The design and mathematical modelling of novel extensor bending pneumatic artificial muscles (EBPAMs) for soft exoskeletons

This article presents the development of a power augmentation and rehabilitation exoskeleton based on a novel actuator. The proposed soft actuators are extensor bending pneumatic artificial muscles. This type of soft actuator is derived from extending McKibben artificial muscles by reinforcing one side to prevent extension. This research has experimentally assessed the performance of this new actuator and an output force mathematical model for it has been developed. This new mathematical model based on the geometrical parameters of the extensor bending pneumatic artificial muscle determines the output force as a function of the input pressure. This model is examined experimentally for different actuator sizes. After promising initial experimental results, further model enhancements were made to improve the model of the proposed actuator. To demonstrate the new bending actuators a power augmentation and rehabilitation soft glove has been developed. This soft hand exoskeleton is able to fit any adult hand size without the need for any mechanical system changes or calibration. EMG signals from the human hand have been monitored to prove the performance of this new design of soft exoskeleton. This power augmentation and rehabilitation wearable robot has been shown to reduce the amount of muscles effort needed to perform a number of simple grasps

A virtual hand assessment system for efficient outcome measures of hand rehabilitation

Previously held under moratorium from 1st December 2016 until 1st December 2021.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control.Hand rehabilitation is an extremely complex and critical process in the medical rehabilitation field. This is mainly due to the high articulation of the hand functionality. Recent research has focused on employing new technologies, such as robotics and system control, in order to improve the precision and efficiency of the standard clinical methods used in hand rehabilitation. However, the designs of these devices were either oriented toward a particular hand injury or heavily dependent on subjective assessment techniques to evaluate the progress. These limitations reduce the efficiency of the hand rehabilitation devices by providing less effective results for restoring the lost functionalities of the dysfunctional hands. In this project, a novel technological solution and efficient hand assessment system is produced that can objectively measure the restoration outcome and, dynamically, evaluate its performance. The proposed system uses a data glove sensorial device to measure the multiple ranges of motion for the hand joints, and a Virtual Reality system to return an illustrative and safe visual assistance environment that can self-adjust with the subject’s performance. The system application implements an original finger performance measurement method for analysing the various hand functionalities. This is achieved by extracting the multiple features of the hand digits’ motions; such as speed, consistency of finger movements and stability during the hold positions. Furthermore, an advanced data glove calibration method was developed and implemented in order to accurately manipulate the virtual hand model and calculate the hand kinematic movements in compliance with the biomechanical structure of the hand. The experimental studies were performed on a controlled group of 10 healthy subjects (25 to 42 years age). The results showed intra-subject reliability between the trials (average of crosscorrelation ρ = 0.7), inter-subject repeatability across the subject’s performance (p > 0.01 for the session with real objects and with few departures in some of the virtual reality sessions). In addition, the finger performance values were found to be very efficient in detecting the multiple elements of the fingers’ performance including the load effect on the forearm. Moreover, the electromyography measurements, in the virtual reality sessions, showed high sensitivity in detecting the tremor effect (the mean power frequency difference on the right Vextensor digitorum muscle is 176 Hz). Also, the finger performance values for the virtual reality sessions have the same average distance as the real life sessions (RSQ =0.07). The system, besides offering an efficient and quantitative evaluation of hand performance, it was proven compatible with different hand rehabilitation techniques where it can outline the primarily affected parts in the hand dysfunction. It also can be easily adjusted to comply with the subject’s specifications and clinical hand assessment procedures to autonomously detect the classification task events and analyse them with high reliability. The developed system is also adaptable with different disciplines’ involvements, other than the hand rehabilitation, such as ergonomic studies, hand robot control, brain-computer interface and various fields involving hand control

Wearable exoskeleton systems based-on pneumatic soft actuators and controlled by parallel processing

Human assistance innovation is essential in an increasingly aging society and one technology that may be applicable is exoskeletons. However, traditional rigid exoskeletons have many drawbacks. This research includes the design and implementation of upper-limb power assist and rehabilitation exoskeletons based on pneumatic soft actuators. A novel extensor-contractor pneumatic muscle has been designed and constructed. This new actuator has bidirectional action, allowing it to both extend and contract, as well as create force in both directions. A mathematical model has been developed for the new novel actuator which depicts the output force of the actuator. Another new design has been used to create a novel bending pneumatic muscle, based on an extending McKibben muscle and modelled mathematically according to its geometric parameters. This novel bending muscle design has been used to create two versions of power augmentation gloves. These exoskeletons are controlled by adaptive controllers using human intention. For finger rehabilitation a glove has been developed to bend the fingers (full bending) by using our novel bending muscles. Inspired by the zero position (straight fingers) problem for post-stroke patients, a new controllable stiffness bending actuator has been developed with a novel prototype. To control this new rehabilitation exoskeleton, online and offline controller systems have been designed for the hand exoskeleton and the results have been assessed experimentally. Another new design of variable stiffness actuator, which controls the bending segment, has been developed to create a new version of hand exoskeletons in order to achieve more rehabilitation movements in the same single glove. For Forearm rehabilitation, a rehabilitation exoskeleton has been developed for pronation and supination movements by using the novel extensor-contractor pneumatic muscle. For the Elbow rehabilitation an elbow rehabilitation exoskeleton was designed which relies on novel two-directional bending actuators with online and offline feedback controllers. Lastly for upper-limb joint is the wrist, we designed a novel all-directional bending actuator by using the moulding bladder to develop the wrist rehabilitation exoskeleton by a single all-directional bending muscle. Finally, a totally portable, power assistive and rehabilitative prototype has been developed using a parallel processing intelligent control chip

