scholarly journals Human Somatosensory Processing and Artificial Somatosensation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Luyao Wang ◽  
Lihua Ma ◽  
Jiajia Yang ◽  
Jinglong Wu
Keyword(s):  
Object Recognition ◽  
Somatosensory System ◽  
Humanoid Robots ◽  
Human Hand ◽  
Somatosensory Processing ◽  
Somatotopic Organization ◽  
Somatosensory Information ◽  
Haptic Object ◽  
Low Threshold ◽  
High Level

In the past few years, we have gained a better understanding of the information processing mechanism in the human brain, which has led to advances in artificial intelligence and humanoid robots. However, among the various sensory systems, studying the somatosensory system presents the greatest challenge. Here, we provide a comprehensive review of the human somatosensory system and its corresponding applications in artificial systems. Due to the uniqueness of the human hand in integrating receptor and actuator functions, we focused on the role of the somatosensory system in object recognition and action guidance. First, the low-threshold mechanoreceptors in the human skin and somatotopic organization principles along the ascending pathway, which are fundamental to artificial skin, were summarized. Second, we discuss high-level brain areas, which interacted with each other in the haptic object recognition. Based on this close-loop route, we used prosthetic upper limbs as an example to highlight the importance of somatosensory information. Finally, we present prospective research directions for human haptic perception, which could guide the development of artificial somatosensory systems.

Somatosensory processing subserving perception and action: Dissociations, interactions, and integration

2007 ◽  
Vol 30 (2) ◽  
pp. 224-230 ◽  
Author(s):  
H. Chris Dijkerman ◽  
Edward H. F. de Haan
Keyword(s):  
Object Recognition ◽  
Perception And Action ◽  
Somatosensory Processing ◽  
Body Representations ◽  
Crossmodal Processing ◽  
Haptic Object

AbstractThe commentaries have raised important points regarding different aspects of our model. Some have queried the nature of the proposed dissociations, whereas others have requested and provided further details regarding aspects we had glossed over. Here we suggest that our approach to identify major processing streams based on the processing goal does not preclude interactions between them. We further specify details regarding body representations, haptic object recognition, and crossmodal processing, but are also aware that several features of the model require further filling in.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

scholarly journals Relationship Between Physiological Response Type (RA and SA) and Vibrissal Receptive Field of Neurons Within the Rat Trigeminal Ganglion

2006 ◽  
Vol 95 (5) ◽  
pp. 3129-3145 ◽  
Author(s):  
Steven C. Leiser ◽  
Karen A. Moxon
Keyword(s):  
Trigeminal Ganglion ◽  
Three Dimensional ◽  
Receptive Fields ◽  
Somatosensory System ◽  
Response Type ◽  
Physiological Level ◽  
Somatotopic Organization ◽  
Tactile Stimuli ◽  
Distinct Features ◽  
The Relationship

Cells within the trigeminal ganglion (Vg) encode all the information necessary for the rat to differentiate tactile stimuli, yet it is the least-studied component in the rodent trigeminal somatosensory system. For example, extensive anatomical and electrophysiological investigations have shown clear somatotopic organization in the higher levels of this system, including VPM thalamus and SI cortex, yet whether this conserved schemata exists in the Vg is unknown. Moreover although there is recent interest in recording from vibrissae-responsive cells in the Vg, it is surprising to note that the locations of these cells have not even been clearly demarcated. To address this, we recorded extracellularly from 350 sensory-responsive Vg neurons in 35 Long-Evans rats. First, we determined three-dimensional locations of these cells and found a finer detail of somatotopy than previously reported. Cells innervating dorsal facial features, even within the whisker region, were more dorsal than midline and ventral features. We also show more cells with caudal than rostral whisker receptive fields (RF), similar to that found in VPM and SI. Next, for each vibrissal cell we determined its response type classified as either rapidly (RA) or slowly (SA) adapting. We examined the relationship between vibrissal RF and response type and demonstrate similar proportions of RA and SA cells responding to any whisker. These results suggest that if RA and SA cells encode distinct features of stimuli, as previously suggested, then at the basic physiological level each whisker has similar abilities to encode for such features.

scholarly journals A New Paradigm for Training Hyperactive Dopamine Transporter Knockout Rats: Influence of Novel Stimuli on Object Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Natalia P. Kurzina ◽  
Anna B. Volnova ◽  
Irina Y. Aristova ◽  
Raul R. Gainetdinov
Keyword(s):  
Object Recognition ◽  
Dopamine Transporter ◽  
Cognitive Task ◽  
Recognition Task ◽  
Long Term Memory ◽  
New Paradigm ◽  
Dopaminergic Transmission ◽  
Novel Objects ◽  
High Level ◽  
The Brain

Attention deficit hyperactivity disorder (ADHD) is believed to be connected with a high level of hyperactivity caused by alterations of the control of dopaminergic transmission in the brain. The strain of hyperdopaminergic dopamine transporter knockout (DAT-KO) rats represents an optimal model for investigating ADHD-related pathological mechanisms. The goal of this work was to study the influence of the overactivated dopamine system in the brain on a motor cognitive task fulfillment. The DAT-KO rats were trained to learn an object recognition task and store it in long-term memory. We found that DAT-KO rats can learn to move an object and retrieve food from the rewarded familiar objects and not to move the non-rewarded novel objects. However, we observed that the time of task performance and the distances traveled were significantly increased in DAT-KO rats in comparison with wild-type controls. Both groups of rats explored the novel objects longer than the familiar cubes. However, unlike controls, DAT-KO rats explored novel objects significantly longer and with fewer errors, since they preferred not to move the non-rewarded novel objects. After a 3 months’ interval that followed the training period, they were able to retain the learned skills in memory and to efficiently retrieve them. The data obtained indicate that DAT-KO rats have a deficiency in learning the cognitive task, but their hyperactivity does not prevent the ability to learn a non-spatial cognitive task under the presentation of novel stimuli. The longer exploration of novel objects during training may ensure persistent learning of the task paradigm. These findings may serve as a base for developing new ADHD learning paradigms.

