scholarly journals Improving Task Performance through High Level Shared Control of Multiple Robots with a Context Aware Human-Robot Interface

2016 ◽  
Author(s):  
Simon O'Keeffe ◽  
Tomas E. Ward ◽  
Rudi Villing
Keyword(s):  
Task Performance ◽  
Shared Control ◽  
Context Aware ◽  
Multiple Robots ◽  
High Level

scholarly journals The Use of Fitness Testing to Predict Occupational Performance in Tactical Personnel: A Critical Review

2021 ◽  
Vol 18 (14) ◽  
pp. 7480
Author(s):  
Robin Orr ◽  
Takato Sakurai ◽  
Jordan Scott ◽  
Jason Movshovich ◽  
J. Jay Dawes ◽  
...  
Keyword(s):  
Task Performance ◽  
Cardiovascular Fitness ◽  
Fitness Testing ◽  
Occupational Performance ◽  
Occupational Tasks ◽  
Wide Range ◽  
Fitness Tests ◽  
Critical Appraisal Skills ◽  
High Level ◽  
Occupational Task

Tactical personnel work in an occupation that involves tasks requiring a high level of cardiovascular fitness as well as muscular strength and endurance. The aim of this literature review was to identify and critique studies investigating the relationship between physical fitness, quantified by fitness assessment measures, and occupational task performance. Databases were searched for relevant articles which assessed a fitness measure and a measure of occupational performance. A total of 15 articles were included and were deemed to be of acceptable methodological quality (8.4/12 on the Critical Appraisal Skills Programme checklist). Included articles assessed a variety of fitness attributes and occupational tasks. Across tactical groups, there appear to be no standardized fitness tests that can determine occupational performance, with aerobic fitness, anaerobic fitness, strength, endurance, power, and agility all being associated with occupational task performance. A wide range of fitness assessments appears to be required to predict occupational performance within tactical personnel. Efforts should be made to base fitness assessments on occupational demands unique to both the environment and requirements of each individual tactical unit.

scholarly journals Quantifying the signals contained in heterogeneous neural responses and determining their relationships with task performance

2014 ◽  
Vol 112 (6) ◽  
pp. 1584-1598 ◽  
Author(s):  
Marino Pagan ◽  
Nicole C. Rust
Keyword(s):  
Task Performance ◽  
Single Neuron ◽  
Weighted Sums ◽  
Neural Responses ◽  
Signal Modulation ◽  
Neural Data ◽  
Match To Sample ◽  
Neural Populations ◽  
Different Types ◽  
High Level

The responses of high-level neurons tend to be mixtures of many different types of signals. While this diversity is thought to allow for flexible neural processing, it presents a challenge for understanding how neural responses relate to task performance and to neural computation. To address these challenges, we have developed a new method to parse the responses of individual neurons into weighted sums of intuitive signal components. Our method computes the weights by projecting a neuron's responses onto a predefined orthonormal basis. Once determined, these weights can be combined into measures of signal modulation; however, in their raw form these signal modulation measures are biased by noise. Here we introduce and evaluate two methods for correcting this bias, and we report that an analytically derived approach produces performance that is robust and superior to a bootstrap procedure. Using neural data recorded from inferotemporal cortex and perirhinal cortex as monkeys performed a delayed-match-to-sample target search task, we demonstrate how the method can be used to quantify the amounts of task-relevant signals in heterogeneous neural populations. We also demonstrate how these intuitive quantifications of signal modulation can be related to single-neuron measures of task performance ( d′).

Context Reasoning for Predicting Gestational Diabetes Mellitus Using CA-RETE Algorithm

2021 ◽  
Vol 17 (4) ◽  
pp. 41-59
Author(s):  
Deeba K. ◽  
Saravanaguru R. A. K.
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Smart Devices ◽  
Context Aware ◽  
Healthcare Applications ◽  
Context Aware Computing ◽  
Context Reasoning ◽  
Reasoning System ◽  
High Level

Today, IoT-related applications play an important role in scientific world development. Context reasoning emphasizes the perception of various contexts by means of collection of IoT data which includes context-aware decision making. Context-aware computing is used to improve the abilities of smart devices and is increased by smart applications. In this paper, context-aware for the internet of things middleware (CAIM) architecture is used for developing a rule-based system using CA-RETE algorithm. The objective of context-aware systems are concentrated on 1) context reasoning methodologies and analyzing how the technologies will involve enhancing the high-level context data, 2) framework of context reasoning system, 3) implementation of CA-RETE algorithm for predicting gestational diabetes mellitus in healthcare applications.

scholarly journals THE USE OF VEHICLE DATA IN ADAS DEVELOPMENT, VERIFICATION AND FOLLOW-UP ON THE SYSTEM

2020 ◽  
Vol 1 ◽  
pp. 2551-2560
Author(s):  
J. Orlovska ◽  
C. Wickman ◽  
R. Soderberg
Keyword(s):  
Data Driven ◽  
Context Aware ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Volvo Cars ◽  
Vehicle Data ◽  
Assistance Systems ◽  
High Level

