The effects of body awareness, movement direction, visual condition, and movement speed on trunk proprioception

2003 ◽  
Author(s):  
Dan Kelaher ◽  
Naomi Glasscock ◽  
Gary Mirka
Keyword(s):  
Body Awareness ◽  
Movement Speed ◽  
Movement Direction ◽  
Visual Condition

scholarly journals Statistical Characteristics of Unsteady Storms in Radar Observations for the Beijing–Tianjin Region

2015 ◽  
Vol 54 (1) ◽  
pp. 106-116 ◽  
Author(s):  
Yu Wang ◽  
Hong-Qing Wang ◽  
Lei Han ◽  
Yin-Jing Lin ◽  
Yan Zhang
Keyword(s):  
Warm Season ◽  
Environmental Parameters ◽  
Statistical Characteristics ◽  
Movement Speed ◽  
Movement Direction ◽  
Upper Troposphere ◽  
Maximum Value ◽  
Dynamic Factors ◽  
The Mean ◽  
Wind Profiles

AbstractThis study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

scholarly journals Movement speed is biased by prior experience

2014 ◽  
Vol 111 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Ulrike Hammerbeck ◽  
Nada Yousif ◽  
Richard Greenwood ◽  
John C. Rothwell ◽  
Jörn Diedrichsen
Keyword(s):  
Training Group ◽  
Movement Speed ◽  
Movement Direction ◽  
Task Constraints ◽  
Reaching Task ◽  
Optimal Balance ◽  
Fast Training ◽  
Human Participants ◽  
Selection Of ◽  
Dependent Mechanism

How does the motor system choose the speed for any given movement? Many current models assume a process that finds the optimal balance between the costs of moving fast and the rewards of achieving the goal. Here, we show that such models also need to take into account a prior representation of preferred movement speed, which can be changed by prolonged practice. In a time-constrained reaching task, human participants made 25-cm reaching movements within 300, 500, 700, or 900 ms. They were then trained for 3 days to execute the movement at either the slowest (900-ms) or fastest (300-ms) speed. When retested on the 4th day, movements executed under all four time constraints were biased toward the speed of the trained movement. In addition, trial-to-trial variation in speed of the trained movement was significantly reduced. These findings are indicative of a use-dependent mechanism that biases the selection of speed. Reduced speed variability was also associated with reduced errors in movement amplitude for the fast training group, which generalized nearly fully to a new movement direction. In contrast, changes in perpendicular error were specific to the trained direction. In sum, our results suggest the existence of a relatively stable but modifiable prior of preferred movement speed that influences the choice of movement speed under a range of task constraints.

scholarly journals Motor cortical control of movement speed with implications for brain-machine interface control

2014 ◽  
Vol 112 (2) ◽  
pp. 411-429 ◽  
Author(s):  
Matthew D. Golub ◽  
Byron M. Yu ◽  
Andrew B. Schwartz ◽  
Steven M. Chase
Keyword(s):  
Kalman Filter ◽  
Motor Cortex ◽  
Movement Speed ◽  
Movement Direction ◽  
Single Trial ◽  
Population Activity ◽  
Related Information ◽  
Lower Accuracy ◽  
Motor Cortical ◽  
Speed Accuracy

Motor cortex plays a substantial role in driving movement, yet the details underlying this control remain unresolved. We analyzed the extent to which movement-related information could be extracted from single-trial motor cortical activity recorded while monkeys performed center-out reaching. Using information theoretic techniques, we found that single units carry relatively little speed-related information compared with direction-related information. This result is not mitigated at the population level: simultaneously recorded population activity predicted speed with significantly lower accuracy relative to direction predictions. Furthermore, a unit-dropping analysis revealed that speed accuracy would likely remain lower than direction accuracy, even given larger populations. These results suggest that the instantaneous details of single-trial movement speed are difficult to extract using commonly assumed coding schemes. This apparent paucity of speed information takes particular importance in the context of brain-machine interfaces (BMIs), which rely on extracting kinematic information from motor cortex. Previous studies have highlighted subjects' difficulties in holding a BMI cursor stable at targets. These studies, along with our finding of relatively little speed information in motor cortex, inspired a speed-dampening Kalman filter (SDKF) that automatically slows the cursor upon detecting changes in decoded movement direction. Effectively, SDKF enhances speed control by using prevalent directional signals, rather than requiring speed to be directly decoded from neural activity. SDKF improved success rates by a factor of 1.7 relative to a standard Kalman filter in a closed-loop BMI task requiring stable stops at targets. BMI systems enabling stable stops will be more effective and user-friendly when translated into clinical applications.

