Industrial robots are delivering more and more manipulation services in manufacturing. However, when the task is complex, it is difficult to programme a robot to fulfil all the requirements because even a relatively simple task such as a peg-in-hole insertion contains many uncertainties, e.g. clearance, initial grasping position and insertion path. Humans, on the other hand, can deal with these variations using their vision and haptic feedback. Although humans can adapt to uncertainties easily, most of the time, the skilled based performances that relate to their tacit knowledge cannot be easily articulated. Even though the automation solution may not fully imitate human motion since some of them are not necessary, it would be useful if the skill based performance from a human could be firstly interpreted and modelled, which will then allow it to be transferred to the robot. This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Recently, Learning from Demonstration (LfD) is gaining interest as a framework to transfer skills from human teacher to robot using probability encoding approaches to model observations and state transition uncertainties. In close or actual contact manipulation tasks, it is difficult to reliabley record the state-action examples without interfering with the human senses and activities. Therefore, wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. Firstly to track human motions accurately and reliably in a defined 3-dimensional workspace, a hybrid system of Vicon and IMUs is proposed to compensate for the known limitations of the individual system. The data fusion method was able to overcome occlusion and frame flipping problems in the two camera Vicon setup and the drifting problem associated with the IMUs. The results indicated that occlusion and frame flipping problems associated with Vicon can be mitigated by using the IMU measurements. Furthermore, the proposed method improves the Mean Square Error (MSE) tracking accuracy range from 0.8˚ to 6.4˚ compared with the IMU only method. Secondly, to record haptic feedback from a teacher without physically obstructing their interactions with the workpiece, wearable surface electromyography (sEMG) armbands were used as an indirect method to indicate contact feedback during manual manipulations. A muscle-force model using a Time Delayed Neural Network (TDNN) was built to map the sEMG signals to the known contact force. The results indicated that the model was capable of estimating the force from the sEMG armbands in the applications of interest, namely in peg-in-hole and beater winding tasks, with MSE of 2.75N and 0.18N respectively. Finally, given the force estimation and the motion trajectories, a Hidden Markov Model (HMM) based approach was utilised as a state recognition method to encode and generalise the spatial and temporal information of the skilled executions. This method would allow a more representative control policy to be derived. A modified Gaussian Mixture Regression (GMR) method was then applied to enable motions reproduction by using the learned state-action policy. To simplify the validation procedure, instead of using the robot, additional demonstrations from the teacher were used to verify the reproduction performance of the policy, by assuming human teacher and robot learner are physical identical systems. The results confirmed the generalisation capability of the HMM model across a number of demonstrations from different subjects; and the reproduced motions from GMR were acceptable in these additional tests. The proposed methodology provides a framework for producing a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute. The implication to industry is reduced efforts and time in programming the robots for applications where human skilled performances are required to cope robustly with various uncertainties during tasks execution.

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.

L’objectif de cette thèse a été d’analyser l’effet de l’exercice physique réalisé sur plateforme vibrante (whole-body vibration, WBV) sur l’activité musculaire des membres inférieurs, de développer des outils d’analyse méthodologiques et de proposer des recommandations pratiques d’utilisation. Deux études méthodologiques ont été menées pour identifier la méthode optimale permettant de traiter les signaux d'électromyographie de surface (sEMG) recueillis pendant la vibration et d'analyser l'influence de la méthode de normalisation de l'activité sEMG. Une troisième étude visait à mieux comprendre si les pics sEMG observés dans le spectre de puissance du signal contiennent des artéfacts de mouvement et/ou de l'activité musculaire réflexe. Les trois études suivantes avaient pour but de quantifier l’effet de la WBV sur l’activité musculaire en fonction de différents paramètres tels que, la fréquence de vibration, l'amplitude de la plateforme, une charge supplémentaire, le type de plateforme, l'angle articulaire du genou, et la condition physique du sujet. En outre, l'objectif a été de déterminer l'accélération verticale minimale permettant de stimuler au mieux l'activité musculaire des membres inférieurs. En résumé, les recherches menées au cours de cette thèse fournissent des solutions pour de futures études sur : i) comment supprimer les pics dans le spectre du signal sEMG et, ii) comment normaliser l'activité musculaire pendant un exercice WBV. Enfin, les résultats de cette thèse apportent à la littérature scientifique de nouvelles recommandations pratiques liées à l’utilisation des plateformes vibrantes à des fins d’exercice physique. / The aim of this thesis was to analyze the effect of whole-body vibration (WBV) exercise on lower limb muscle activity and to give methodological implications and practical applications. Two methodological studies were conducted that served to evaluate the optimal method to process the surface electromyography (sEMG) signals during WBV exercise and to analyze the influence of the normalization method on the sEMG activity. A third study aimed to gain insight whether the isolated spikes in the sEMG spectrum contain motion artifacts and/or reflex activity. The subsequent three investigations aimed to explore how the muscle activity is affected by WBV exercise, with a particular focus on the vibration frequency, platform amplitude, additional loading, platform type, knee flexion angle, and the fitness status of the WBV user. The final goal was to evaluate the minimal required vertical acceleration to stimulate the muscle activity of the lower limbs. In summary, the research conducted for this thesis provides implication for future investigations on how to delete the excessive spikes in the sEMG spectrum and how to normalize the sEMG during WBV. The outcomes of this thesis add to the current literature in providing practical applications for exercising on a WBV platform.