Flexible Electronics for High-Density EMG Based Signal Acquisition for Upper Limb Myoelectric Prosthesis Control

The research detailed in this thesis is aimed at developing flexible electrodes for high-density control of an upper limb myoelectric prosthesis. Different flexible dry electrode materials (made from doped traditionally non-conductive substrates) were used and compared to titanium (which is the industry standard for EMG electrodes). We determined that conductivity measurements alone, (the current industry standard for characterizing electrical properties of materials), are not sufficient due to their complex impedance. We measured the skin electrode complex impedance and relationship with signal to noise ratio (SNR) and settling time. We show that complex skin electrode impedance is linearly related to the SNR of signals and that complex skin electrode impedance better characterizes the electrical properties of doped, traditionally non-conductive materials for physiological signal acquisition. Next we constructed a flexible high-density array with 128- contact points arranged in an 8 x 16 configuration to cover the entire residual limb. Myoelectric signals, and its relationship to derived time domain features of all 128 channels were extracted and represented as spatio-temporal values as 8 x 16 images to represent the muscle activity map of the residual limb. Thus, a traditional signal-processing problem is converted into an image processing problem. Obtaining High Density (HD) (128 channel) spatio-temporal information has significant merits which include: ability to easily identify the optimum myoelectric recording sites on a residual limb, ability to temporally study the onset and decline of a contraction, predicting the stage of contraction and, finally, ability to implement proportional control and fine motor myoelectric control

Myoelectric Human Computer Interaction Using Reliable Temporal Sequence-based Myoelectric Classification for Dynamic Hand Gestures

To put a computerized device under human control, various interface techniques have been commonly studied in the realm of Human Computer Interaction (HCI) design. What this dissertation focuses on is a myoelectric interface, which controls a device via neuromuscular electrical signals. Myoelectric interface has advanced by recognizing repeated patterns of the signal (pattern recognition-based myoelectric classification). However, when the myoelectric classification is used to extract multiple discrete states within limited muscle sites, there are robustness issues due to external conditions: limb position changes, electrode shifts, and skin condition changes. Examined in this dissertation is the robustness issue, or drop in the performance of the myoelectric classification when the limb position varies from the position where the system was trained. Two research goals outlined in this dissertation are to increase reliability of myoelectric system and to build a myoelectric HCI to manipulate a 6-DOF robot arm with a 1-DOF gripper. To tackle the robustness issue, the proposed method uses dynamic motions which change their poses and configuration over time. The method assumes that using dynamic motions is more reliable, vis-a-vis the robustness issues, than using static motions. The robustness of the method is evaluated by choosing the training sets and validation sets at different limb positions. Next, an HCI system manipulating a 6-DOF robot arm with a 1-DOF gripper is introduced. The HCI system includes an inertia measurement unit to measure the limb orientation, as well as EMG sensors to acquire muscle force and to classify dynamic motions. Muscle force and the orientation of a forearm are used to generate velocity commands. Classified dynamic motions are used to change the manipulation modes. The performance of the myoelectric interface is measured in terms of real-time classification accuracy, path efficiency, and time-related measures. In conclusion, this dissertation proposes a reliable myoelectric classification and develops a myoelectric interface using the proposed classification method for an HCI application. The robustness of the proposed myoelectric classification is verified as compared to previous myoelectric classification approaches. The usability of the developed myoelectric interface is compared to a well-known interface

Classification de mouvements fantômes du membre supérieur chez des amputés huméraux à l'aide de mesures électromyographiques et cinématiques

