Robot-assisted gait self-training: assessing the level achieved

This paper presents the technological status of robot-assisted gait self-training under real clinical environment conditions. A successful rehabilitation after surgery in hip endoprosthetics comprises self-training of the lessons taught by physiotherapists. While doing this, immediate feedback to the patient about deviations from the expected physiological gait pattern during training is important. Hence, the Socially Assistive Robot (SAR) developed for this type of training employs task-specific, user-centered navigation and autonomous, real-time gait feature classification techniques to enrich the self-training through companionship and timely corrective feedback. The evaluation of the system took place during user tests in a hospital from the point of view of technical benchmarking, considering the therapists’ and patients’ point of view with regard to training motivation and from the point of view of initial findings on medical efficacy as a prerequisite from an economic perspective. In this paper, the following research questions were primarily considered: Does the level of technology achieved enable autonomous use in everyday clinical practice? Has the gait pattern of patients who used additional robot-assisted gait self-training for several days been changed or improved compared to patients without this training? How does the use of a SAR-based self-training robot affect the motivation of the patients

Localization of Mobile Robot Using Multiple Sensors

Tato práce se věnuje celoživotnímu určování polohy mobilního robotu, který je vybavený různými senzory. Informace o poloze robotu a mapa jsou nezbytné pro zajištění autonomního pohybu. Cílem je implementovat metodu řešící problém zvaný Simultání lokalizace a mapování pomocí přístupu využívající Transformaci normálního rozdělení. Důraz je kladen na schopnost využít CAD výkresy prostředí jako počáteční mapu. Práce zahrnuje princip metody, popis implementace a zhodnocení výsledků, které bylo zaměřeno na rozdíly v lokalizaci a mapování s využitím CAD výkresů a bez nich.This thesis is dedicated to a lifelong localization of a mobile robot, which is equipped with the multiple sensors. The information about the robot position and the map are necessary for the autonomous movement. The goal of this thesis is implementing the method based on the Normal Distribution Transform for solving the problem called Simultaneous localization and mapping. The important requirement is the ability to use the CAD drawing of the environment as an initial map. The thesis contains the principle of the method, the description of the implementation, and the experiments evaluation. The experiments have been focused on the difference between the localization and mapping process with and without the CAD drawing