Stable interaction with an n-DOF virtual object using a modified passivity based control architecture

Author(s):  
R. Jaberzadeh Ansari ◽  
S. M. Rezaei ◽  
H. A. Talebi ◽  
M. Zareinejad ◽  
K. Baghestan ◽  
...  
1995 ◽  
Vol 34 (05) ◽  
pp. 475-488
Author(s):  
B. Seroussi ◽  
J. F. Boisvieux ◽  
V. Morice

Abstract:The monitoring and treatment of patients in a care unit is a complex task in which even the most experienced clinicians can make errors. A hemato-oncology department in which patients undergo chemotherapy asked for a computerized system able to provide intelligent and continuous support in this task. One issue in building such a system is the definition of a control architecture able to manage, in real time, a treatment plan containing prescriptions and protocols in which temporal constraints are expressed in various ways, that is, which supervises the treatment, including controlling the timely execution of prescriptions and suggesting modifications to the plan according to the patient’s evolving condition. The system to solve these issues, called SEPIA, has to manage the dynamic, processes involved in patient care. Its role is to generate, in real time, commands for the patient’s care (execution of tests, administration of drugs) from a plan, and to monitor the patient’s state so that it may propose actions updating the plan. The necessity of an explicit time representation is shown. We propose using a linear time structure towards the past, with precise and absolute dates, open towards the future, and with imprecise and relative dates. Temporal relative scales are introduced to facilitate knowledge representation and access.


1998 ◽  
Author(s):  
Mark A. Murphy ◽  
Robert L. Williams ◽  
III

1999 ◽  
Vol 7 (4) ◽  
pp. 391-404 ◽  
Author(s):  
TONG FANG ◽  
MOHSEN A. JAFARI ◽  
AHMAD SAFARI ◽  
STEPHEN C. DANFORTH

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Md Moin Uddin Atique ◽  
Joseph Thachil Francis

AbstractMirror Neurons (MNs) respond similarly when primates make or observe grasping movements. Recent work indicates that reward expectation influences rostral M1 (rM1) during manual, observational, and Brain Machine Interface (BMI) reaching movements. Previous work showed MNs are modulated by subjective value. Here we expand on the above work utilizing two non-human primates (NHPs), one male Macaca Radiata (NHP S) and one female Macaca Mulatta (NHP P), that were trained to perform a cued reward level isometric grip-force task, where the NHPs had to apply visually cued grip-force to move and transport a virtual object. We found a population of (S1 area 1–2, rM1, PMd, PMv) units that significantly represented grip-force during manual and observational trials. We found the neural representation of visually cued force was similar during observational trials and manual trials for the same units; however, the representation was weaker during observational trials. Comparing changes in neural time lags between manual and observational tasks indicated that a subpopulation fit the standard MN definition of observational neural activity lagging the visual information. Neural activity in (S1 areas 1–2, rM1, PMd, PMv) significantly represented force and reward expectation. In summary, we present results indicating that sensorimotor cortices have MNs for visually cued force and value.


Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1069
Author(s):  
Deyby Huamanchahua ◽  
Adriana Vargas-Martinez ◽  
Ricardo Ramirez-Mendoza

Exoskeletons are an external structural mechanism with joints and links that work in tandem with the user, which increases, reinforces, or restores human performance. Virtual Reality can be used to produce environments, in which the intensity of practice and feedback on performance can be manipulated to provide tailored motor training. Will it be possible to combine both technologies and have them synchronized to reach better performance? This paper consists of the kinematics analysis for the position and orientation synchronization between an n DoF upper-limb exoskeleton pose and a projected object in an immersive virtual reality environment using a VR headset. To achieve this goal, the exoskeletal mechanism is analyzed using Euler angles and the Pieper technique to obtain the equations that lead to its orientation, forward, and inverse kinematic models. This paper extends the author’s previous work by using an early stage upper-limb exoskeleton prototype for the synchronization process.


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