RÉSUMÉ La perte d’un membre supérieur engendre de nombreux déficits fonctionnels pour l’amputé dans sa vie de tous les jours. En effet, la plupart des activités de la vie quotidienne, telles qu’attacher ses souliers ou ouvrir une bouteille, sont complexes et difficiles à réaliser avec un seul bras fonctionnel. Les impacts de ces déficits augmentent à mesure que le niveau d’amputation est plus haut au niveau du bras. Pour toutes ces personnes, les nombreuses avancées dans le domaine des prothèses myoélectriques, c’est-à-dire commandées par l’activité musculaire des muscles restants après l’amputation, sont encourageantes parce qu’elles permettent d’entretenir l’espoir d’une prothèse à la commande intuitive. Un phénomène particulier, présent chez la majorité des amputés, est celui des sensations au membre fantôme. Ces sensations peuvent se manifester sous plusieurs formes : thermiques, douleurs, mobilités. Les mobilités du membre fantôme sont particulièrement intéressantes pour le développement des prothèses myoélectriques étant donné qu’il a été démontré que les mouvements fantômes produisent une activité électromyographique (EMG) au niveau du membre amputé. Cependant, les études s’intéressant à la détection des mouvements fantômes ont enregistré l’activité EMG provenant de muscles difficilement intégrables dans l’emboiture d’une prothèse myoélectriques, tels que ceux du dos, du torse et de l’épaule. La présente étude se concentre sur la classification des mouvements fantômes chez les amputés huméraux à l’aide de l’EMG dans l’optique de développer une prothèse myoélectrique commandée par reconnaissance de formes. Cinq adultes ayant subi une amputation unilatérale humérale suite à un trauma ont participé à cette étude. L’activité EMG des participants a été enregistrée exclusivement autour de leur moignon. Durant les enregistrements, il était demandé aux participants de réaliser l’un des principaux mouvements fantômes du membre supérieur : la flexion ou l’extension du coude, la pronation ou la supination de l’avant-bras, la flexion ou l’extension du poignet, l’ouverture ou la fermeture de la main et le repos. Chaque mouvement fantôme devait être réalisé symétriquement à l’aide du bras sain et la cinématique de ce dernier a été enregistrée à l’aide d’un système d’analyse du mouvement. Dix caractéristiques (ou « features » en anglais) temporels ont été extraites des signaux EMG et utilisées pour entraîner un réseau de neurones permettant de classifier les mouvements fantômes du membre supérieur.----------ABSTRACT Upper limb amputation creates substantial functional deficits for the amputee. Indeed, most activities of daily living, such as tying shoelaces or opening a bottle, are complex and hard to achieve with only one functional arm. These functional impairments increase as the level of amputation is higher up the arm. For these people, recent advances in the field of myoelectric prostheses, i.e. controlled by the activity of the remaining muscles after amputation, are encouraging because they help maintain the hope of an intuitive prosthesis. A particular phenomenon, occurring in the majority of amputees, is the presence of phantom limb sensations. Phantom limb sensations are of many types: thermal, pain, and mobility. Phantom limb mobilities are particularly interesting for the development of myoelectric prostheses since it has been shown that they produce an electromyographic (EMG) activity in the amputated limb. However, the studies focusing on the detection of phantom movements recorded EMG from muscles that are hard to integrate into the socket element of a myoelectric prosthesis, such as the back, chest and shoulder muscles. This study focuses on the classification of phantom movements in transhumeral amputees using EMG in the context of developing a myoelectric prosthesis controlled by pattern recognition. Five adults who underwent unilateral humeral amputation following a trauma participated in this study. The EMG activity of the participants was recorded exclusively around their stump. During the recordings, participants were asked to perform one of the main upper limb phantom movements: flexion or extension of the elbow, pronation or supination of the forearm, flexion or extension of the wrist, opening or closing the hand and rest. Each phantom movement was to be made symmetrical with the unaffected arm and the kinematics of the latter was recorded using a motion analysis system. Ten time-domain features were extracted from the EMG signals and used to train a neural network to classify the phantom limb movements. The performance of the classifier was evaluated based on the number of movements studied and an optimal set of four EMG features was determined. The impact of kinematic information on the classification performance was also evaluated. The accuracy of the classification varies from one amputee to another, but some trends are common: performance decreases if the number of degrees of freedom considered in the classification increases and/or if the phantom movements become more distal. Moreover, the optimal set of four EMG features provided a performance equivalent to that obtained with all ten EMG features. The addition of the kinematic information improved classification accuracy for all amputees