Error minimising gradients for improving cerebellar model articulation controller performance

In motion control applications where the desired trajectory velocity exceeds an actuator’s maximum velocity limitations, large position errors will occur between the desired and actual trajectory responses. In these situations standard control approaches cannot predict the output saturation of the actuator and thus the associated error summation cannot be minimised.An adaptive feedforward control solution such as the Cerebellar Model Articulation Controller (CMAC) is able to provide an inherent level of prediction for these situations, moving the system output in the direction of the excessive desired velocity before actuator saturation occurs. However the pre-empting level of a CMAC is not adaptive, and thus the optimal point in time to start moving the system output in the direction of the excessive desired velocity remains unsolved. While the CMAC can adaptively minimise an actuator’s position error, the minimisation of the summation of error over time created by the divergence of the desired and actual trajectory responses requires an additional adaptive level of control.This thesis presents an improved method of training CMACs to minimise the summation of error over time created when the desired trajectory velocity exceeds the actuator’s maximum velocity limitations. This improved method called the Error Minimising Gradient Controller (EMGC) is able to adaptively modify a CMAC’s training signal so that the CMAC will start to move the output of the system in the direction of the excessive desired velocity with an optimised pre-empting level.The EMGC was originally created to minimise the loss of linguistic information conveyed through an actuated series of concatenated hand sign gestures reproducing deafblind sign language. The EMGC concept however is able to be implemented on any system where the error summation associated with excessive desired velocities needs to be minimised, with the EMGC producing an improved output approximation over using a CMAC alone.In this thesis, the EMGC was tested and benchmarked against a feedforward / feedback combined controller using a CMAC and PID controller. The EMGC was tested on an air-muscle actuator for a variety of situations comprising of a position discontinuity in a continuous desired trajectory. Tested situations included various discontinuity magnitudes together with varying approach and departure gradient profiles.Testing demonstrated that the addition of an EMGC can reduce a situation’s error summation magnitude if the base CMAC controller has not already provided a prior enough pre-empting output in the direction of the situation. The addition of an EMGC to a CMAC produces an improved approximation of reproduced motion trajectories, not only minimising position error for a single sampling instance, but also over time for periodic signals

Advancing the Functionality and Wearability of Robotic Hand Orthoses Towards Activities of Daily Living in Stroke Patients

Post stroke rehabilitation is effective when a large number of motor repetitions are provided to patients. However, conventional physical therapy or traditional desktop-size robot aided rehabilitation do not provide sufficient number of repetitions due to cost and logistical barriers. Our vision is to realize a wearable and functional hand orthosis that could be used outside of controlled, clinical settings, thus allowing for more training repetitions. Furthermore, if such a device can prove effective for Activities of Daily Living (ADLs) while actively worn, this can incentivize patients to increase its use, further enhancing rehabilitative effects. However, in order to provide such clinical benefits, the device must be completely wearable without obtrusive features, and intuitive to control even for non-experts. In this thesis, we thus focus on wearability, functionality, and intuitive intent detection technology for a novel hand robot, and assess its performance when used both as a rehabilitative device and an assistive tool. A fully wearable device must deliver meaningful manipulation capability in small and lightweight package. In this context, we investigate the capability of single-actuator devices to assist whole hand movement patterns through a network of exotendons. Our prototypes combine a single linear actuator (mounted on a forearm splint) with a network of exotendons (routed on the surface of a soft glove). We investigate two possible tendon network configurations: one that produces full finger extension (overcoming flexor spasticity) and one that combines proximal flexion with distal extension at each finger. In experiments with stroke survivors, we measure the force levels needed to overcome various levels of spasticity and to open the hand for grasping using the first of these configurations, and qualitatively demonstrate the ability to execute fingertip grasps using the second. Our results support the feasibility of developing future wearable devices able to assist a range of manipulation tasks. In order to further improve the wearability of the device, we propose two designs that provide effective force transmission by increasing moment arms around finger joints. We evaluate the designs with geometric models and experiment using a 3D-printed artificial finger to find force and joint angle characteristics of the suggested structures. We also perform clinical tests with stroke patients to demonstrate the feasibility of the designs. The testing supports the hypothesis that the proposed designs efficiently elicit extension of the digits in patients with spasticity as compared to existing baselines. With the suggested transmission designs, the device can deliver sufficient extension force even when the users have increased muscle tone due to fatigue. The vision of an orthotic device used for ADLs can only be realized if the patients are able to operate the device themselves. However, the field is generally lacking effective methods by which the user can operate the device: such controls must be effective, intuitive, and robust to the wide range of possible impairment patterns. The variety of encountered upper limb impairment patterns in stroke patients means that a single sensing modality, such as electromyography, might not be sufficient to enable controls for a broad range of users. To address this significant gap, we introduce a multimodal sensing and interaction paradigm for an active hand orthosis. In our proof-of-concept implementation, EMG is complemented by other sensing modalities, such as finger bend and contact pressure sensors. We propose multimodal interaction methods that utilize this sensory data as input, and show they can enable tasks for stroke survivors who exhibit different impairment patterns. We then assess the performance of the robotic orthosis for two possible roles: as a therapeutic tool that facilitates device mediated hand exercises to recover neuromuscular function, or as an assistive device for use in everyday activities to aid functional use of the hand. 11 chronic stroke (> 2 years) patients with moderate muscle tone (Modified Ashworth Scale ≤ 2 in upper extremity) engage in a month-long training protocol using the orthosis. Individuals are evaluated using standardized outcome measures, both with and without orthosis assistance. The results highlight the potential for wearable and user-driven robotic hand orthoses to extend the use and training of the affected upper limb after stroke. The advances proposed in this thesis have the potential to enable robotic based hand rehabilitation during daily activities (as opposed to isolated hand exercises with limited upper limb engagement) and over extended periods of time, even in a patient’s home environment. Numerous challenges must still be overcome in order to achieve this vision, related to design (compact devices with easier donning/doffing), control (robust yet intuitive intent inferral), and effectiveness (improved functionality in a wider range of metrics). However, if these challenges can be addressed, wearable robotic devices have the potential to greatly extend the use and training of the affected upper limb after stroke, and help improve the quality of life for a large patient population

