A Behavior Tree Cognitive Assistant System for Emergency Medical Services This paper presents a cognitive assistant system for emergency medical services (EMS) that can serve as a rescue robot or virtual assistant, helping with improving situational awareness of the first responders through automated collection and analysis of data from the incident scene and providing suggestions to them. The proposed system relies on a Behavior Tree (BT) framework that combines the knowledge of EMS protocol guidelines with speech recognition, natural language processing, and machine learning methods to (i) extract critical information from respondersxe2x80x99 conversations and verbalized observations, (ii) infer the incident context, and (iii) decide on safe and effective response interventions to perform. We use a dataset of 8302 real EMS call records from an urban, high volume regional ambulance agency in the U.S. to evaluate the responsiveness and cognitive ability of the system and assess the safety of the suggestions provided to the responders. The experimental results show that the developed cognitive assistant achieves an average top3 accuracy of 89% in selecting the correct EMS protocols and an average F1score of 71% in suggesting the protocol specific interventions while providing transparency and evidence for the suggestions.

Machine learning for assistance systems: patternbased approach to online step recognition Computeraided assistance systems are entering the world of work and production. Such systems utilize augmentedxe2x80x93 and virtualxe2x80x93reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of everchanging products and xe2x80x98lot size onexe2x80x99 customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in humancentered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The posthoc, or historical, analysis yields promising results. The adhoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt statexe2x80x93 ofxe2x80x93thexe2x80x93art machine learning to operator movement recognition with a special focus on approaches to spatial time series data preprocessing. Presented experiment results validate the approach and show that it performs well in a realworld scenario.