Discrimination of Dual-Arm Motions Using a Joint Posterior Probability Neural Network for Human-Robot Interfaces

2018 ◽  
pp. 347-374
Author(s):  
Taro Shibanoki ◽  
Toshio Tsuji
Keyword(s):  
Posterior Probability ◽  
Discrimination Performance ◽  
Robotic Manipulator ◽  
Posterior Probabilities ◽  
Arm Motions ◽  
High Discrimination ◽  
Left And Right ◽  
Arm Motion ◽  
Human Robot Interfaces ◽  
Dual Arm

This chapter describes a novel dual-arm motion discrimination method that combines posterior probabilities estimated independently for left and right arm movements, and its application to control a robotic manipulator. The proposed method estimates the posterior probability of each single-arm motion through learning using recurrent probabilistic neural networks. The posterior probabilities output from the networks are then combined based on motion dependency between arms, making it possible to calculate a joint posterior probability of dual-arm motions. With this method, all the dual-arm motions consisting of each single-arm motion can be discriminated through leaning of single-arm motions only. In the experiments performed, the proposed method was applied to the discrimination of up to 50 dual-arm motions. The results showed that the method enables relatively high discrimination performance. In addition, the possibility of applying the proposed method for a human-robot interface was confirmed through operation experiments for the robotic manipulator using dual-arm motions.

Investigation of Synchronous Accuracy of Dual Arm Motion of Industrial Robot

2012 ◽  
Vol 516 ◽  
pp. 234-239 ◽  
Author(s):  
Wei Wu ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama
Keyword(s):  
Rotational Motion ◽  
Manufacturing Systems ◽  
Industrial Robot ◽  
Circular Path ◽  
Stable Operation ◽  
Motion Error ◽  
Ball Rolling ◽  
Arm Motion ◽  
Five Axis ◽  
Dual Arm

Recently, new needs have emerged to control not only linear motion but also rotational motion in high-accuracy manufacturing fields. Many five-axis-controlled machining centres are therefore in use. However, one problem has been the difficulty of creating flexible manufacturing systems with methods based on the use of these machine tools. On the other hand, the industrial dual-arm robot has gained attention as a new way to achieve accurate linear and rotational motion in an attempt to control a working plate like a machine tool table. In the present report, a cooperating dual-arm motion is demonstrated to make it feasible to perform stable operation control, such as controlling the working plate to keep a ball rolling around a circular path on it. As a result, we investigated the influence of each axis motion error on a ball-rolling path.

scholarly journals Simultaneous Dual-Arm Motion Planning for Minimizing Operation Time

2017 ◽  
Vol 7 (12) ◽  
pp. 1210 ◽  
Author(s):  
Jun Kurosu ◽  
Ayanori Yorozu ◽  
Masaki Takahashi
Keyword(s):  
Motion Planning ◽  
Operation Time ◽  
Arm Motion ◽  
Dual Arm

Experimental Study and Characterization of SEMG Signals for Upper Limbs

2015 ◽  
Vol 14 (03) ◽  
pp. 1550028 ◽  
Author(s):  
Karan Veer
Keyword(s):  
Learning Algorithm ◽  
Statistical Technique ◽  
Computational Techniques ◽  
Dual Channel ◽  
The Neural Network ◽  
Arm Motions ◽  
Upper Limb Movements ◽  
Arm Motion ◽  
Hidden Layer ◽  
Semg Signals

Surface electromyogram (SEMG) is used to measure the activity of superficial muscles and is an essential tool to carry out biomechanical assessments required for prosthetic design. Many previous attempts suggest that, electromyogram (EMG) signals have random nature. Here, dual channel evaluation of EMG signals acquired from the amputed subjects using computational techniques for classification of arm motion are presented. After recording data from four predefined upper arm motions, interpretation of signal was done for six statistical features. The signals are classified by the neural network (NN) and then interpretation was done using statistical technique to extract the effectiveness of recorded signals. The network performances are analyzed by considering the number of input features, hidden layer, learning algorithm and mean square error. From the results, it is observed that there exists calculative difference in amplitude gain across different motions and have great potential to classify arm motions. The outcome indicates that NN algorithm performs significantly better than other algorithms with classification accuracy (CA) of 96.40%. Analysis of variance technique presents the results to validate the effectiveness of recorded data to discriminate SEMG signals. Results are of significant thrust in identifying the operations that can be implemented for classifying upper limb movements suitable for prostheses design.

scholarly journals Asymmetric practices of reading and writing shape visuospatial attention and discrimination

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rita Mendonça ◽  
Margarida V. Garrido ◽  
Gün R. Semin
Keyword(s):  
Attentional Bias ◽  
Congruency Effect ◽  
Eye Gaze ◽  
Discrimination Performance ◽  
Region Of Interest ◽  
Visuospatial Attention ◽  
Reading And Writing ◽  
Attention Orienting ◽  
Left And Right ◽  
The Right

