Fractures and injuries can happen anytime to any person. However, there are body parts such as the upper limbs that have a high recurrence of lesions such as Carpal Tunnel Syndrome (CTS), Scaphoid fracture and Trigger Finger (TF) which are more prevalent in young adult populations. The purpose of this work is to develop an AI-VR-based hand rehabilitation system with a quantitative objective scale that aims to optimize the rehabilitation of the patients. The system analyses muscle (EMG) signals using an AI algorithm to classify different rehabilitation movements and provides visual feedback through a Virtual Reality (VR) environment. Moreover, a tracking hand system was used to acquire the difference in degrees for each movement to keep track of the hand movement in order to provide a better measure of accuracy.

Cognitive decline in aging is a pressing issue that can lead to long term functional impairments, including dementia. Computer-based cognitive training applications have been shown to improve cognitive skills, however, they often lack ecological validity. Researchers have proposed the use of Brain-Machine interface (BMI) systems as cognitive training tools but still face the limitation that the user cannot move freely while performing the cognitive training. Previously, we reported the successful use of a BMI system with a physical robotic third arm that allowed users to do multitasking by doing two tasks simultaneously, thereby engaging multiple cognitive skills such as attention switching, mental focus, coordination, decision making and visual information processing. In this paper, we present a cognitive training platform based on our previous multitasking paradigm with a BMI enhanced with a virtual reality (VR) experience. We conducted an experiment to investigate the efficiency of the proposed platform and monitored the level of accuracy and processing speed of the attention switching skill and compared to the traditional Attention Switching Task (AST) cognitive training paradigm. Preliminary experimental results showed that mean difference in attention accuracy scores were 3.96 s faster for the BMI-VR group compared to the AST group. Although there was a high degree of intersubject variability making the result not statistically significant, preliminary evidence reflects a potential for the proposed training approach to improve attention switching speed.

Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices that will extend the physical capabilities of humans in an unprecedented way. Researchers have explored the possibility to control SRLs in diverse ways - from manual operation through a joystick to myoelectric signals from muscle impulses - but the ultimate goal is to be able to control them with the brain. Brain-machine interface systems (BMI) have allowed the control of prosthetics and robotic devices using brainwaves alone, but the low number of brain-based commands that can be decoded does not allow an SRL to achieve a high number of actions. For this reason, in this paper, we present an intelligent brain-controlled SRL that has context-aware capabilities in order to complement BMI-based commands and increase the number of actions that it can perform with the same BMI-based command. The proposed system consists of a human-like robotic limb that can be activated (i.e. grasp action) with a non-invasive EEG-based BMI when the human operator imagines the action. Since there are different ways that the SRL can perform the action (i.e. different grasping configurations) depending on the context (i.e. type of the object), we provided vision capabilities to the SRL so it can recognize the context and optimize its behavior in order to match the user intention. The proposed hybrid BMI-SRL system opens up the possibilities to explore more complex and realistic human augmentation applications.

This study extends existing Rubber Hand Illusion (RHI) experiments to employ life-size full-body humanlike android robot to investigate body ownership illusion and the sense of agency. For this study, we designed a novel experimental setting. The android robot moved synchronously with a participant's head and arm motion. Furthermore, a stereoscopic camera was mounted on the android's head and the video stream from the camera was provided to a head mounted display (HMD) that the participant wore. Then, the participant and the robot were facing each other, so that the participant could see his/her own body in the HMD like a reflection in a mirror. During the experiment, the participant was asked to touch his/her own body by the hands of an android robot. The results of our study confirmed that the body ownership process worked with the full body android robot. The results also showed that our system enabled the participants to feel the sense of agency to some extent.

The use of robots as educational tools has demonstrated to be highly effective for attracting students to science and technology related academic fields. Although these academic fields are very important, we believe that other subjects such as language, music, arts, literature, history, etc., are also essential for future generations. For this reason, the goal of this research is to explore the potential use of robots as educational tools for non-technology related fields such as history. We discuss an alternative approach for designing robots inspired in traits and characteristics of historical figures that play an important role in the topic to be studied. We present some examples of conceptual designs of robots inspired in ancient gods of Mesoamerican and South American cultures. We discuss how some of the traits of ancient gods could serve as inspiration for the appearance design of commercial robots, and how these robots could be used in educational environments to attract the attention of students to learn about this history topic.

The objective of this research is to investigate the personality factors that influence human decision-making in particular scenarios such as during the interaction with a robot or a human counterpart. We conducted the experiment in a public environment in which participants were approached by either a human or a robot agent. The agent asked a verbal request in a direct or indirect manner that participants could accept or decline. We used the Ten Item Personality Measure (TIPI) in order to measure the personality traits of the agent that had a strong influence in the acceptance decision of the participants. Our results suggest that within the context that our experiment took place, the humanoid robot was more effective at influencing human-decision making than the human agent, in particular when indirect request was used. The personality traits that made the robot to be more effective were: `extrovert', `enthusiastic' and `sympathetic'.

This paper presents a quantitative and qualitative analysis of the first robotics education workshop in which children and parents interact by experimenting with concepts of robotics. Unlike traditional robotics educational workshops that are targeted to children only, our initiative encourages children and parents to interact as a team and learn through ludic activities using a robotic kit. Therefore, apart from presenting the advantages of robotic educational tools, the concept of parenting styles is introduced in order to explore their relationships with participants learning. Analysis results describe child's performance and evidence of the influence of parenting style in the child's learning process. Our approach establishes a precedent to design and implement innovative workshops to encourage and motivate students in order to gain interest in science and technology through robotics, and their parents to use positively the different aspects of parenting styles in the learning process of their children in these workshops.

