Robotic Assistance in Nursing Care: Survey on Challenges and Scenarios

Year: 2020 Volume: 14 Issue: 9 269 - 274 Pages

• Authors:
• Pascal Gliesche
• Kathrin Seibert
• Christian Kowalski
• Dominik Domhoff
• Max Pfingsthorn
• Karin Wolf-Ostermann
• Andreas Hein

Abstract: Robotic assistance in nursing care is an increasingly important area of research and development. Facing a shortage of labor and an increasing number of people in need of care, the German Nursing Care Innovation Center (Pflegeinnovationszentrum, PIZ) aims to address these challenges from the side of technology. Little is known about nurses experiences with existing robotic assistance systems. Especially nurses perspectives on starting points for the development of robotic solutions, that target recurring burdensome tasks in everyday nursing care, are of interest. This paper presents findings focusing on robotics resulting from an explanatory mixed-methods study on nurses experiences with and their expectations for innovative technologies in nursing care in stationary and ambulant care facilities and hospitals in Germany. Based on the findings, eight scenarios for robotic assistance are identified based on the real needs of practitioners. An initial system addressing a single use-case is described to show perspectives for the use of robots in nursing care.

• Keywords:
• Robotics and automation
• engineering management
• engineering in medicine and biology
• medical services
• public healthcare.

A Novel Nucleus-Based Classifier for Discrimination of Osteoclasts and Mesenchymal Precursor Cells in Mouse Bone Marrow Cultures

Year: 2012 Volume: 6 Issue: 7 286 - 291 Pages

• Authors:
• Andreas Heindl
• Alexander K. Seewald
• Martin Schepelmann
• Radu Rogojanu
• Giovanna Bises
• Theresia Thalhammer
• Isabella Ellinger

Abstract: Bone remodeling occurs by the balanced action of bone resorbing osteoclasts (OC) and bone-building osteoblasts. Increased bone resorption by excessive OC activity contributes to malignant and non-malignant diseases including osteoporosis. To study OC differentiation and function, OC formed in in vitro cultures are currently counted manually, a tedious procedure which is prone to inter-observer differences. Aiming for an automated OC-quantification system, classification of OC and precursor cells was done on fluorescence microscope images based on the distinct appearance of fluorescent nuclei. Following ellipse fitting to nuclei, a combination of eight features enabled clustering of OC and precursor cell nuclei. After evaluating different machine-learning techniques, LOGREG achieved 74% correctly classified OC and precursor cell nuclei, outperforming human experts (best expert: 55%). In combination with the automated detection of total cell areas, this system allows to measure various cell parameters and most importantly to quantify proteins involved in osteoclastogenesis.

• Keywords:
• osteoclasts
• machine learning
• ellipse fitting.