A Neural Network Model for the Acquisition of a Spatial Body Scheme Through Sensorimotor Interaction

2011 ◽  
Vol 23 (7) ◽  
pp. 1821-1834 ◽  
Author(s):  
Vadim Y. Roschin ◽  
Alexander A. Frolov ◽  
Yves Burnod ◽  
Marc A. Maier
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Degrees Of Freedom ◽  
Three Dimensional ◽  
Internal Representation ◽  
Spherical Body ◽  
The Body ◽  
Body Scheme ◽  
Sensorimotor Interaction

This letter presents a novel unsupervised sensory matching learning technique for the development of an internal representation of three-dimensional information. The representation is invariant with respect to the sensory modalities involved. Acquisition of the internal representation is demonstrated with a neural network model of a sensorimotor system of a simple model creature, consisting of a tactile-sensitive body and a multiple-degrees-of-freedom arm with proprioceptive sensitivity. Acquisition of the 3D representation as well as a distributed representation of the body scheme, occurs through sensorimotor interactions (i.e., the sensory-motor experience of the creature). Convergence of the learning is demonstrated through computer simulations for the model creature with a 7-DoF arm and a spherical body covered by 20 tactile fields.

scholarly journals Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoyi Guo ◽  
Wei Zhou ◽  
Qun Lu ◽  
Aiyan Du ◽  
Yinghua Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Dry Weight ◽  
Hemodialysis Patients ◽  
Normal Weight ◽  
The Body ◽  
Functional Link ◽  
Patient Will ◽  
Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

scholarly journals A neural network model of three-dimensional dynamic electron density in the inner magnetosphere

2017 ◽  
Vol 122 (9) ◽  
pp. 9183-9197 ◽  
Author(s):  
X. Chu ◽  
J. Bortnik ◽  
W. Li ◽  
Q. Ma ◽  
R. Denton ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Density ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Inner Magnetosphere

scholarly journals An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram

2019 ◽  
Vol 8 (2S6) ◽  
pp. 502-507
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
The Body ◽  
Mental Wellbeing ◽  
Daily Routine ◽  
Back Propagation Algorithm ◽  
Suggested Technique

People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone's response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.

Neural Network Modeling of Maximum Insertion Force of Bevel-Tip Surgical Needle

2018 ◽  
Author(s):  
Sai Teja Reddy Gidde ◽  
Tololupe Verissimo ◽  
Nuo Chen ◽  
Parsaoran Hutapea ◽  
Byoung-gook Loh
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Modeling ◽  
The Body ◽  
Neural Network Modeling ◽  
Tissue Deformation ◽  
Input Output ◽  
Insertion Force ◽  
Tissue Phantom

Recently there has been a growing interest to develop innovative surgical needles for percutaneous interventional procedures. Needles are commonly used to reach target locations inside of the body for various medical interventions. The effectiveness of these procedures depends on the accuracy with which the needle tips reach the targets, such as a biopsy procedure to assess cancerous cells and tumors. One of the major issues in needle steering is the force during insertion, also known as the insertion (penetration) force. The insertion force causes tissue damage as well as tissue deformation. It has been well studied that tissue deformation causes the needle to deviate from its target thus causing an ineffective procedure. Simulation of surgical procedures provides an effective method for a robot-assisted surgery for pre- and intra-operative planning. Accurate modeling of the mechanical behavior on the interface of surgical needles and organs, specifically the insertion force, has been well recognized as a major challenge. Overcoming such obstacle by development of robust numerical models will enable realistic force feedback to the user during surgical simulation. This study investigates feasibility of predicting the insertion force of bevel-tip needles based on experimental data using neural network modeling. Simulation of the proposed neural network model is performed using Kera’s Python Deep Learning Library with TensorFlow as a backend. The insertion forces of needles with different bevel-tip angles in gel tissue phantom are measured using a specially designed automated needle insertion test setup. Input-output datasets are generated where the inputs are defined as bevel-tip angles and gel tissue phantom stiffness, and the output is defined as the insertion force. A properly trained neural network then maps the input data to the output data and the input-output dataset is supplied to train a neural network. Its performance is then evaluated using different and unseen input-output dataset. This paper shows that the proposed neural network model accurately predicts the insertion force.

A Neural Network Model for Simulating Cloth Texture Deformation

2012 ◽  
Vol 157-158 ◽  
pp. 1608-1613
Author(s):  
Zhi Hong Zhao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Three Dimensional ◽  
Experimental Results ◽  
Curved Surface ◽  
Topological Structures ◽  
The Relationship ◽  
Smooth Texture

This study describes a method to simulate cloth texture deformation using a neural network model. The cloth texture may be represented by its texture colors, positions and its topological structures. In addition, the relationship between the texture colors can be deduced based on the smooth texture and the two and three dimensional texture deformation are correspondingly concerned. A multilayered single direction neural network model is adopted to numerically represent the cloth texture for the purpose of speeding up the simulation. The color values of the points on the cloth deformed curved surface can be calculated with such neural network model. The experimental results show that such method is efficient and executable for the regularized texture deformation.

Sign in / Sign up

Export Citation Format

Share Document