scholarly journals Radar-derived convective storms' climatology for the Prut River basin: 2003–2017

2019 ◽  
Vol 19 (7) ◽  
pp. 1305-1318 ◽  
Author(s):  
Sorin Burcea ◽  
Roxana Cică ◽  
Roxana Bojariu
Keyword(s):  
River Basin ◽  
Selection Criterion ◽  
Movement Speed ◽  
Movement Direction ◽  
The Black Sea ◽  
Peak Time ◽  
Convective Storm ◽  
Convective Storms ◽  
Storm Cells ◽  
Prut River

Abstract. Weather radar measurements are used to study the climatology of convective storms and their characteristics in the transboundary Prut River basin. The Storm Cell Identification and Tracking (SCIT) algorithm was used to process the volumetric reflectivity measurements, in order to identify, characterize, and track the convective storm cells. The storm attribute table output of the algorithm was used to separate the convective from the stratiform storm cells, by applying a simple selection criterion based on the average vertically integrated liquid (VIL) values. The radar-derived characteristics of convective storms were used to document the spatial and temporal distributions and storm properties in terms of duration, distance travelled, movement direction, and intensity. The results show that 94.3 % of all convective storm cells were detected during May–August, with the peak in July. The peak time for convective storm cells' occurrence was in the afternoon and evening hours between 10:00 and 18:00 UTC. The median duration of a convective storm was 42 min, the median distance travelled was 23 km, and the median movement speed was 7.7 m s−1. The average movement of storms varied with months, but overall most convective storms move from the south-west and south–south-east. Also, the analysis shows that the longer-lasting convective storms were the most intense. The spatial distribution of the convective cells reveals yearly variation patterns and hotspots but also highlights the limitations of radar measurement at longer distances. Reanalysis data suggest that low values of sea level pressure over the Black Sea can act as a dynamical driver of convective storms in the analysed area.

Three-Dimensional Aerial Image Interface, 3DAII

2019 ◽  
Vol 31 (5) ◽  
pp. 657-670 ◽  
Author(s):  
Takafumi Matsumaru ◽  
◽  
Asyifa Imanda Septiana ◽  
Kazuki Horiuchi
Keyword(s):  
Performance Test ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Aerial Image ◽  
Robotic Arm ◽  
Movement Speed ◽  
Movement Direction ◽  
Object Image ◽  
Projected Image ◽  
Dimensional Object

In this paper, we introduce the three-dimensional aerial image interface, 3DAII. This interface reconstructs and aerially projects a three-dimensional object image, which can be simultaneously observed from various viewpoints or by multiple users with the naked eye. A pyramid reflector is used to reconstruct the object image, and a pair of parabolic mirrors is used to aerially project the image. A user can directly manipulate the three-dimensional object image by superimposing a user’s hand-finger or a rod on the image. A motion capture sensor detects the user’s hand-finger that manipulates the projected image, and the system immediately exhibits some reaction such as deformation, displacement, and discoloration of the object image, including sound effects. A performance test is executed to confirm the functions of 3DAII. The execution time of the end-tip positioning of a robotic arm has been compared among four operating devices: touchscreen, gamepad, joystick, and 3DAII. The results exhibit the advantages of 3DAII; we can directly instruct the movement direction and movement speed of the end-tip of the robotic arm, using the three-dimensional Euclidean vector outputs of 3DAII in which we can intuitively make the end-tip of the robotic arm move in three-dimensional space. Therefore, 3DAII would be one important alternative to an intuitive spatial user interface, e.g., an operation device of aerial robots, a center console of automobiles, and a 3D modelling system. A survey has been conducted to evaluate comfort and fatigue based on ISO/TS 9241-411 and ease of learning and satisfaction based on the USE questionnaire. We have identified several challenges related to visibility, workspace, and sensory feedback to users that we would like to address in the future.

scholarly journals Kinematic analysis of bimanual movements during food handling by head-fixed rats

2019 ◽  
Vol 121 (2) ◽  
pp. 490-499 ◽  
Author(s):  
Masakazu Igarashi ◽  
Jeff Wickens
Keyword(s):  
Kinematic Analysis ◽  
Bimanual Coordination ◽  
Tracking System ◽  
Three Dimensional ◽  
Movement Speed ◽  
Movement Direction ◽  
Food Handling ◽  
Bimanual Movements ◽  
Forelimb Movement ◽  
Forelimb Movements