Brain Machine Interface systems provide ways of communication and control of a variety of devices that range from domestic appliances to humanoid robots. Most BMI systems are designed exclusively to control devices using low-level commands, or high-level commands when devices with pre-programmed functionalities are available. In this paper, we build on our previous work on BMI-based Learning System in which we presented a different approach for designing BMI systems that incorporate learning capabilities that relieve the user from tedious low-level control. In this work, we extend the capabilities of our framework to allow a user to be able to teach and evaluate a robotic system by using a BMI. We provide general system architecture and demonstrate its applicability in new domains such as teaching a humanoid robot object manipulation skills and evaluating its performance. Our approach consists of 1) tele-operating robot's actions while robot's camera collects object's visual properties, 2) learning manipulation skills (i.e. push-left, lift-up, etc.) by approximating a posterior probability of commonly performed actions when observing similar properties, and 3) evaluating robot's performance by considering brain-based error perception of the human while he/she passively observes the robot performing the learned skill. This technique consists of monitoring EEG signals to detect a brain potential called error related negativity (ERN) that spontaneously occurs when the user perceives an error made by the robot. By using human error perception, we demonstrate that it is possible to evaluate robot actions and provide feedback to improve its learning performance. We present results from five human subjects who successfully used our framework to teach a humanoid robot how to manipulate diverse objects, and evaluate robot skills by visual observation.

This paper addresses the problem of mental fatigue caused by prolonged use of Brain Machine Interface (BMI) Systems. We propose a system that gradually becomes autonomous by learning user preferences and by considering error perception feedback. As a particular application, we show that our system allows patients to control electronic appliances in a hospital room, and learns the correlation of room sensor data, brain states, and user control commands. Moreover, error perception feedback based on a brain potential called error related negativity (ERN) - that spontaneously occurs when the user perceives an error made by the system - was used to correct system's mistakes and improve its learning performance. Experimental results with volunteers demonstrate that our system reduces the level of mental fatigue, and achieves over 90% overall learning performance when error perception feedback is considered. Note to Practitioners - This paper suggests a new approach for designing BMI systems that incorporate learning capabilities and error perception feedback in order to gradually become autonomous. This approach consists in learning the relationship between sensing data from the environment-brain and user actions when controlling robotic devices. After the system is trained, can predict control commands on behalf of the user under similar conditions. If the system makes a mistake, user's error perception feedback is considered to improve the learning performance the system. In this paper, we describe the methodologies to design and build hardware and software interfaces, acquire and process brain signals, and train the system using machine learning techniques. We then provide experimental evidence that demonstrates the effectiveness of this approach to design BMI systems that gradually become autonomous.

In this research we present a non-invasive Brain-Machine Interface (BMI) system that allows patients with motor paralysis conditions to control electronic appliances in a hospital room. The novelty of our system compared to other BMI applications is that our system gradually becomes autonomous by learning user actions (i.e. turning on/off window, lights, etc.) under certain environment conditions (temperature, illumination, etc.) and brain states (i.e. awake, sleepy, etc.). By providing learning capabilities to the system, patients are relieved from mental fatigue or stress caused by continuously controlling appliances using a BMI. We present an interface that allows the user to select and control appliances using electromyogram signals (EMG) generated by muscle contractions such as eyebrow movement. Our learning approach consists in two steps: 1) monitoring user actions, input data from sensors distributed around the room, and Electroencephalogram (EEG) data from the user, and 2) using an extended version of the Bayes Point Machine approach trained with Expectation Propagation to approximate a posterior probability from previously observed user actions under a similar combination of brain states and environmental conditions. Experimental results with volunteers demonstrate that our system provides satisfactory user experience and achieves over 85% overall learning performance after only a few trials.

This paper presents a preliminary analysis of the first robotics education workshop in which parent and children interact by experimenting with concepts of robotics and develop problem solving skills. Unlike traditional robotics education workshops targeted to children only, our initiative encourages both parent and children to interact as a team and become interested in science and technology. Post-workshop analysis aims to describe the relation between the filial interaction of the participants, the level of learning and new skills obtained during the robotics workshop. With this new type workshop we expect students to gain more interest in technology and their parents to encourage their children toward engineering and science majors.

This paper seeks to establish a precedent for future development and design of social robots by considering the knowledge and expectations about robots of a group of 296 children. Human-robot interaction experiments were conducted with a Tele-operated anthropomorphic robot, and surveys were taken before and after the experiments. Children were also asked to perform a drawing of a robot. An image analysis algorithm was developed to classify drawings into 4 types: Anthropomorphic Mechanic/Non Mechanic (AM/AnM) and Non-Anthropomorphic Mechanic/Non Mechanic (nAM/nAnM). Image analysis algorithm was used in combination with human classification using a 2oo3 (two out of three) voting scheme to find children's strongest stereotype about robots. Survey and image analysis results suggest that children in general have some general knowledge about robots, and some children even have a deep understanding and expectations for future robots. Moreover, children's strongest stereotype is directed towards mechanical anthropomorphic systems.

We present our method for learning object categories from the internet using cues obtained through human-robot interaction. Such cues include an object model acquired by observation and the name of the object. Our learning approach emulates the natural learning process of children when they observe their environment, encounter unknown objects and ask adults the name of the object. Using this learning approach, our robot is able to discover objects in a domestic environment by observing when humans naturally move objects as part of their daily activities. Using speech interface,the robot directly asks humans the name of the object by showing an example of the acquired model. The name in text format and the previously learnt model serve as input parameters to retrieve object category images from a search engine, select similar object images, and build a classifier. Preliminary results demonstrate the effectiveness of our learning approach.