AbstractAdvanced Driver Assistance Systems (ADAS) require a high level of interaction between the driver and the system, depending on driving context at a particular moment. Context-aware ADAS evaluation based on vehicle data is the most prominent way to assess the complexity of ADAS interactions. In this study, we conducted interviews with the ADAS development team at Volvo Cars to understand the role of vehicle data in the ADAS development and evaluation. The interviews’ analysis reveals strategies for improvement of current practices for vehicle data-driven ADAS evaluation.

scholarly journals Global Context-Aware Progressive Aggregation Network for Salient Object Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 10599-10606 ◽  
Author(s):  
Zuyao Chen ◽  
Qianqian Xu ◽  
Runmin Cong ◽  
Qingming Huang
Keyword(s):  
Object Detection ◽  
Critical Role ◽  
Saliency Map ◽  
Salient Object Detection ◽  
Semantic Features ◽  
Context Aware ◽  
Salient Object ◽  
Deep Convolutional Neural Networks ◽  
Global Context ◽  
High Level

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple-level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Automated Context Formalization for Context-aware Specification Approach

2018 ◽  
Vol 9 (3) ◽  
pp. 23-47
Author(s):  
Amel Benabbou ◽  
Safia Nait-Bahloul
Keyword(s):  
Environmental Conditions ◽  
Use Cases ◽  
Domain Specific Language ◽  
Context Aware ◽  
Effective Technique ◽  
Specific Language ◽  
Requirement Specification ◽  
Domain Specific ◽  
High Level ◽  
Formal Requirements

Requirement specification is a key element in model-checking verification. The context-aware approach is an effective technique for automating the specification of requirement considering specific environmental conditions. In most of existing approaches, there is no support of this crucial task and are mainly based on the considerable efforts and expertise of engineers. A domain-specific language, called CDL, has been proposed to facilitate the specification of requirement by formalizing contexts. However, the feedback has shown that manually writing CDL is hard, error prone and difficult to grasp on complex systems. In this article, the authors propose an approach to automatically generate CDL models using (IODs) elaborated through transformation chains from textual use cases. They offer an intermediate formalism between informal use cases scenarios and CDL models allowing to engineers to manipulate with familiar artifacts. Thanks to such high-level formalism, the gap between informal and formal requirements is reduced; consequently, the requirement specification is facilitated.

Visual Attention and Coactivation of Response Decisions for Features from Different Dimensions

2002 ◽  
Vol 13 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Uri Feintuch ◽  
Asher Cohen
Keyword(s):  
Visual Attention ◽  
Task Performance ◽  
Redundancy Gain ◽  
Attentional Focus ◽  
Action Model ◽  
Different Dimensions ◽  
Response Decisions ◽  
High Level ◽  
Dimensional Module

The role of visual attention in task performance has been extensively debated. On the basis of the dimensional-action model, we hypothesized that a major role of attention is to transfer response decisions from targets on which it is focused to high-level centers dealing with response execution. This hypothesis predicts that response decisions for two targets will interact only when attention is focused on both targets, and only when the response to the targets is defined by different dimensions. Three experiments, using the redundancy-gain paradigm, tested and confirmed this prediction. Experiment 1 showed that coactivation of two cross-dimensional targets occurred only when the targets were positioned in the same location, not when they were in separate locations. Experiment 2 manipulated the focus of attention and showed that coactivation can occur even for targets positioned in different locations if they are both within the attentional focus. Experiment 3 showed that this attention-induced coactivation does not occur for targets from the same dimensional module. These results suggest that a major role of attention is postperceptual and involves gating of selected responses to executive functions.

scholarly journals Learning a Novel Myoelectric-Controlled Interface Task

2008 ◽  
Vol 100 (4) ◽  
pp. 2397-2408 ◽  
Author(s):  
Saritha M. Radhakrishnan ◽  
Stuart N. Baker ◽  
Andrew Jackson
Keyword(s):  
Task Performance ◽  
Motor System ◽  
Two Dimensional ◽  
Trained Subjects ◽  
Flexible Control ◽  
Hand Muscles ◽  
Myoelectric Prostheses ◽  
Control Signals ◽  
Arm Muscles ◽  
High Level

Control of myoelectric prostheses and brain–machine interfaces requires learning abstract neuromotor transformations. To investigate the mechanisms underlying this ability, we trained subjects to move a two-dimensional cursor using a myoelectric-controlled interface. With the upper limb immobilized, an electromyogram from multiple hand and arm muscles moved the cursor in directions that were either intuitive or nonintuitive and with high or low variability. We found that subjects could learn even nonintuitive arrangements to a high level of performance. Muscle-tuning functions were cosine shaped and modulated so as to reduce cursor variability. Subjects exhibited an additional preference for using hand muscles over arm muscles, which resulted from a greater capacity of these to form novel, task-specific synergies. In a second experiment, nonvisual feedback from the hand was degraded with amplitude- and frequency-modulated vibration. Although vibration impaired task performance, it did not affect the rate at which learning occurred. We therefore conclude that the motor system can acquire internal models of novel, abstract neuromotor mappings even in the absence of overt movements or accurate proprioceptive signals, but that the distal motor system may be better suited to provide flexible control signals for neuromotor prostheses than structures related to the arm.

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