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

Touching on elements for a non-invasive sensory feedback system for use in a prosthetic hand

Hand amputation results in the loss of motor and sensory functions, impacting activities of daily life and quality of life. Commercially available prosthetic hands restore the motor function but lack sensory feedback, which is crucial to receive information about the prosthesis state in real-time when interacting with the external environment. As a supplement to the missing sensory feedback, the amputee needs to rely on visual and audio cues to operate the prosthetic hand, which can be mentally demanding. This thesis revolves around finding potential solutions to contribute to an intuitive non-invasive sensory feedback system that could be cognitively less burdensome and enhance the sense of embodiment (the feeling that an artificial limb belongs to one’s own body), increasing acceptance of wearing a prosthesis.A sensory feedback system contains sensors to detect signals applied to the prosthetics. The signals are encoded via signal processing to resemble the detected sensation delivered by actuators on the skin. There is a challenge in implementing commercial sensors in a prosthetic finger. Due to the prosthetic finger’s curvature and the fact that some prosthetic hands use a covering rubber glove, the sensor response would be inaccurate. This thesis shows that a pneumatic touch sensor integrated into a rubber glove eliminates these errors. This sensor provides a consistent reading independent of the incident angle of stimulus, has a sensitivity of 0.82 kPa/N, a hysteresis error of 2.39±0.17%, and a linearity error of 2.95±0.40%.For intuitive tactile stimulation, it has been suggested that the feedback stimulus should be modality-matched with the intention to provide a sensation that can be easily associated with the real touch on the prosthetic hand, e.g., pressure on the prosthetic finger should provide pressure on the residual limb. A stimulus should also be spatially matched (e.g., position, size, and shape). Electrotactile stimulation has the ability to provide various sensations due to it having several adjustable parameters. Therefore, this type of stimulus is a good candidate for discrimination of textures. A microphone can detect texture-elicited vibrations to be processed, and by varying, e.g., the median frequency of the electrical stimulation, the signal can be presented on the skin. Participants in a study using electrotactile feedback showed a median accuracy of 85% in differentiating between four textures.During active exploration, electrotactile and vibrotactile feedback provide spatially matched modality stimulations, providing continuous feedback and providing a displaced sensation or a sensation dispatched on a larger area. Evaluating commonly used stimulation modalities using the Rubber Hand Illusion, modalities which resemble the intended sensation provide a more vivid illusion of ownership for the rubber hand.For a potentially more intuitive sensory feedback, the stimulation can be somatotopically matched, where the stimulus is experienced as being applied on a site corresponding to their missing hand. This is possible for amputees who experience referred sensation on their residual stump. However, not all amputees experience referred sensations. Nonetheless, after a structured training period, it is possible to learn to associate touch with specific fingers, and the effect persisted after two weeks. This effect was evaluated on participants with intact limbs, so it remains to evaluate this effect for amputees.In conclusion, this thesis proposes suggestions on sensory feedback systems that could be helpful in future prosthetic hands to (1) reduce their complexity and (2) enhance the sense of body ownership to enhance the overall sense of embodiment as an addition to an intuitive control system