Estimation of Targeted-Reaching-Positions by Around-Shoulder Muscle Activities and Images from an Action Camera for Trans-Humeral Prosthesis Control

For trans-humeral amputation, daily living tasks requiring bimanual coordination, such as lifting up a box, are most difficult, hence most urgent for a trans-humeral prosthesis to fulfill. However, in studies reported on trans-humeral prosthetic control, the states of the target objects, such as their size, relative pose and position, which are important for any real reaching and manipulation tasks, have not been taken into account. In our previous study, for a box lifting-up task, we investigated the possibility of using around-shoulder EMG (electromyogram), for identifying target-reaching-positions for the boxes with different configurations (relative pose and position). However, with only the around-shoulder EMG, it is impossible for the system to guide the prosthesis to hold or grasp target objects precisely and fast sufficiently. The purpose of this study is to explore the possibility of using both the image information from an action camera and around-shoulder EMG, to identify targeted-reaching-positions for various box configurations more accurately and more rapidly. Multinomial logistic regression was employed to realize both information integration of, and the target-reaching-position identification. A set of experiments were conducted. As a result, an average classification rate of 75.1% could be achieved for various box configurations

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

A non-invasive human-machine interfacing framework for investigating dexterous control of hand muscles

The recent fast development of virtual reality and robotic assistive devices enables to augment the capabilities of able-body individuals as well as to overcome the motor missing functions of neurologically impaired or amputee individuals. To control these devices, movement intentions can be captured from biological structures involved in the process of motor planning and execution, such as the central nervous system (CNS), the peripheral nervous system (in particular the spinal motor neurons) and the musculoskeletal system. Thus, human-machine interfaces (HMI) enable to transfer neural information from the neuro-muscular system to machines. To prevent any risks due to surgical operations or tissue damage in implementing these HMIs, a non-invasive approach is proposed in this thesis. In the last five decades, surface electromyography (sEMG) has been extensively explored as a non-invasive source of neural information. EMG signals are constituted by the mixed electrical activity of several recruited motor units, the fundamental components of muscle contraction. High-density sEMG (HD-sEMG) with the use of blind source separation methods enabled to identify the discharge patterns of many of these active motor units. From these decomposed discharge patterns, the net common synaptic input (CSI) to the corresponding spinal motor neurons was quantified with cross-correlation in the time and frequency domain or with principal component analysis (PCA) on one or few muscles. It has been hypothesised that this CSI would result from the contribution of spinal descending commands sent by supra-spinal structures and afferences integrated by spinal interneurons. Another motor strategy implying the integration of descending commands at the spinal level is the one regarding the coordination of many muscles to control a large number of articular joints. This neurophysiological mechanism was investigated by measuring a single EMG amplitude per muscle, thus without the use of HD-sEMG and decomposition. In this case, the aim was to understand how the central nervous system (CNS) could control a large set of muscles actuating a vast set of combinations of degrees of freedom in a modular way. Thus, time-invariant patterns of muscle coordination, i.e. muscle synergies , were found in animals and humans from EMG amplitude of many muscles, modulated by time-varying commands to be combined to fulfil complex movements. In this thesis, for the first time, we present a non-invasive framework for human-machine interfaces based on both spinal motor neuron recruitment strategy and muscle synergistic control for unifying the understanding of these two motor control strategies and producing control signals correlated to biomechanical quantities. This implies recording both from many muscles and using HD-sEMG for each muscle. We investigated 14 muscles of the hand, 6 extrinsic and 8 intrinsic. The first two studies, (in Chapters 2 and 3, respectively) present the framework for CSI quantification by PCA and the extraction of the synergistic organisation of spinal motor neurons innervating the 14 investigated muscles. For the latter analysis, in Chapter 3, we proposed the existence of what we named as motor neuron synergies extracted with non-negative matrix factorisation (NMF) from the identified motor neurons. In these first two studies, we considered 7 subjects and 7 grip types involving differently all the four fingers in opposition with the thumb. In the first study, we found that the variance explained by the CSI among all motor neuron spike trains was (53.0 ± 10.9) % and its cross-correlation with force was 0.67 ± 0.10, remarkably high with respect to previous findings. In the second study, 4 motor neuron synergies were identified and associated with the actuation of one finger in opposition with the thumb, finding even higher correlation values with force (over 0.8) for each synergy associated with a finger during the actuation of the relative finger. In Chapter 4, we then extended the set of analysed movements in a vast repertoire of gestures and repeated the analysis of Chapter 3 by finding a different synergistic organisation during the execution of tens of tasks. We divided the contribution among extrinsic and intrinsic muscles and we found that intrinsic better enable single-finger spatial discrimination, while no difference was found in regression of joint angles by dividing the two groups of muscles. Finally, in Chapter 5 we proposed the techniques of the previous chapters for cases of impairment due both to amputation and stroke. We analysed one case of pre and post rehabilitation sessions of a trans-humeral amputee, the case of a post-stroke trans-radial amputee and three cases of acute stroke, i.e. less than one month from the stroke event. We present future perspectives (Chapter 6) aimed to design and implement a platform for both rehabilitation monitoring and myoelectric control. Thus, this thesis provides a bridge between two extensively studied motor control mechanisms, i.e. motor neuron recruitment and muscle synergies, and proposes this framework as suitable for rehabilitation monitoring and control of assistive devices.Open Acces