Bimanual coordination, in which both hands work together to achieve a goal, is crucial for the basic needs of life, such as gathering and feeding. Such coordinated motor skill is highly developed in primates, where it has been most extensively studied. Rodents also exhibit remarkable dexterity and coordination of forelimbs during food handling and consumption. However, rodents have been less commonly used in the study of bimanual coordination because of limited quantitative measuring techniques. In this article we describe a high-resolution tracking system that enables kinematic analysis of rat forelimb movement. The system is used to quantify forelimb movements bilaterally in head-fixed rats during food handling and consumption. Forelimb movements occurring naturally during feeding were encoded as continuous three-dimensional trajectories. The trajectories were then automatically segmented and analyzed, using a novel algorithm, according to the laterality of movement speed or the asymmetry of movement direction across the forelimbs. Bilateral forelimb movements were frequently observed during spontaneous food handling. Both symmetry and asymmetry in movement direction were frequently observed, with symmetric bilateral movements quantitatively more common. The proposed method overcomes a limitation in the precise quantification of bimanual coordination in rodents. This enables the use of powerful rodent-based research tools such as optogenetics and chemogenetics in the further investigation of neural mechanisms of bimanual coordination. NEW & NOTEWORTHY We describe a new method for quantifying and classifying three-dimensional, bilateral forelimb trajectories in head-fixed rats. The method overcomes limits on quantifying bimanual coordination in rats. When applied to kinematic analysis of food handling behavior, continuous forelimb trajectories were automatically segmented and classified. Bilateral forelimb movements were observed more frequently than unilateral movements during spontaneous food handling. Both symmetry and asymmetry in movement direction were frequently observed. However, symmetric bilateral forelimb movements were more common.

Motor Cortical Activity During Drawing Movements: Population Representation During Lemniscate Tracing

1999 ◽  
Vol 82 (5) ◽  
pp. 2705-2718 ◽  
Author(s):  
Andrew B. Schwartz ◽  
Daniel W. Moran
Keyword(s):  
Prediction Interval ◽  
Single Cells ◽  
Premotor Cortex ◽  
Neural Representation ◽  
Movement Speed ◽  
Movement Direction ◽  
Population Vector ◽  
Vector Length ◽  
Drawing Task ◽  
Spatial Aspect

Activity was recorded extracellularly from single cells in motor and premotor cortex as monkeys traced figure-eights on a touch-sensitive computer monitor using the index finger. Each unit was recorded individually, and the responses collected from four hemispheres (3 primary motor and 1 dorsal premotor) were analyzed as a population. Population vectors constructed from this activity accurately and isomorphically represented the shape of the drawn figures showing that they represent the spatial aspect of the task well. These observations were extended by examining the temporal relation between this neural representation and finger displacement. Movements generated during this task were made in four kinematic segments. This segmentation was clearly evident in a time series of population vectors. In addition, the [Formula: see text] power law described for human drawing was also evident in the neural correlate of the monkey hand trajectory. Movement direction and speed changed continuously during the task. Within each segment, speed and direction changed reciprocally. The prediction interval between the population vector and movement direction increased in the middle of the segments where curvature was high, but decreased in straight portions at the beginning and end of each segment. In contrast to direction, prediction intervals between the movement speed and population vector length were near-constant with only a modest modulation in each segment. Population vectors predicted direction (vector angle) and speed (vector length) throughout the drawing task. Joint angular velocity and arm muscle EMG were well correlated to hand direction, suggesting that kinematic and kinetic parameters are correlated in these tasks.

scholarly journals Video Based Suspicious Human Behaviour Recognition System

2021 ◽  
Vol 9 (VII) ◽  
pp. 545-554
Author(s):  
Dr. Chetana Prakash
Keyword(s):  
Intelligent System ◽  
Recognition System ◽  
Movement Speed ◽  
Movement Direction ◽  
Human Observer ◽  
Public And Private ◽  
Frame Sequence ◽  
Accurate Performance ◽  
Private And Public ◽  
Motion Characteristics

In recent years, the number of surveillance cameras installed to monitor private and public spaces has increased rapidly. The demand has raised for smarter video surveillance of public and private spaces using intelligent vision systems which can differentiate between 'suspicious' and 'unsuspicious' behaviours according to the human observer. Generally, the video streams are constantly recorded or monitored by operators. In these cases, an intelligent system can give more accurate performance than a human. We have proposed a method called motion influence map under machine learning for representing human activities. Optical-flow is computed for each pixel in a frame that are processed sequentially. The key feature of the proposed motion influence map is that it effectively reflects the motion characteristics such as movement speed, movement direction, and size of the objects or subjects and their interactions within a frame sequence. It further extracts frames of high motion influence values and compares with the testing frames to automatically detect suspicious activities.

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