Towards predictive behavior analysis for smart environments

Predictive behavior analysis allows prediction of the (human) behavior based on the analysis of historical data. Efficient approaches for predictive behavior analysis are available for scenarios with structured processes (e.g., based on ERP systems). The prediction of behavior becomes an obstacle when unstructured (decision making) processes underlie the scenario. Scenarios with unstructured processes can be found in smart environments logging sensor (event) streams such as e.g., Smart Home or Connected Cars. No efficient solutions exist to identify abnormal behavior (anomalies) in such smart environments. To provide a solution for anomaly detection in unstructured processes we suggest crossing process engineering with deep learning. Methods from process engineering allow identifying deviations while deep learning improves the robustness of anomalie detection and prediction. This conjunction is a promising approach in order to find an efficient solution

Analyse endoskopischer Bildsequenzen für ein laparoskopisches Assistenzsystem

Rechnergestützte Assistenzsysteme zielen auf eine Minimierung der chirurgischen Belastung und Verbesserung der Operationsqualität ab und werden immer häufiger eingesetzt. Im Fokus der vorliegenden Arbeit steht die Analyse endoskopischer Bildsequenzen für eine Unterstützung eines minimalinvasiven Eingriffs. Zentrale Themen hierbei sind die Vorverarbeitung der endoskopischen Bilder, die dreidimensionale Analyse der Szene und die Klassifikation unterschiedlicher Handlungsaspekte

Analyse endoskopischer Bildsequenzen für ein laparoskopisches Assistenzsystem

Rechnergestützte Assistenzsysteme zielen auf eine Minimierung der chirurgischen Belastung und Verbesserung der Operationsqualität ab und werden immer häufiger eingesetzt. Im Fokus der vorliegenden Arbeit steht die Analyse endoskopischer Bildsequenzen für eine Unterstützung eines minimalinvasiven Eingriffs. Zentrale Themen hierbei sind die Vorverarbeitung der endoskopischen Bilder, die dreidimensionale Analyse der Szene und die Klassifikation unterschiedlicher Handlungsaspekte

Robot-Assisted Minimally Invasive Surgery-Surgical Robotics in the Data Age

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective