The Use of Skeletal Muscle to Amplify Action Potentials in Transected Peripheral Nerves

Upper limb amputees suffer with problems associated with control and attachment of prostheses. Skin-surface electrodes placed over the stump, which detect myoelectric signals, are traditionally used to control hand movements. However, this method is unintuitive, the electrodes lift-off, and signal selectivity can be an issue. One solution to these limitations is to implant electrodes directly on muscles. Another approach is to implant electrodes directly into the nerves that innervate the muscles. A significant challenge with both solutions is the reliable transmission of biosignals across the skin barrier. In this thesis, I investigated the use of implantable muscle electrodes in an ovine model using myoelectrodes in combination with a bone-anchor, acting as a conduit for signal transmission. High-quality readings were obtained which were significantly better than skin-surface electrode readings. I further investigated the effect of electrode configurations to achieve the best signal quality. For direct recording from nerves, I tested the effect of adsorbed endoneural basement membrane proteins on nerve regeneration in vivo using microchannel neural interfaces implanted in rat sciatic nerves. Muscle and nerve signal recordings were obtained and improvements in sciatic nerve function were observed. Direct skeletal fixation of a prosthesis to the amputation stump using a bone-anchor has been proposed as a solution to skin problems associated with traditional socket-type prostheses. However, there remains a concern about the risk of infection between the implant and skin. Achieving a durable seal at this interface is therefore crucial, which formed the final part of the thesis. Bone-anchors were optimised for surface pore size and coatings to facilitate binding of human dermal fibroblasts to optimise skin-implant seal in an ovine model. Implants silanised with Arginine-Glycine-Aspartic Acid experienced significantly increased dermal tissue infiltration. This approach may therefore improve the soft tissue seal, and thus success of bone-anchored implants. By addressing both the way prostheses are attached to the amputation stump, by way of direct skeletal fixation, as well as providing high fidelity biosignals for high-level intuitive prosthetic control, I aim to further the field of limb loss rehabilitation

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

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

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Development of an Upper Limb Neuroprosthesis: Conquering Weakness and Fatigue

Neuroprosthetics are devices that substitute for or supplant motor, sensory or cognitive modalities damaged as a result of spinal cord injury or stroke. Functional electrical stimulation (FES) neuroprosthetics utilize artificial stimulation to restore motor function in paralyzed muscles, where control exerted by higher nervous system centers over muscle may be impaired. Although promising, FES has failed to gain widespread acceptance due in part to weak contraction strength and rapid fatigue observed with artificial stimulation. This dissertation documents an attempt to create an upper limb FES neuroprosthetic and subsequently to address the issues of weakness and fatigue. To exploit the capabilities of the musculoskeletal system the neural drive to muscle first must be decoded. Decoding the neural drive for specific movements has been approached using either a deterministic (engineering) or machine learning model. While a deterministic model accounts for all components of a limb, number of joints, degrees of freedom, limb length, muscle length, etc, machine learning characterizes the relationship between select variables, in this case whole muscle electromyographic data (EMG) and limb kinematics. Ultimately, the output of both approaches is used to predict the neural drive required to generate movements. In this study we first attempt to build an upper limb FES neuroprosthetic. Utilizing machine learning, we characterize the relationship between limb kinematics and EMG. Then, predict EMG based solely on limb kinematics. Finally, stimulation pulses were generated and delivered via intramuscular electrodes to produce movement. Additionally, to address force generation we hypothesized that due to the distributed nature of motor axons within a muscle stimulating with multiple spatially distributed electrodes would activate a larger muscle volume thus generating additional force. This in turn would facilitate load sharing among muscle fibers, and reduce fatigue. To evaluate fatigue we compared interleaved and synchronous patterns of stimulation as well as single electrode vs multiple electrode stimulation. We approached these questions and aims with a combination of strategies and techniques including machine learning, implantation of stimulating electrodes in a non-human primate model and finally human subjects. While machine learning provided EMG predictions with high R values, we were unable to generate substantive movements activating all the muscle in a complete joint system. However, we were able to generate movements stimulating a single muscle in an intact joint system. We found that single electrode force could be augmented with multiple electrodes. Additional results indicate that multiple electrode stimulation was less fatiguing than single electrode stimulation. Interleaved stimulation however, did not result in less fatigue than synchronous stimulation

