic devices for studies on human grasp and rehabilitation

This thesis aims to develop and to validate a new set of devices for accurate investigation of human finger stiffness and force distribution in grasping tasks. The ambitious goal of this research is twofold: 1) to advance the state of art on human strategies in manipulation tasks and provide tools to assess rehabilitation procedure, 2) to investigate human strategies for impedance control that can be used for human robot interaction and control of myoelectric prosthesis. The first part of this thesis describes two types of systems that enable to achieve a complete set of measurements on force distribution and contact point locations. More specifically, this part includes: (i) the design process and validation of tripod grasp devices with controllable stiffness at the contact to be used also for rehabilitation purposes, and (ii) the validation of multi-digit wearable sensor system. Results on devices validation as well as illustrative measurement examples are reported and discussed. The effectiveness of these devices in grasp analysis was also experimentally demonstrated and applications to neuroscientific studies are discussed. In the second part of this thesis, the tripod devices are exploited in two different studies to investigate stiffness regulation principles in humans. The first study provides evidence on the existence of coordinated stiffening patterns in human hand fingers and establishes initial steps towards a real-time and effective modelling of finger stiffness in tripod grasp. This pattern further supports the evidence of synergistic control in human grasping. To achieve this goal, the endpoint stiffness of the thumb, index and middle fingers of healthy subjects are experimentally identified and correlated with the electromyography (EMG) signals recorded from a dominant antagonistic pair of the forearm muscles. Our findings suggest that the magnitude of the stiffness ellipses at the fingertips grows in a coordinated way, subsequent to the co-contraction of the forearm muscles. The second study presents experimental findings on how humans modulate their hand stiffness while grasping object of varying levels of compliance. Subjects perform a grasp and lift task with a tripod-grasp object with contact surfaces of variable compliance; EMG from the main finger flexor and extensor muscles was recorded along with force and torque data at the contact points. A significant increase in the extensor muscle and cocontraction levels is evidenced with an increasing compliance at the contact points. Overall results give solid evidence on the validity and utility of the proposed devices to investigate human grasp proprieties. The underlying motor control principles that are exploited by humans in the achievement of a reliable and robust grasp can be potentially integrated into the control framework of robotic or prosthetic hands to achieve a similar interaction performance

Compensating hand function in chronic stroke patients through the robotic sixth finger

A novel solution to compensate hand grasping abilities is proposed for chronic stroke patients. The goal is to provide the patients with a wearable robotic extra-finger that can be worn on the paretic forearm by means of an elastic band. The proposed prototype, the Robotic Sixth Finger, is a modular articulated device that can adapt its structure to the grasped object shape. The extra-finger and the paretic hand act like the two parts of a gripper cooperatively holding an object. We evaluated the feasibility of the approach with four chronic stroke patients performing a qualitative test, the Frenchay Arm Test. In this proof of concept study, the use of the Robotic Sixth Finger has increased the total score of the patients of 2 points in a 5 points scale. The subjects were able to perform the two grasping tasks included in the test that were not possible without the robotic extra-finger. Adding a robotic opposing finger is a very promising approach that can significantly improve the functional compensation of the chronic stroke patient during everyday life activities