scholarly journals Influence on the user's emotional state of the graphic complexity level in virtual therapies based on a robot-assisted neuro-rehabilitation platform

2020 ◽  
Vol 190 ◽  
pp. 105359
Author(s):  
Borja F. Villar ◽  
Pablo F. Viñas ◽  
Javier P. Turiel ◽  
J. Carlos Fraile Marinero ◽  
Alfonso Gordaliza
Keyword(s):  
Emotional State ◽  
Robot Assisted ◽  
Complexity Level ◽  
Graphic Complexity

Classification of the Emotional State of a Subject Using Machine Learning Algorithms for RehabRoby

2015 ◽  
pp. 53-80
Author(s):  
Duygun Erol Barkana ◽  
Engin Masazade
Keyword(s):  
Machine Learning ◽  
Emotional State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Difficulty Level ◽  
Robot Assisted ◽  
Actual Performance ◽  
Sensory Data ◽  
Machine Learning Methods

Robot-assisted rehabilitation systems have shown to be helpful in neuromotor rehabilitation because it is possible to deliver interactive and repeatable sensorimotor exercise and monitor the actual performance continuously. Note that it is also essential to distinguish if subject finds the rehabilitation task difficult or easy, since the difficulty level of a task can yield different emotional state, such as excited, bored, over-stressed, etc., at each subject. It is important to adjust the difficulty level of the task to encourage the non-motivated subjects during the therapy. The physiological measurements, which can be obtained from the biofeedback sensors, can be used to estimate the subject's emotional state during the execution of the rehabilitation task. Machine learning methods can be used to classify the emotional state using the features of the biofeedback sensory data. This is explored in this chapter.

161: Robot-Assisted Laparoscopic Pyeloplasty - 5-Year Follow-Up in a Single Center

2007 ◽  
Vol 177 (4S) ◽  
pp. 55-55
Author(s):  
Christian Schwentner ◽  
Andreas Lunacek ◽  
Alexandre E. Pelzer ◽  
Richard Neururer ◽  
Wolfgang Horninger ◽  
...  
Keyword(s):  
Single Center ◽  
Laparoscopic Pyeloplasty ◽  
Robot Assisted

V869: Robot Assisted Vesicovaginal Fistula Repair

2007 ◽  
Vol 177 (4S) ◽  
pp. 289-289
Author(s):  
Michael E. Woods ◽  
Jeff Bejma ◽  
Rodney Davis
Keyword(s):  
Vesicovaginal Fistula ◽  
Fistula Repair ◽  
Robot Assisted

1483: Robot-Assisted Urethra, Vagina & Uterine Preserving Radical Cystectomy and Urinary Diversion in Female

2004 ◽  
Vol 171 (4S) ◽  
pp. 390-390
Author(s):  
Mani Menon ◽  
Ashok K. Hemal ◽  
Ashutosh Tewari ◽  
Hassan Abol-Enein ◽  
Mohamed Ghoneim
Keyword(s):  
Radical Cystectomy ◽  
Urinary Diversion ◽  
Robot Assisted

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

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