Analyzing human gait and posture by combining feature selection and kernel methods

This paper evaluates a set of computational algorithms for the automatic estimation of human postures and gait properties from signals provided by an inertial body sensor. The use of a single sensor device imposes limitations for the automatic estimation of relevant properties, like step length and gait velocity, as well as for the detection of standard postures like sitting or standing. Moreover, the exact location and orientation of the sensor is also a common restriction that is relaxed in this study. Based on accelerations provided by a sensor, known as the `9 2', three approaches are presented extracting kinematic information from the user motion and posture. Firstly, a two-phases procedure implementing feature extraction and Support Vector Machine based classi cation for daily living activity monitoring is presented. Secondly, Support Vector Regression is applied on heuristically extracted features for the automatic computation of spatiotemporal properties during gait. Finally, sensor information is interpreted as an observation of a particular trajectory of the human gait dynamical system, from which a reconstruction space is obtained, and then transformed using standard principal components analysis, nally Support Vector Regression is used for prediction. Daily living Activities are detected and spatiotemporal parameters of human gait are estimated using methods sharing a common structure based on feature extraction and kernel methods. The approaches presented are susceptible to be used for medical purposes.Peer ReviewedPreprin

External force estimation for textile grasp detection

Our current work on external force estimation without end-effector force sensor is resented.To verify if a grasp of a textile has been successful, the external wrench applied on the robot is computed online, with a state observer based on a LWPR [3] model of a task.Peer ReviewedPostprint (author’s final draft

Emerging behaviors by learning joint coordination in articulated mobile robots

A Policy Gradient Reinforcement Learning (RL) technique is used to design the low level controllers that drives the joints of articulated mobile robots: A search in the controller’s parameters space. There is an unknown value function that measures the quality of the controller respect to the parameters of it. The search is orientated by the approximation of the gradient of the value function. The approximation is made by means of the robot experiences and then the behaviors emerge. This technique is employed in a structure that processes sensor information to achieve coordination. The structure is based on a modularization principle in which complex overall behavior is the result of the interaction of individual ‘simple’ components. The simple components used are standard low level controllers (PID) which output is combined, sharing information between articulations and therefore taking integrated control actions. Modularization and Learning are cognitive features, here we endow the robots with this features. Learning experiences in simulated robots are presented as demonstration.Peer Reviewe

Aibo JukeBox : a robot dance interactive experience

This paper presents a human-robot interaction system based on the Aibo platform. This robot is both, complex and empathetic enough to generate a high level of interest from the user. The complete system is an interactive JukeBox intending to generate affective participation, i.e., empathy, from the user towards the robot and its behavior. This application is based on a robotic dance control system that generates movements adequate to the music rhythm using a stochastic controller. The user can interact with the system selecting or providing the songs to be danced by the robot. The application has been successfully presented in different non-scientific scenarios.Peer Reviewe

Aibo JukeBox : a robot dance interactive experience

This paper presents a human-robot interaction system based on the Aibo platform. This robot is both, complex and empathetic enough to generate a high level of interest from the user. The complete system is an interactive JukeBox intending to generate affective participation, i.e., empathy, from the user towards the robot and its behavior. This application is based on a robotic dance control system that generates movements adequate to the music rhythm using a stochastic controller. The user can interact with the system selecting or providing the songs to be danced by the robot. The application has been successfully presented in different non-scientific scenarios.Peer Reviewe

External force estimation for textile grasp detection

Our current work on external force estimation without end-effector force sensor is resented.To verify if a grasp of a textile has been successful, the external wrench applied on the robot is computed online, with a state observer based on a LWPR [3] model of a task.Peer Reviewe

External force estimation during compliant robot manipulation

This paper presents a method to estimate external forces exerted on a manipulator, avoiding the use of a sensor. The method is based on task-oriented dynamics model learning and a robust disturbance state observer. The combination of both leads to an efficient torque observer that can be incorporated to any control scheme. The use of a learned based approach avoids the need of analytical models of friction or Coriolis dynamics effects.Peer Reviewe

Dynamically consistent probabilistic model for robot motion learning

This work presents a probabilistic model for learning robot tasks from human demonstrations using kinesthetic teaching. The difference with respect to previous works is that a complete state of the robot is used to obtain a consistent representation of the dynamics of the task. The learning framework is based on hidden Markov models and Gaussian mixture regression, used for coding and reproducing the skills. Benefits of the proposed approach are shown in the execution of a simple self-crossing trajectory by a 7-DoF manipulator

Human - Humanoid Robot Interaction: The 20Q Game

This article introduces the development of a human-robot interaction’s system. The NAO humanoid robot has been programmed to play the popular 20 questions (20Q) game. The system includes modules for both, speech recognition and text-to-speech as mechanisms for exchanging information with the humanoid robot. The system has been tested with non-expert volunteers to analyse which kind of interaction is obtained.Peer Reviewe

Building up child-robot relationship: from initial attraction towards long-term social engagement

To explore social bonds’ emergence with robots, a field study with 49 sixth grade scholars (aged 11-12 years) and 4 different robots was carried out at an elementary school. A subsequent laboratory experiment with 4 of the participants was completed in order to explore social engagement. At school, children’s preferences, expectations on functionality and communication, and interaction behavior were studied. In the lab, recognition, partner’s selection, and dyadic interaction were explored. Both at school and in the lab, data from videotaped direct observation, questionnaires and interviews were gathered. The results showed that different robots’ appearance and performance elicit in children distinctive perceptions and interactive behavior and affect social processes (e. g., role attribution and attachment). The preliminary results will help in the design of robot-based programs for hospitalized children to improve quality of life1Peer Reviewe