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The ability to acquire Electroencephalogram (EEG) signals from the brain has led to the development of Brain Computer Interfaces (BCI), which capture signals generated by the physical processes in the brain and use them to control external devices. In this paper, we establish an application to control a robot on the Arduino platform by the use of a BCI system, which does not require training for individual...
In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic...
Humans can often learn high-level features of a piece of music, such as beats, from only a few seconds of audio. If robots could obtain this information just as rapidly, they would be more capable of musical interaction without needing long lead times to learn the music. The presence of robot ego noise, however, makes accurately analyzing music more difficult. In this paper, we focus on the task of...
Our goal is to develop robots that naturally engage people in social exchanges. In this paper, we focus on the problem of recognizing that a person is responsive to a robot's request for interaction. Inspired by human cognition, our approach is to treat this as a contingency detection problem. We present a simple discriminative Support Vector Machine (SVM) classifier to compare against previous generative...
This paper presents the dimensional synthesis of a 4 dofs (3T-1R) actuatedly redundant parallel manipulator (called ARROW V1) based on dual criteria related to rapidity and precision. The dynamical (rapidity) criterion is the worst isotropic linear acceleration starting from rest, and the precision related criterion is the worst translational resolution amplification factor. This latter amplification...
This paper reports the progress of microsystem integration using hybrid microassembly, based on the recently finished EU FP7 project FAB2ASM. Hybrid microassembly is a technology that merges both highspeed coarse placement and high-accuracy self-alignment technologies. This paper addresses the basic concept of hybrid microassembly for microsystem integration and its process chain: component and interfaces,...
This paper presents modification of a speech emotion recognition system for a social robot. Using speaker dependent classifiers with prior speaker identification step was proposed. Emotion recognition is done using global acoustic features of the speech. Six speech signal parameters are computed with the specialised software. The feature extraction is based on calculation of global statistics of those...
Stroke can be a source of significant upper extremity dysfunction and affect the quality of life (QoL) in survivors. In this context, novel rehabilitation approaches employing robotic rehabilitation devices combined with brain-machine interfaces can greatly help in expediting functional recovery in these individuals by actively engaging the user during therapy. However, optimal training conditions...
People with transradial hand amputations who own a myoelectric prosthesis currently have some control capabilities via sEMG. However, the control systems are still limited and not natural. The Ninapro project is aiming at helping the scientific community to overcome these limits through the creation of publicly available electromyography data sources to develop and test machine learning algorithms...
We considered a robot-assisted neuroendoscopy, and we developed a handling interface for linking a clinically-used endoscope to a lightweight robot (tool holder) with 7 DoFs. Such a robot holds potential for soft interaction with the surgeon, yet its intrinsic compliance must be suitably tamed not to lose tool targeting accuracy. Starting from practical specifications by neurosurgeons, we designed,...
In this paper we describe a multiplayer brain-computer interface (BCI) based on the classic game of checkers using steady-state visually evoked potentials (SSVEPs). Previous research in BCI gaming focuses mainly on the production of software-based games using a computer screen — few hardware-based BCI games using a physical board have been developed. Hardware-based games can present a unique set of...
Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification...
We performed an experimental study (n=48) of the effects of context congruency on human perceptions of robotic facial expressions across cultures (Western and East Asian individuals). We found that context congruency had a significant effect on human perceptions, and that this effect varied by the emotional valence of the context and facial expression. Moreover, these effects occurred regardless of...
We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked...
Recognizing a person from a distance is important to establish meaningful social interaction and to provide additional cues regarding the situations experienced by a robot. To do so, face recognition and speaker identification are biometrics commonly used, with identification performance that are influenced by the distance between the person and the robot. This paper presents a system that combines...
Urban Search And Rescue (USAR) robots are used to find and save victims in the wake of disasters such as earthquakes or terrorist attacks. The operators of these robots are affected by high cognitive load; this hinders effective robot usage. This paper presents a cognitive task load model for real-time monitoring and, subsequently, balancing of workload on three factors that affect operator performance...
Studies about human interaction have shown that subtle changes in movement performance and body posture may improve people's acceptance in social groups. The same applies also to robots. However, most of the work has been done on faces and bio-inspired or humanoid robots, while still few works have focused on generic robot body movement to produce interesting interaction settings, which include emotion...
Due to rapid development of agent systems and robotics, more and more chances are available for humans to interact with agent-based robotic technology (e.g., Robotic vacuums, robotic surgery, etc.), this trend increases the importance of human-robot interaction including human-robot communication. For the robust human-robot communication, natural language processing (NLP) can be implemented, among...
This paper presents an adaptive and incremental learning method to visualize series data on a category map. We designate this method as Adaptive Category Mapping Networks (ACMNs). The architecture of ACMNs comprises three modules: a codebook module, a labeling module, and a mapping module. The codebook module converts input features into codebooks as low-dimensional vectors using Self-Organizing Maps...
In the field of active perception, object search is a widely studied problem. To search for an object in large rooms, it would be expensive to explore and check each object's similarity with the object of interest. The expense could uncontrollably bloat as the number of objects to be searched increases. If the objects are of the order of a 2-5cm, they appear very small, making it difficult for the...
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