Mechanisms controlling human head stabilization. I. Head-neck dynamics during random rotations in the horizontal plane

1995 ◽  
Vol 73 (6) ◽  
pp. 2293-2301 ◽  
Author(s):  
F. A. Keshner ◽  
B. W. Peterson
Keyword(s):  
Visual Feedback ◽  
Horizontal Plane ◽  
Mental Arithmetic ◽  
Human Head ◽  
The Body ◽  
Myoelectric Activity ◽  
Head Tracking ◽  
Arithmetic Task ◽  
Head Velocity ◽  
Head Neck

1. Potential mechanisms for controlling stabilization of the head and neck include voluntary movements, vestibular (VCR) and proprioceptive (CCR) neck reflexes, and system mechanics. In this study we have tested the hypothesis that the relative importance of those mechanisms in producing compensatory actions of the head-neck motor system depends on the frequency of an externally applied perturbation. Angular velocity of the head with respect to the trunk (neck) and myoelectric activity of three neck muscles were recorded in seven seated subjects during pseudorandom rotations of the trunk in the horizontal plane. Subjects were externally perturbed with a random sum-of-sines stimulus at frequencies ranging from 0.185 to 4.11 Hz. Four instructional sets were presented. Voluntary mechanisms were examined by having the subjects actively stabilize the head in the presence of visual feedback as the body was rotated (VS). Visual feedback was then removed, and the subjects attempted to stabilize the head in the dark as the body was rotated (NV). Reflex mechanisms were examined when subjects performed a mental arithmetic task during body rotations in the dark (MA). Finally, subjects performed a voluntary head tracking task while the body was kept stationary (VT). 2. Gains and phases of head velocity indicated good compensation to the stimulus in VS and NV at frequencies < 1 Hz. Gains dropped and phases advanced between 1 and 2 Hz, suggesting interference between neural and mechanical components. Above 3 Hz, the gains of head velocity increased steeply and exceeded unity, suggesting the emergence of mechanical resonance.(ABSTRACT TRUNCATED AT 250 WORDS)

Mechanisms controlling human head stabilization. II. Head-neck characteristics during random rotations in the vertical plane

1995 ◽  
Vol 73 (6) ◽  
pp. 2302-2312 ◽  
Author(s):  
E. A. Keshner ◽  
R. L. Cromwell ◽  
B. W. Peterson
Keyword(s):  
Visual Feedback ◽  
Mental Arithmetic ◽  
Vertical Plane ◽  
The Body ◽  
Myoelectric Activity ◽  
Neck Muscle ◽  
Head Tracking ◽  
Frequency Range ◽  
Response Dynamics ◽  
Head Neck

1. In this study we have tested the hypothesis that the mechanisms controlling stabilization of the head-neck motor system can vary with both the frequency and spatial orientation of an externally applied perturbation. Angular velocity of the head with respect to the trunk (neck) and myoelectric activity of two neck muscles (semispinalis capitis and sternocleidomastoid) were recorded in eight seated subjects during pseudorandom rotations of the trunk in the vertical (pitch) plane. Subjects were externally perturbed with a random sum-of-sines stimulus at frequencies ranging from 0.35 to 3.05 Hz. Four instructional sets were presented. Voluntary mechanisms were examined by having the subjects actively stabilize the head in the presence of visual feedback as the body was rotated (VS). Visual feedback was then removed, and the subjects attempted to stabilize the head in the dark as the body was rotated (NV). Reflex mechanisms were examined when subjects performed a mental arithmetic task during body rotations in the dark (MA). Finally, subjects performed a voluntary head tracking task while the body was kept stationary (VT). 2. In VS and NV, gains and phases of head velocity indicated good compensation for the perturbation at frequencies up to 2 Hz. Between 2 and 3 Hz, gains dropped slowly and then steeply descended above 3 Hz as phases became scattered. 3. In MA, gains were lower and exhibited more scatter than in VS and NV at frequencies < 1 Hz. Phases around -180 degrees indicated that compensatory activity was occurring even with these low gains. Between 1 and 2 Hz, response gains steeply ascended, implying that reflex mechanisms were becoming the predominant mechanism for compensation in this frequency range. Above 2 Hz, gains dropped off to 0.5 and lower, but phases remained close to -180 degrees, suggesting that the reflex mechanisms were not dominant in this frequency range, but that they were still contributing toward compensation for the trunk perturbation. 4. Neck muscle electromyographic (EMG) responses were similar in VS, NV, and MA, demonstrating decreasing gains between 0.35 and 1.5 Hz, and then increasing beyond the previous high level of activation. This U-shaped response pattern implies an enhanced participation of neural mechanisms, probably of reflex origin, in the higher frequency range. 5. Patterns observed during external perturbations of the trunk were not apparent in the response dynamics of voluntary head tracking. In VT, subjects successfully tracked the stimulus only at the lowest frequencies of head movement.(ABSTRACT TRUNCATED AT 400 WORDS)

