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Cognitive assessments are a crucial part of rehabilitation in persons with a neurological disorder and vocational rehabilitation, where people need to be trained to improve their cognitive abilities. While human action involves using several cognitive skills and physical skills, most assessment systems focus on detecting or assessing either the cognitive ability or just physical ability. There is a need for a system that bridges the gap between real-world activity, which involves physical activity and cognition, and clinical tests that are tailored for a specific use. To address this need, we propose a novel interactive 9-Hole Pegboard called the 9-Peg Move (9PM) capable of performing both cognitive and physical assessments in the same system. The system incorporates wearable sensors to collect data for objective evaluation. Preliminary machine learning results indicate that the data collected using our system can reliably recognize cognitive factors like perceived mental effort, perceived task difficulty, and perceived interest in a task. These results are the first step toward building an automated immersive assessment system.

Human-Robot Interaction often involves a robot assisting or providing feedback to a human partner's performance or cooperating to complete a task. In such an interaction scenario, the robotic system requires to perceive the human teammate's cognitive state that might affect task performance. In this pilot study, the focus is on developing a framework that assesses the human's cognitive performance for human-robot synergetic task, such as an assembly task. Specifically, we explore the correlation between a person's quality of sleep and performance metric through a standard task for cognitive assessment, the N-back task. To validate our hypothesis, we conducted a study with 25 participants, and our results indicate that there is a moderate correlation between some stages of sleep cycle and performance. Additionally, we present a possible Human-Robot Interaction setup that could benefit from our results.

Objective: In this paper, we present a multimodal robot-based framework to investigate how physical and mental fatigue affect task performance, and how it relates to subjective self-reports.Methodology: In this pilot study, seven healthy participants underwent the robot-based assessment. In each session, the participants performed a series of reaching tasks, including a task with cognitive demands, by moving the end effector of the Barrett WAM arm in the direction of the virtual targets displayed on a computer screen. Multimodal data, including EEG, EMG and user performance data like reaction time, number of targets, trajectory of the robot path, were recorded for further analysis.Results: Based on the analysis of subjective user self-report and objective task performance metrics, we observe that the user's perceived level of task difficulty increased over time while objective task performance also improved over time. We speculate that this might be due to the effect of fatigue on the user's perception of task difficulty.Conclusion: Further studies are required, with a more diverse population, to understand the impact of fatigue on the user's cognitive and physical ability. We must also evaluate how contextual parameters may affect task performance and fatigue.

Recent developments in computer vision and the emergence of wearable sensors have opened opportunities for the development of advanced and sophisticated techniques to enable multi-modal user assessment and personalized training which is important in educational, industrial training and rehabilitation applications. They have also paved way for the use of assistive robots to accurately assess human cognitive and physical skills. Assessment and training cannot be generalized as the requirement varies for every person and for every application. The ability of the system to adapt to the individual's needs and performance is essential for its effectiveness. In this paper, the focus is on task performance prediction which is an important parameter to consider for personalization. Several research works focus on how to predict task performance based on physiological and behavioral data. In this work, we follow a multi-modal approach where the system collects information from different modalities to predict performance based on (a) User's emotional state recognized from facial expressions(Behavioral data), (b) User's emotional state from body postures(Behavioral data) (c) task performance from EEG signals (Physiological data) while the person performs a robot-based cognitive task. This multi-modal approach of combining physiological data and behavioral data produces the highest accuracy of 87.5 percent, which outperforms the accuracy of prediction extracted from any single modality. In particular, this approach is useful in finding associations between facial expressions, body postures and brain signals while a person performs a cognitive task.

As more industries adopt the use of robots to increase productivity, there is an increased need for effective human-robot interaction training, especially in the case of heavy and high precision robots. This implies the need for easy assessment methods that ensure accurate and personalized employee training. Most current assessments are done via manual observation and surveys. This paper addresses the need for the design of intelligent systems to assess a user's training needs based on the user's behavior and engagement while performing a vocational task simulation. In this paper, we propose a multi-sensory intelligent system to predict user engagement using facial expression and body posture data while the user performs a task to provide cognitive assessment of the user's capabilities, a critical factor in successful vocational performance using robots.

Vocational assessment is the process of identifying and assessing an individual’s level of functioning in relation to vocational preparation. In this research, we have designed a framework to evaluate and train the visual working memory and attention level of users by using a humanoid robot and a brain headband sensor. The humanoid robot generates a sequence of colors and the user performs the task by arranging the colored blocks in the same order. In addition, a task-switching paradigm is used to switch between the tasks and colors to give a new instruction to the user by the robot. The humanoid robot displays guidance error detection information, observes the performance of users during the assessment and gives instructive feedback to them. This research describes the profile of cognitive and behavioral characteristics associated with visual working memory skills, selective attention and ways of supporting the learning needs of workers affected by this problem. Finally, the research concludes the relationships between visual working memory and attentional level during different level of the assessment.

Robot Assisted Training (RAT) systems have been successfully deployed to provide assistance during a training task, promoting an efficient interaction with the user. Personalization can improve the efficiency of the interaction and thus enhance the effects of the training session. Personalization can be achieved through user skill assessment in order to choose an appropriate robot behavior that matches user abilities and needs. Graphical User Interfaces have been used to enable human supervisors to control robots and guide the interaction in RAT-based systems. This work focuses on how such interfaces can be used to enable human supervisor users (e.g., therapists) to assess user skills during a robot-based cognitive task. In this study, we investigate how different visualization features affect decision making and efficiency, towards the design of an intelligent and informative interface.

Extensive research has been carried out in using computer-based techniques to train and prepare workers for various industry positions. Most of this research focuses on how to best enable the workers to perform a type of task safely and efficiently. In fact, many of the accidents in manufacturing and construction environments are due to the lack of proper training needed for employees. In this study, we compare the impact of three types of training approaches on the planning and problem-solving abilities of a trainee while he/she performs the Towers of Hanoi (TOH) task. The three approaches are (a) traditional (with a human trainer), (b) gamification (game-based training simulation), and (c) computer-aided training. The aim of this study is to evaluate a worker’s level of functioning and problem-solving skills based on a specific training approach. Exact assessment of functional capacities is an important prerequisite to ensure effective and personalized training. The study uses workplace simulation to collect different types of performance data and assess the impact of these training approaches.