scholarly journals Brain Signals Classification Based on Fuzzy Lattice Reasoning

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1063
Author(s):  
Eleni Vrochidou ◽  
Chris Lytridis ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
Hiroaki Wagatsuma ◽  
...  
Keyword(s):  
Decision Making ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Human Robot Interaction ◽  
Visual Signals ◽  
K Nearest Neighbor ◽  
Robot Interaction ◽  
Brain Signals ◽  
Fuzzy Lattice ◽  
Fuzzy Order

Cyber-Physical System (CPS) applications including human-robot interaction call for automated reasoning for rational decision-making. In the latter context, typically, audio-visual signals are employed. Τhis work considers brain signals for emotion recognition towards an effective human-robot interaction. An ElectroEncephaloGraphy (EEG) signal here is represented by an Intervals’ Number (IN). An IN-based, optimizable parametric k Nearest Neighbor (kNN) classifier scheme for decision-making by fuzzy lattice reasoning (FLR) is proposed, where the conventional distance between two points is replaced by a fuzzy order function (σ) for reasoning-by-analogy. A main advantage of the employment of INs is that no ad hoc feature extraction is required since an IN may represent all-order data statistics, the latter are the features considered implicitly. Four different fuzzy order functions are employed in this work. Experimental results demonstrate comparably the good performance of the proposed techniques.

scholarly journals WINkNN: Windowed Intervals’ Number kNN Classifier for Efficient Time-Series Applications

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 413 ◽  
Author(s):  
Chris Lytridis ◽  
Anna Lekova ◽  
Christos Bazinas ◽  
Michail Manios ◽  
Vassilis G. Kaburlasos
Keyword(s):  
Time Series ◽  
Ad Hoc ◽  
Nearest Neighbor ◽  
Classification Performance ◽  
Human Robot Interaction ◽  
Time Series Classification ◽  
K Nearest Neighbor ◽  
Time Dimension ◽  
Knn Classifier ◽  
Benchmark Datasets

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

scholarly journals A Hybrid Mapping Method with Position and Stiffness for Manipulator Teleoperation

2019 ◽  
Vol 9 (23) ◽  
pp. 5005 ◽  
Author(s):  
Jiang ◽  
Ni ◽  
Yang ◽  
Li ◽  
Yang ◽  
...  
Keyword(s):  
Free Space ◽  
Nearest Neighbor ◽  
Mapping Method ◽  
Human Robot Interaction ◽  
Hybrid Mapping ◽  
K Nearest Neighbor ◽  
Robot Interaction ◽  
Human Arm ◽  
Virtual Impedance ◽  
Exoskeleton Device

Transferring versatile skills of human behavior to teleoperate manipulators to execute tasks with large uncertainties is challenging in robotics. This paper proposes a hybrid mapping method with position and stiffness for manipulator teleoperation through the exoskeleton device combining with the surface electromyography (sEMG) sensors. Firstly, according to the redefinition of robot workspace, the fixed scale mapping in free space and virtual impedance mapping in fine space are presented for position teleoperation. Secondly, the stiffness at the human arm endpoint is predicted and classified into three levels based on the K nearest neighbor (KNN) and XGBoost, and the stiffness mapping method is utilized to regulate the stiffness behavior of manipulator. Finally, the proposed method is demonstrated in three complementary experiments, namely the trajectory tracking in free space, the obstacle avoidance in fine space and the human robot interaction in contact space, which illustrate the effectiveness of the method.

scholarly journals A Proposal of Accessibility Guidelines for Human-Robot Interaction

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 561
Author(s):  
Malak Qbilat ◽  
Ana Iglesias ◽  
Tony Belpaeme
Keyword(s):  
Social Impact ◽  
Social Acceptance ◽  
Ad Hoc ◽  
Human Robot Interaction ◽  
Design Practice ◽  
Robot Interaction ◽  
Important Place ◽  
Accessibility Barriers ◽  
Accessibility Guidelines ◽  
Different Characteristics