Modeling head tracking of visual targets

2002 ◽  
Vol 12 (1) ◽  
pp. 25-33
Author(s):  
K.J. Chen ◽  
E.A. Keshner ◽  
B.W. Peterson ◽  
T.C. Hain
Keyword(s):  
Control System ◽  
Visual Feedback ◽  
Human Head ◽  
Feedback System ◽  
Optimization Methods ◽  
Head Tracking ◽  
Feedback Systems ◽  
Control Gain ◽  
Head Control ◽  
Visual Targets

Control of the head involves somatosensory, vestibular, and visual feedback. The dynamics of these three feedback systems must be identified in order to gain a greater understanding of the head control system. We have completed one step in the development of a head control model by identifying the dynamics of the visual feedback system. A mathematical model of human head tracking of visual targets in the horizontal plane was fit to experimental data from seven subjects performing a visual head tracking task. The model incorporates components based on the underlying physiology of the head control system. Using optimization methods, we were able to identify neural processing delay, visual control gain, and neck viscosity parameters in each experimental subject.

Effects of Attached Body on Biomechanical Response of the Helmeted Human Head Under Blast

2013 ◽  
Author(s):  
M. Salimi Jazi ◽  
A. Rezaei ◽  
G. Karami ◽  
F. Azarmi ◽  
M. Ziejewski
Keyword(s):  
Boundary Conditions ◽  
Blast Wave ◽  
Computational Study ◽  
Human Head ◽  
Blast Waves ◽  
The Body ◽  
Head Model ◽  
Mechanical Responses ◽  
Dynamic Nonlinear Analysis ◽  
Head Neck

The results of a computational study on the effect of the body on biomechanical responses of a helmeted human head under various blast load orientations are presented in this work. The focus of the work is to study the effects of the human head model boundary conditions on mechanical responses of the head such as variations of intracranial pressure (ICP). In this work, finite element models of the helmet, padding system, and head components are used for a dynamic nonlinear analysis. Appropriate contacts and conditions are applied between different components of the head, pads and helmet. Blast is modeled in a free space. Two different blast wave orientations with respect to head position are set, so that, blast waves tackle the front and back of the head. Standard trinitrotoluene is selected as the high explosive (HE) material. The standoff distance in all cases is one meter from the explosion site and the mass of HE is 200 grams. To study the effect of the body, three different boundary conditions are considered; the head-neck model is free; the base of the neck is completely fixed; and the head-neck model is attached to the body. Comparing the results shows that the level of ICP and shear stress on the brain are similar during the first five milliseconds after the head is hit by the blast waves. It explains the fact that the rest of the body does not have any contribution to the response of the head during the first 5 milliseconds. However, the conclusion is just reasonable for the presented blast situations and different blast wave incidents as well as more directions must be considered.

scholarly journals A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

2021 ◽  
Vol 11 (12) ◽  
pp. 5503
Author(s):  
Munkhjargal Gochoo ◽  
Syeda Amna Rizwan ◽  
Yazeed Yasin Ghadi ◽  
Ahmad Jalal ◽  
Kibum Kim
Keyword(s):  
Deep Learning ◽  
Human Head ◽  
Point Clouds ◽  
Head Tracking ◽  
Complex Environments ◽  
People Tracking ◽  
Practical Applications ◽  
Remote Cameras ◽  
Video Frames ◽  
Benchmark Datasets

Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

Estimation of Cognitive Distraction from Driver Gazing

2017 ◽  
Vol 9 (2) ◽  
pp. 50-60 ◽  
Author(s):  
Akira Yoshizama ◽  
Hiroyuki Nishiyama ◽  
Hirotoshi Iwasaki ◽  
Fumio Mizoguchi
Keyword(s):  
Qualitative Data ◽  
Mental Arithmetic ◽  
Support Vector ◽  
Simulation Environment ◽  
Time Intervals ◽  
Arithmetic Task ◽  
Task Load ◽  
Cognitive Distraction ◽  
Mental Arithmetic Task ◽  
Using Data

In their study, the authors sought to generate rules for cognitive distractions of car drivers using data from a driving simulation environment. They collected drivers' eye-movement and driving data from 18 research participants using a simulator. Each driver drove the same 15-minute course two times. The first drive was normal driving (no-load driving), and the second drive was driving with a mental arithmetic task (load driving), which the authors defined as cognitive-distraction driving. To generate rules of distraction driving using a machine-learning tool, they transformed the data at constant time intervals to generate qualitative data for learning. Finally, the authors generated rules using a Support Vector Machine (SVM).

Development of Autonomous Robotic Cataract Surgery Device

2016 ◽  
Author(s):  
Matthew Francom ◽  
Clinton Burns ◽  
Philip Repisky ◽  
Benjamin Medina ◽  
Alex Kinney ◽  
...  
Keyword(s):  
Cataract Surgery ◽  
Visual Feedback ◽  
Surgical Procedure ◽  
Parallel Mechanism ◽  
Horizontal Plane ◽  
Robotic System ◽  
Design Work ◽  
In Series ◽  
Extracapsular Cataract Surgery ◽  
Future Work

The current rate of incidence of cataracts is increasing faster than treatment capacity, and an autonomous robotic system is proposed to mitigate this by carrying out cataract surgeries. The robot is composed of a three actuator RPS parallel mechanism in series with an actuated rail mounted roller that moves around the eye, and is designed to perform a simplified version of the extracapsular cataract surgery procedure autonomously. The majority of the design work has been completed, and it is projected that the system will have a tool accuracy of 0.167 mm, 0.141 mm, and 0.290 mm in the x, y, and z directions, respectively. Such accuracies are within the acceptable errors of 1.77mm in the x and y directions of the horizontal plane, as well as 1.139 mm in the vertical z direction. Tracking of the tool when moving at 2 mm/s should give increments of 0.08 mm per frame, ensuring constant visual feedback. Future work will involve completing construction and testing of the device, as well as adding the capability to perform a more comprehensive surgical procedure if time allows.

scholarly journals Frequency spectrum of the human head–neck to mechanical vibrations

2017 ◽  
Vol 37 (3) ◽  
pp. 611-618 ◽  
Author(s):  
Bin Yang ◽  
Zheng Shi ◽  
Qun Wang ◽  
Feng Xiao ◽  
Tong-Tong Gu ◽  
...  
Keyword(s):  
Finite Element ◽  
Frequency Spectrum ◽  
Mechanical Vibration ◽  
Three Dimensional ◽  
Human Head ◽  
Element Model ◽  
Mode Shapes ◽  
Joint Disorder ◽  
Biomechanical Responses ◽  
Head Neck

This study is based on a real finite element human head–neck model and concentrates on its numerical vibration characteristic. Frequency spectrum and mode shapes of the finite element model of human head–neck under mechanical vibration have been calculated. These vibration characteristics are in good agreement with the previous studies. The simulated fundamental frequency of 35.25 Hz is fairly similar to the published documents, and rarely reported modal responses such as “mastication” and flipping of nasal lateral cartilages modes, however, are introduced by our three-dimensional modal analysis. These additional modes may be of interest to surgeons or clinicians who are specialized in temporomandibular or rhinoplasty joint disorder. Modal validation in terms of modal shapes proposes a necessity for elaborate modeling to identify each individual part’s extra frequencies. Furthermore, it also studies the influence of damping on resonant frequencies and biomechanical responses. It is discovered that damping has an inverse proportionality between damping effect on natural frequency and that on biomechanical responses.

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