AbstractMovement is generally conceived of as unfolding laterally in the writing direction that one is socialized into. In ‘Western’ languages, this is a left-to-right bias contributing to an imbalance in how attention is distributed across space. We propose that the rightward attentional bias exercises an additional unidirectional influence on discrimination performance thus shaping the congruency effect typically observed in Posner-inspired cueing tasks. In two studies, we test whether faces averted laterally serve as attention orienting cues and generate differences in both target discrimination latencies and gaze movements across left and right hemifields. Results systematically show that right-facing faces (i.e. aligned with the script direction) give rise to an advantage for cue-target pairs pertaining to the right (versus left) side of space. We report an asymmetry between congruent conditions in the form of right-sided facilitation for: (a) response time in discrimination decisions (experiment 1–2) and (b) eye-gaze movements, namely earlier onset to first fixation in the respective region of interest (experiment 2). Left and front facing cues generated virtually equal exploration patterns, confirming that the latter did not prime any directionality. These findings demonstrate that visuospatial attention and consequent discrimination are highly dependent on the asymmetric practices of reading and writing.

IoT-Enabled Dual-Arm Motion Capture and Mapping for Telerobotics in Home Care

2020 ◽  
Vol 24 (6) ◽  
pp. 1541-1549 ◽  
Author(s):  
Huiying Zhou ◽  
Geng Yang ◽  
Honghao Lv ◽  
Xiaoyan Huang ◽  
Huayong Yang ◽  
...  
Keyword(s):  
Home Care ◽  
Motion Capture ◽  
Arm Motion ◽  
Dual Arm

scholarly journals Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2104 ◽  
Author(s):  
Umme Zakia ◽  
Carlo Menon
Keyword(s):  
Real Time ◽  
Preliminary Investigation ◽  
Support Vector ◽  
Force Estimation ◽  
Motion Patterns ◽  
Interactive Control ◽  
Arm Motions ◽  
Force Myography ◽  
Arm Motion ◽  
Hand Forces

Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.

The Use of the Posterior Probability in Score Differencing

2020 ◽  
pp. 107699862095742
Author(s):  
Sandip Sinharay ◽  
Matthew S. Johnson
Keyword(s):  
Statistical Methods ◽  
Simulation Study ◽  
Null Hypothesis ◽  
Posterior Probability ◽  
Alternative Hypothesis ◽  
Real Data ◽  
Posterior Probabilities ◽  
Bayesian Approaches ◽  
Suggested Approach

Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack & Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to perform score differencing, the use of the posterior probability of better performance on one item set compared to another. In a simulation study, the suggested approach performs satisfactory compared to several existing approaches for score differencing. A real data example demonstrates how the suggested approach may be effective in detecting fraudulent examinees. The results in this article call for more attention to the use of posterior probabilities, and Bayesian approaches in general, in investigations of test fraud.

scholarly journals Computation with Spikes in a Winner-Take-All Network

2009 ◽  
Vol 21 (9) ◽  
pp. 2437-2465 ◽  
Author(s):  
Matthias Oster ◽  
Rodney Douglas ◽  
Shih-Chii Liu
Keyword(s):  
Biological Networks ◽  
Discrimination Performance ◽  
Recurrent Network ◽  
Sensory Stimuli ◽  
Spiking Network ◽  
High Discrimination ◽  
Standard Rate ◽  
Winner Take All ◽  
Theoretical Results ◽  
Take All

The winner-take-all (WTA) computation in networks of recurrently connected neurons is an important decision element of many models of cortical processing. However, analytical studies of the WTA performance in recurrent networks have generally addressed rate-based models. Very few have addressed networks of spiking neurons, which are relevant for understanding the biological networks themselves and also for the development of neuromorphic electronic neurons that commmunicate by action potential like address-events. Here, we make steps in that direction by using a simplified Markov model of the spiking network to examine analytically the ability of a spike-based WTA network to discriminate the statistics of inputs ranging from stationary regular to nonstationary Poisson events. Our work extends previous theoretical results showing that a WTA recurrent network receiving regular spike inputs can select the correct winner within one interspike interval. We show first for the case of spike rate inputs that input discrimination and the effects of self-excitation and inhibition on this discrimination are consistent with results obtained from the standard rate-based WTA models. We also extend this discrimination analysis of spiking WTAs to nonstationary inputs with time-varying spike rates resembling statistics of real-world sensory stimuli. We conclude that spiking WTAs are consistent with their continuous counterparts for steady-state inputs, but they also exhibit high discrimination performance with nonstationary inputs.

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