We will increasingly become dependent on automation to support our manufacturing and daily living, and robots are likely to take an important place in this. Unfortunately, currently not all the robots are accessible for all users. This is due to the different characteristics of users, as users with visual, hearing, motor or cognitive disabilities were not considered during the design, implementation or interaction phase, causing accessibility barriers to users who have limitations. This research presents a proposal for accessibility guidelines for human-robot interaction (HRI). The guidelines have been evaluated by seventeen HRI designers and/or developers. A questionnaire of nine five-point Likert Scale questions and 6 open-ended questions was developed to evaluate the proposed guidelines for developers and designers, in terms of four main factors: usability, social acceptance, user experience and social impact. The questions act as indicators for each factor. The majority (15 of 17 participants) agreed that the guidelines are helpful for them to design and implement accessible robot interfaces and applications. Some of them had considered some ad hoc guidelines in their design practice, but none of them showed awareness of or had applied all the proposed guidelines in their design practice, 72% of the proposed guidelines have been applied by less than or equal to 8 participants for each guideline. Moreover, 16 of 17 participants would use the proposed guidelines in their future robot designs or evaluation. The participants recommended the importance of aligning the proposed guidelines with safety requirements, environment of interaction (indoor or outdoor), cost and users’ expectations.

scholarly journals Neuro-cognitive mechanisms of decision making in joint action: A human–robot interaction study

2011 ◽  
Vol 30 (5) ◽  
pp. 846-868 ◽  
Author(s):  
Estela Bicho ◽  
Wolfram Erlhagen ◽  
Luis Louro ◽  
Eliana Costa e Silva
Keyword(s):  
Decision Making ◽  
Joint Action ◽  
Human Robot Interaction ◽  
Interaction Study ◽  
Cognitive Mechanisms ◽  
Robot Interaction

scholarly journals A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2691 ◽  
Author(s):  
Marcos Maroto-Gómez ◽  
Álvaro Castro-González ◽  
José Castillo ◽  
María Malfaz ◽  
Miguel Salichs
Keyword(s):  
Decision Making ◽  
Human Robot Interaction ◽  
Learning Mechanisms ◽  
Biologically Inspired ◽  
Robot Interaction ◽  
Detection Mechanism ◽  
Learned Behaviors ◽  
Decision Making System ◽  
Real Robot ◽  
Self Learning

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.

Affective Goal and Task Selection for Social Robots

2009 ◽  
pp. 74-87 ◽  
Author(s):  
Matthias Scheutz ◽  
Paul Schermerhorn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Task Selection ◽  
Resource Limitations ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Selection Of

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

scholarly journals A Predictive Prescription Using Minimum Volume k-Nearest Neighbor Enclosing Ellipsoid and Robust Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 119
Author(s):  
Shunichi Ohmori
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Robust Optimization ◽  
Nearest Neighbor ◽  
Predictive Analytics ◽  
Uncertain Parameters ◽  
Minimum Volume ◽  
K Nearest Neighbor ◽  
Modeling Framework ◽  
Prescriptive Analytics

This paper studies the integration of predictive and prescriptive analytics framework for deriving decision from data. Traditionally, in predictive analytics, the purpose is to derive prediction of unknown parameters from data using statistics and machine learning, and in prescriptive analytics, the purpose is to derive a decision from known parameters using optimization technology. These have been studied independently, but the effect of the prediction error in predictive analytics on the decision-making in prescriptive analytics has not been clarified. We propose a modeling framework that integrates machine learning and robust optimization. The proposed algorithm utilizes the k-nearest neighbor model to predict the distribution of uncertain parameters based on the observed auxiliary data. The enclosing minimum volume ellipsoid that contains k-nearest neighbors of is used to form the uncertainty set for the robust optimization formulation. We illustrate the data-driven decision-making framework and our novel robustness notion on a two-stage linear stochastic programming under uncertain parameters. The problem can be reduced to a convex programming, and thus can be solved to optimality very efficiently by the off-the-shelf solvers.

Affective Goal and Task Selection for Social Robots

2010 ◽  
pp. 2150-2163
Author(s):  
Matthias Scheutz ◽  
Paul Schermerhorn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Task Selection ◽  
Resource Limitations ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Selection Of

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

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