Improved medical diagnosis and treatment can be achieved by combining modern technologies with well-developed methods. This dissertation aims for deriving new methods that facilitate the use of inertial measurement units and learning control techniques in biomedical systems and at demonstrating how advanced diagnosis and treatment systems can be designed by incorporating these technologies. To this end, a modular set of novel methods for inertial realtime gait analysis is proposed. This includes methods for the detection of characteristic gait events as well as for realtime assessment of the foot orientation. Moreover, methods for joint axis and position estimation are derived that exploit the kinematic constraints induced by the joint, and methods for flexion/extension joint angle measurement are proposed. All of these methods avoid the use of magnetometers and can therefore be used indoors as well as in the proximity of ferromagnetic material and magnetic disturbances. Furthermore, they supersede precise sensor mounting requirements as well as restrictive calibration protocols and automatically adjust themselves to the user. For each of the new methods, a practical proof of concept is provided by means of gait experiments with healthy subjects, stroke patients, or transfemoral amputees. Just as inertial sensing, Iterative Learning Control (ILC) is considered a promising tool for biomedical application systems by a growing number of researchers. This dissertation addresses the fact that classic ILC theory is technically too restrictive for some of these applications and extends the classic ILC design in the lifted-systems framework to the class of repetitive trajectory tracking tasks with variable pass length. Two standard learning laws are considered. The maximum-pass-length error is introduced as a useful concept for convergence analysis of variable-pass-length systems, and necessary and sufficient conditions for monotonic convergence of this error are derived. All results are summarized in a set of practical control design guidelines. The potential of ILC for biomedical systems is then demonstrated using the example of a continuous blood pressure measurement technique that requires precise control of the blood flow through a superficial artery. This dissertation demonstrates that the controller performance, and thus the measurement accuracy, can be improved by exploiting the repetitive nature of the control problem. A learning cascaded controller is designed and evaluated experimentally. In a second application example, methods from inertial gait analysis and ILC are combined to propose an adaptive system for improved treatment of the drop foot syndrome via functional electrical stimulation (FES) of the peroneal nerve. A novel three-electrodes setup and a piecewise linear controller output mapping are proposed to provide two independent FES parameters, which are manipulated by a decentralized ILC scheme to control the pitch and roll angle of the paretic foot during swing phase. Experiments with stroke patients demonstrate that this closed-loop approach allows the system to quickly adjust the FES to the patient's needs and to compensate changes in muscular tone and fatigue automatically.
Learning control and inertial realtime gait analysis in biomedical applications.
Verbesserung von Diagnose und Behandlung durch automatische Anpassung und Regelung
improving diagnosis and treatment by automatic adaption and feedback control
Lernende Regelung und inertiale Echtzeit-Ganganalyse in biomedizinischen Anwendungen
2016
Miscellaneous
Electronic Resource
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