scholarly journals Marmoset invariant object recognition behavior mirrors that of humans and macaques at fine scale

2020 ◽  
Author(s):  
Alexander J.E. Kell ◽  
Sophie L. Bokor ◽  
You-Nah Jeon ◽  
Tahereh Toosi ◽  
Elias B. Issa
Keyword(s):  
Object Recognition ◽  
Model Organism ◽  
High Standard ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Visual Neuroscience ◽  
Brain Functions ◽  
Invariant Object Recognition ◽  
Small Model ◽  
High Level

The marmoset—a small monkey with a flat cortex—offers powerful techniques for studying neural circuits in a primate. However, it remains unclear whether brain functions typically studied in larger primates can be studied in the marmoset. Here, we asked whether the 300-gram marmosets’ perceptual and cognitive repertoire approaches human levels or is instead closer to rodents’. Using high-level visual object recognition as a testbed, we found that on the same task marmosets substantially outperformed rats and generalized far more robustly across images, all while performing ∼1000 trials/day. We then compared marmosets against the high standard of human behavior. Across the same 400 images, marmosets’ image-by-image recognition behavior was strikingly human-like—essentially as human-like as macaques’. These results demonstrate that marmosets have been substantially underestimated and that high-level abilities have been conserved across simian primates. Consequently, marmosets are a potent small model organism for visual neuroscience, and perhaps beyond.

scholarly journals Visual Behavior Based Bio-Inspired Polarization Techniques in Computer Vision and Robotics

2012 ◽  
pp. 243-272 ◽  
Author(s):  
Abd El Rahman Shabayek ◽  
Olivier Morel ◽  
David Fofi
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Perception ◽  
Polarization Vision ◽  
Visual Behavior ◽  
Biologically Inspired ◽  
Long Time ◽  
Comprehensive Survey ◽  
High Level ◽  
And Robotics

For long time, it was thought that the sensing of polarization by animals is invariably related to their behavior, such as navigation and orientation. Recently, it was found that polarization can be part of a high-level visual perception, permitting a wide area of vision applications. Polarization vision can be used for most tasks of color vision including object recognition, contrast enhancement, camouflage breaking, and signal detection and discrimination. The polarization based visual behavior found in the animal kingdom is briefly covered. Then, the authors go in depth with the bio-inspired applications based on polarization in computer vision and robotics. The aim is to have a comprehensive survey highlighting the key principles of polarization based techniques and how they are biologically inspired.

scholarly journals Robust Image Labeling Using Conditional Random Fields

2021 ◽  
Author(s):  
Maryam Nematollahi Arani
Keyword(s):  
Object Recognition ◽  
Mixture Models ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Gap ◽  
Visual Features ◽  
Image Labeling ◽  
Low Level ◽  
Robust Image ◽  
High Level

Object recognition has become a central topic in computer vision applications such as image search, robotics and vehicle safety systems. However, it is a challenging task due to the limited discriminative power of low-level visual features in describing the considerably diverse range of high-level visual semantics of objects. Semantic gap between low-level visual features and high-level concepts are a bottleneck in most systems. New content analysis models need to be developed to bridge the semantic gap. In this thesis, algorithms based on conditional random fields (CRF) from the class of probabilistic graphical models are developed to tackle the problem of multiclass image labeling for object recognition. Image labeling assigns a specific semantic category from a predefined set of object classes to each pixel in the image. By well capturing spatial interactions of visual concepts, CRF modeling has proved to be a successful tool for image labeling. This thesis proposes novel approaches to empowering the CRF modeling for robust image labeling. Our primary contributions are twofold. To better represent feature distributions of CRF potentials, new feature functions based on generalized Gaussian mixture models (GGMM) are designed and their efficacy is investigated. Due to its shape parameter, GGMM can provide a proper fit to multi-modal and skewed distribution of data in nature images. The new model proves more successful than Gaussian and Laplacian mixture models. It also outperforms a deep neural network model on Corel imageset by 1% accuracy. Further in this thesis, we apply scene level contextual information to integrate global visual semantics of the image with pixel-wise dense inference of fully-connected CRF to preserve small objects of foreground classes and to make dense inference robust to initial misclassifications of the unary classifier. Proposed inference algorithm factorizes the joint probability of labeling configuration and image scene type to obtain prediction update equations for labeling individual image pixels and also the overall scene type of the image. The proposed context-based dense CRF model outperforms conventional dense CRF model by about 2% in terms of labeling accuracy on MSRC imageset and by 4% on SIFT Flow imageset. Also, the proposed model obtains the highest scene classification rate of 86% on MSRC dataset.

Collaborative Autonomy between High-level Behaviors and Human Operators for Remote Manipulation Tasks using Different Humanoid Robots

2016 ◽  
Vol 34 (2) ◽  
pp. 333-358 ◽  
Author(s):  
Alberto Romay ◽  
Stefan Kohlbrecher ◽  
Alexander Stumpf ◽  
Oskar von Stryk ◽  
Spyros Maniatopoulos ◽  
...  
Keyword(s):  
Humanoid Robots ◽  
Remote Manipulation ◽  
Human Operators ◽  
High Level ◽  
Collaborative Autonomy
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