Novel Bidirectional Body - Machine Interface to Control Upper Limb Prosthesis

Objective. The journey of a bionic prosthetic user is characterized by the opportunities and limitations involved in adopting a device (the prosthesis) that should enable activities of daily living (ADL). Within this context, experiencing a bionic hand as a functional (and, possibly, embodied) limb constitutes the premise for mitigating the risk of its abandonment through the continuous use of the device. To achieve such a result, different aspects must be considered for making the artificial limb an effective support for carrying out ADLs. Among them, intuitive and robust control is fundamental to improving amputees’ quality of life using upper limb prostheses. Still, as artificial proprioception is essential to perceive the prosthesis movement without constant visual attention, a good control framework may not be enough to restore practical functionality to the limb. To overcome this, bidirectional communication between the user and the prosthesis has been recently introduced and is a requirement of utmost importance in developing prosthetic hands. Indeed, closing the control loop between the user and a prosthesis by providing artificial sensory feedback is a fundamental step towards the complete restoration of the lost sensory-motor functions. Within my PhD work, I proposed the development of a more controllable and sensitive human-like hand prosthesis, i.e., the Hannes prosthetic hand, to improve its usability and effectiveness. Approach. To achieve the objectives of this thesis work, I developed a modular and scalable software and firmware architecture to control the Hannes prosthetic multi-Degree of Freedom (DoF) system and to fit all users’ needs (hand aperture, wrist rotation, and wrist flexion in different combinations). On top of this, I developed several Pattern Recognition (PR) algorithms to translate electromyographic (EMG) activity into complex movements. However, stability and repeatability were still unmet requirements in multi-DoF upper limb systems; hence, I started by investigating different strategies to produce a more robust control. To do this, EMG signals were collected from trans-radial amputees using an array of up to six sensors placed over the skin. Secondly, I developed a vibrotactile system to implement haptic feedback to restore proprioception and create a bidirectional connection between the user and the prosthesis. Similarly, I implemented an object stiffness detection to restore tactile sensation able to connect the user with the external word. This closed-loop control between EMG and vibration feedback is essential to implementing a Bidirectional Body - Machine Interface to impact amputees’ daily life strongly. For each of these three activities: (i) implementation of robust pattern recognition control algorithms, (ii) restoration of proprioception, and (iii) restoration of the feeling of the grasped object's stiffness, I performed a study where data from healthy subjects and amputees was collected, in order to demonstrate the efficacy and usability of my implementations. In each study, I evaluated both the algorithms and the subjects’ ability to use the prosthesis by means of the F1Score parameter (offline) and the Target Achievement Control test-TAC (online). With this test, I analyzed the error rate, path efficiency, and time efficiency in completing different tasks. Main results. Among the several tested methods for Pattern Recognition, the Non-Linear Logistic Regression (NLR) resulted to be the best algorithm in terms of F1Score (99%, robustness), whereas the minimum number of electrodes needed for its functioning was determined to be 4 in the conducted offline analyses. Further, I demonstrated that its low computational burden allowed its implementation and integration on a microcontroller running at a sampling frequency of 300Hz (efficiency). Finally, the online implementation allowed the subject to simultaneously control the Hannes prosthesis DoFs, in a bioinspired and human-like way. In addition, I performed further tests with the same NLR-based control by endowing it with closed-loop proprioceptive feedback. In this scenario, the results achieved during the TAC test obtained an error rate of 15% and a path efficiency of 60% in experiments where no sources of information were available (no visual and no audio feedback). Such results demonstrated an improvement in the controllability of the system with an impact on user experience. Significance. The obtained results confirmed the hypothesis of improving robustness and efficiency of a prosthetic control thanks to of the implemented closed-loop approach. The bidirectional communication between the user and the prosthesis is capable to restore the loss of sensory functionality, with promising implications on direct translation in the clinical practice