Treatments of proximal upper extremity amputations : utility of hand allotransplantation versus myoelectric prostheses

Les amputations d’un membre supérieur sont non seulement dévastatrices pour le bien-être physique, psychologique et social des patients, mais elles comportent également des répercussions financières importantes pour l’individu et le système de santé. Les allotransplantations de tissus composites vascularisés ont été proposées en tant que solution permettant de rétablir la forme et la fonction au détriment d’une immunosuppression à vie et d’un taux élevé de rejet chronique. Les prothèses myoélectriques combinent l’expertise chirurgicale avec les avancées technologiques pour réhabiliter les fonctions motrices d’un moignon amputé, mais elles demeurent limitées par un taux élevé d’abandon et des coûts importants. Dans les systèmes de santé avec des ressources limitées, les dirigeants ont la tâche complexe de partager équitablement l’allocation de ressources entre plusieurs maladies et interventions. Dans le domaine de l’économie de la santé, les analyses de type coût-bénéfice ont été développées pour répondre à ces questions. Les mesures d’utilité doivent incorporer l’impact que le traitement suscite sur l’espérance de vie et la qualité de vie. Ces utilités sont ensuite rapportées en fonction du coût, ce qui permet aux dirigeants de la santé de déterminer dans quels traitements il serait préférable d’investir les ressources. Dans cette thèse, nous proposons un modèle pour étudier les coûts-utilité des allotransplantations de la main et des prothèses myoélectriques. Pour commencer, une étude pilote a été effectuée sur les amputations du pouce traitées avec des lambeaux libres de l’orteil, ce qui nous a permis de confirmer la faisabilité des questionnaires d’utilité conçus. Par la suite, les utilités ont été mesurées dans une population d’amputés du membre supérieur, de patients réimplantés proximalement et de contrôles en santé. Les résultats démontrent que 1) les patients réimplantés rapportent la meilleure utilité avec les prothèses myoélectriques, 2) les amputés unilatéraux préfèrent significativement les prothèses myoélectriques également, et 3) aucune différence n’a été recelée entre les deux traitements chez les amputés bilatéraux. Au final, une analyse des coûts-bénéfices a été effectuée dans le contexte du système de santé canadien, démontrant que le traitement des patients amputés unilatéralement avec des prothèses myoélectriques permettrait de sauver davantage de coûts, alors que l’écart en épargnes monétaires se rétrécit pour les amputés bilatéraux traités avec une allotransplantation ou une prothèse. Avec les résultats rapportés dans cette thèse, nous pouvons proposer une mise à jour des indications de traitements pour les patients avec une amputation du membre supérieur. Basé sur l’analyse de type coût-utilité, nous concluons que les amputés unilatéraux sont de meilleurs candidats pour des prothèses myoélectriques, alors que les deux traitements sont encore adéquats pour les amputations bilatérales.Amputations of the upper extremity are not only devastating for the patient’s physical, psychological and social well-being, but they also yield significant financial repercussions to the individual and the healthcare system. Vascularized composite allotransplantations of the upper extremity were proposed as a solution to restore form and function, albeit to the detriment of lifelong immunosuppression and high rates of chronic rejection. New-generation myoelectric prostheses combine surgical prowess with technological refinements to rehabilitate motor functions of the amputated stump, but remain plagued by high rates of abandonment and significant costs. In healthcare systems wherein resources are limited, financial regulators have the difficult task of proposing an equitable divide of resource allocations between a multitude of diseases and interventions. In the field of health economics, cost-benefit analyses were developed to assist in this decision-making process. Utility outcome measures need to encompass the impact that a treatment elicits on life expectancy and quality of life. Comparison of utilities of different interventions as a function of cost further indicates which route healthcare regulators should partake. In this thesis, we propose a model to study cost-utilities of hand allotransplantation and myoelectric prostheses. To begin, a pilot study was performed on thumb amputations treated with free toe flaps, which allowed to confirm the feasibility of the utility questionnaires that we developed. Afterwards, utilities and quality adjusted life years were measured in a population of upper extremity amputees, proximally replanted patients and healthy controls. Findings demonstrated that 1) replanted patients reported the highest utility outcomes for myoelectric prostheses, 2) unilateral amputees significantly preferred myoelectric prostheses as well, and 3) no significant preference between both interventions was obtained in patients with bilateral amputations. Finally, a cost-benefit analysis was performed in the context of the Canadian healthcare system, demonstrating that significant savings can be achieved with treatment of unilateral amputations with myoelectric prostheses, whereas the gap in cost savings between both treatment groups becomes less significant in bilateral amputees. With the findings reported in this thesis, we can propose an update of the indications for treatment in patients with upper extremity amputations. From the perspective of cost-utility analyses, we conclude that unilateral amputees are better candidates for myoelectric prostheses, and that both treatments can still be offered in cases of bilateral amputations

