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

Artificial neural network EMG classifier for functional hand grasp movements prediction

To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution

Assistive Device For Elderly Rehabilitation: Signal Processing Techniques.

Ph.DDOCTOR OF PHILOSOPH

Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

Non-linear actuators and simulation tools for rehabilitation devices

Mención Internacional en el título de doctorRehabilitation robotics is a field of research that investigates the applications of robotics in motor function therapy for recovering the motor control and motor capability. In general, this type of rehabilitation has been found effective in therapy for persons suffering motor disorders, especially due to stroke or spinal cord injuries. This type of devices generally are well tolerated by the patients also being a motivation in rehabilitation therapy. In the last years the rehabilitation robotics has become more popular, capturing the attention at various research centers. They focused on the development more effective devices in rehabilitation therapy, with a higher acceptance factor of patients tacking into account: the financial cost, weight and comfort of the device. Among the rehabilitation devices, an important category is represented by the rehabilitation exoskeletons, which in addition to the human skeletons help to protect and support the external human body. This became more popular between the rehabilitation devices due to the easily adapting with the dynamics of human body, possibility to use them such as wearable devices and low weight and dimensions which permit easy transportation. Nowadays, in the development of any robotic device the simulation tools play an important role due to their capacity to analyse the expected performance of the system designed prior to manufacture. In the development of the rehabilitation devices, the biomechanical software which is capable to simulate the behaviour interaction between the human body and the robotics devices, play an important role. This helps to choose suitable actuators for the rehabilitation device, to evaluate possible mechanical designs, and to analyse the necessary controls algorithms before being tested in real systems. This thesis presents a research proposing an alternative solution for the current systems of actuation on the exoskeletons for robotic rehabilitation. The proposed solution, has a direct impact, improving issues like device weight, noise, fabrication costs, size an patient comfort. In order to reach the desired results, a biomechanical software based on Biomechanics of Bodies (BoB) simulator where the behaviour of the human body and the rehabilitation device with his actuators can be analysed, was developed. In the context of the main objective of this research, a series of actuators have been analysed, including solutions between the non-linear actuation systems. Between these systems, two solutions have been analysed in detail: ultrasonic motors and Shape Memory Alloy material. Due to the force - weight characteristics of each device (in simulation with the human body), the Shape Memory Alloy material was chosen as principal actuator candidate for rehabilitation devices. The proposed control algorithm for the actuators based on Shape Memory Alloy, was tested over various configurations of actuators design and analysed in terms of energy eficiency, cooling deformation and movement. For the bioinspirated movements, such as the muscular group's biceps-triceps, a control algorithm capable to control two Shape Memory Alloy based actuators in antagonistic movement, has been developed. A segmented exoskeleton based on Shape Memory Alloy actuators for the upper limb evaluation and rehabilitation therapy was proposed to demosntrate the eligibility of the actuation system. This is divided in individual rehabilitation devices for the shoulder, elbow and wrist. The results of this research was tested and validated in the real elbow exoskeleton with two degrees of freedom developed during this thesis.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Eduardo Rocón de Lima.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Martin Stoele

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