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This paper focuses on creating video-based human activity recognition methods towards an automated cognitive assessment system for children. We present the Activate Test for Embodied Cognition (ATEC), which assesses executive functioning in children through physical/cognitive tasks. Detecting activities for children is challenging due to high amount of random motion and variability. This paper focuses on creating a ubiquitous and non-intrusive activity recognition system for upper-body movements. Our proposed methods are evaluated on real-world data from children performing the Cross-your-Body task. The dataset includes 15 children performing 8 types of activities, resulting to 1900 annotated video samples.

In this paper, we present our preliminary data analysis towards an automated assessment system for the Sailor Step task. The Sailor Step task is an embodied cognition task designed to assess attention, working memory, response inhibition, rhythm and coordination in children. Children must remember instructions and coordinate their movements following visual cues presented during a themed video clip. Skeleton data were collected through the Kinect camera. This paper presents an overview of the assessment task, the data collection and annotation with a preliminary analysis towards an automated scoring system through motion capture analysis methods.

Risk-taking, when unregulated by good executive function, may predispose youth to potentially dangerous behaviors such as substance abuse, unsafe sex practices, and criminal activity. The computer-based Balloon Analogue Risk Task (BART) has been widely used to study risk-taking propensity and predict real-world risky behaviors. We administered the youth version BART-Y to 6,929 children between grades K-8 and hypothesize that executive function, a cognitive skillset for self-regulation controlled by the frontal lobe, improves risk tolerance. Our preliminary results show that BART Total Points increased across grades and also significantly improved (p

Recent research has shown that hundreds of millions of workers worldwide may lose their jobs to robots and automation by 2030, impacting over 40 developed and emerging countries and affecting more than 800 types of jobs. While automation promises to increase productivity and relieve workers from tedious or heavy-duty tasks, it can also widen the gap, leaving behind workers who lack automation training. In this project, we propose to build a technologically based, personalized vocational cyberlearning training system, where the user is assessed while immersed in a simulated workplace/factory task environment, and the system collecting and analyzing multisensory cognitive, behavioral and physiological data. Such a system, will produce recommendations to support targeted vocational training decision-making. The focus is on collecting and analyzing specific neurocognitive functions that include, working memory, attention, cognitive overload and cognitive flexibility. Collected data are analyzed to reveal, in iterative fashion, relationships between physiological and cognitive performance metrics, and how these relate to work-related behavioral patterns that require special vocational training.

There is a recent trend of research and applications of Cyber-Physical Systems (CPS) in manufacturing to enhance human-robot collaboration and production. In this paper, we propose a CPS framework for personalized Human-Robot Collaboration and Training to promote safe human-robot collaboration in manufacturing environments. We propose a human-centric CPS approach that focuses on multimodal human behavior monitoring and assessment, to promote human worker safety and enable human training in Human-Robot Collaboration tasks. We present the architecture of our proposed system, our experimental testbed and our proposed methods for multimodal physiological sensing, human state monitoring and interactive robot adaptation, to enable personalized interaction.