The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses

Brachial Plexus Injury

In this book, specialists from different countries and continents share their knowledge and experience in brachial plexus surgery. It discusses the different types of brachial plexus injury and advances in surgical treatments

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

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Wearable Technology Supported Home Rehabilitation Services in Rural Areas:– Emphasis on Monitoring Structures and Activities of Functional Capacity Handbook

The sustainability of modern healthcare systems is under threat. – the ageing of the population, the prevalence of chronic disease and a need to focus on wellness and preventative health management, in parallel with the treatment of disease, pose significant social and economic challenges. The current economic situation has made these issues more acute. Across Europe, healthcare expenditure is expected to rice to almost 16% of GDP by 2020. (OECD Health Statistics 2018). Coupled with a shortage of qualified personnel, European nations are facing increasing challenges in their ability to provide better-integrated and sustainable health and social services. The focus is currently shifting from treatment in a care center to prevention and health promotion outside the care institute. Improvements in technology offers one solution to innovate health care and meet demand at a low cost. New technology has the potential to decrease the need for hospitals and health stations (Lankila et al., 2016. In the future the use of new technologies – including health technologies, sensor technologies, digital media, mobile technology etc. - and digital services will dramatically increase interaction between healthcare personnel and customers (Deloitte Center for Health Solutions, 2015a; Deloitte Center for Health Solutions 2015b). Introduction of technology is expected to drive a change in healthcare delivery models and the relationship between patients and healthcare providers. Applications of wearable sensors are the most promising technology to aid health and social care providers deliver safe, more efficient and cost-effective care as well as improving people’s ability to self-manage their health and wellbeing, alert healthcare professionals to changes in their condition and support adherence to prescribed interventions. (Tedesco et al., 2017; Majumder et al., 2017). While it is true that wearable technology can change how healthcare is monitored and delivered, it is necessary to consider a few things when working towards the successful implementation of this new shift in health care. It raises challenges for the healthcare systems in how to implement these new technologies, and how the growing amount of information in clinical practice, integrates into the clinical workflows of healthcare providers. Future challenges for healthcare include how to use the developing technology in a way that will bring added value to healthcare professionals, healthcare organizations and patients without increasing the workload and cost of the healthcare services. For wearable technology developers, the challenge will be to develop solutions that can be easily integrated and used by healthcare professionals considering the existing constraints. This handbook summarizes key findings from clinical and laboratory-controlled demonstrator trials regarding wearables to assist rehabilitation professionals, who are planning the use of wearable sensors in rehabilitation processes. The handbook can also be used by those developing wearable sensor systems for clinical work and especially for use in hometype environments with specific emphasis on elderly patients, who are our major health care consumers

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare