Browse Topic: Mental processes
Chronic stress can lead to increased blood pressure and cardiovascular disease, decreased immune function, depression, and anxiety. Unfortunately, the tools we use to monitor stress are often imprecise or expensive, relying on self-reporting questionnaires and psychiatric evaluations.
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the
Biofeedback training is a technology that enhances cognitive and emotional capabilities, empowering peak performance. What sets it apart is the Biocybernetics adaptation systems, which not only collect biofeedback data but also dynamically adjust your environment based on physiological signals. Imagine surroundings adapting — changing lighting, sounds, and more — in response to your biofeedback. Traditionally confined to clinical or training rooms, the real innovation is its integration into daily life. This system offers a new level of self-regulation. Users can navigate daily life venues with real-time insights into their physiological signals, providing continuous feedback and motivation for cognitive and emotional control. Efforts yield positive surroundings, fostering well-being and peak performance.
Meet CARMEN — short for Cognitively Assistive Robot for Motivation and Neurorehabilitation — a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.
Engineers at the University of California San Diego in collaboration with clinicians, people with MCI, and their care partners have developed CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation — a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.
Using electrical impedance tomography (EIT), researchers have developed a system using a flexible tactile sensor for objective evaluation of fine finger movements. Demonstrating high accuracy in classifying diverse pinching motions, with discrimination rates surpassing 90 percent, this innovation holds potential in cognitive development and automated medical research.
Advances in healthcare and medical treatments have led to longer life expectancies in many parts of the world. As people receive better healthcare and management of other health conditions, they are more likely to reach an age where neurodegenerative diseases become a greater risk. Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), are complex and can affect various aspects of a person's cognitive, motor, and sensory functions.
In this study, a novel assessment approach of in-vehicle speech intelligibility is presented using psychometric curves. Speech recognition performance scores were modeled at an individual listener level for a set of speech recognition data previously collected under a variety of in-vehicle listening scenarios. The model coupled an objective metric of binaural speech intelligibility (i.e., the acoustic factors) with a psychometric curve indicating the listener’s speech recognition efficiency (i.e., the listener factors). In separate analyses, two objective metrics were used with one designed to capture spatial release from masking and the other designed to capture binaural loudness. The proposed approach is in contrast to the traditional approach of relying on the speech recognition threshold, the speech level at 50% recognition performance averaged across listeners, as the metric for in-vehicle speech intelligibility. Results from the presented analyses suggest the importance of
Artificial intelligence (AI) has become prevalent in many fields in the modern world, ranging from vacuum cleaners to lawn mowers and commercial automobiles. These capabilities are continuing to evolve and become a part of more products and systems every day, with numerous potential benefits to humans. AI is of particular interest in autonomous vehicles (AVs), where the benefits include reduced cognitive workload, increased efficiency, and improved safety for human operators. Numerous investments from academia and industry have been made recently with the intent of improving the enabling technologies for AVs. Google and Tesla are two of the more well-known examples in industry, with Google developing a self-driving car and Tesla providing its Full Self-Driving (FSD) autopilot system. Ford and BMW are also working on their own AVs.
Prior investigations of swarm robot control focus on optimizing communication and coordination between agents, with at most one human control scheme, or with discrete (rather than continuous) human control schemes. In these studies, focus tends to be on human-robot interactions, including human-machine gesture interfaces, human-machine interaction during conversation, or evaluation of higher-level mental states like comfort, happiness and cognitive load. While there is early work in human control of Unmanned Arial Vehicles (UAVs) and interface design, there are few systematic studies of how human operators perceive fundamental properties of small swarms of ground-based semi-autonomous robots. Therefore, the goal of this study is to better understand how humans perceive swarms of semi-autonomous agents across a range of conditions.
Modern in-vehicle experiences are brimming with functionalities and convenience driven by automation, digitalization, and electrification. While automotive manufacturers are competing to provide the best systems to their customers, there is no common ground to evaluate these in-vehicle experiences as they become increasingly complex. Existing automotive guidelines do not offer thresholds for cognitive distraction, or—more appropriately—“disengagement.” What can researchers can do to change this? Evaluation of the In-vehicle Experience discusses acceptable levels of disengagement by evaluating the driving context and exploring how system reliability can translate to distraction and frustration. It also covers the need to test systems for their complexity and ease of use, and to prevent users from resorting to alternative systems while driving (e.g., smartphones). It highlights the value in naturalistic data generation using vehicles already sold to customers and the issues around
A team of Cornell University researchers has laid the foundation for developing a new class of untethered soft robots that can achieve more complex motions with less reliance on explicit computation. By taking advantage of viscosity — the very thing that previously stymied the movement of soft robots — the new approach offloads control of a soft robot’s cognitive capability from the “brain” onto the body using the robot’s mechanical reflexes and ability to leverage its environment.
Automated driving is considered a key technology for reducing traffic accidents, improving road utilization, and enhancing transportation economy and thus has received extensive attention from academia and industry in recent years. Although recent improvements in artificial intelligence are beginning to be integrated into vehicles, current AD technology is still far from matching or exceeding the level of human driving ability. The key technologies that need to be developed include achieving a deep understanding and cognition of traffic scenarios and highly intelligent decision-making. Automated Vehicles, the Driving Brain, and Artificial Intelligenceaddresses brain-inspired driving and learning from the human brain's cognitive, thinking, reasoning, and memory abilities. This report presents a few unaddressed issues related to brain-inspired driving, including the cognitive mechanism, architecture implementation, scenario cognition, policy learning, testing, and validation. Click here
Reliably operating electromagnetic (EM) systems including radar, communications, and navigation, while deceiving or disrupting the adversary, is critical to success on the battlefield. As threats evolve, electronic warfare (EW) systems must remain flexible and adaptable, with performance upgrades driven by the constant game of cat and mouse between opposing systems. This drives EW researchers and systems engineers to develop novel techniques and capabilities, based on new waveforms and algorithms, multifunction RF systems, and cognitive and adaptive modes of operation.
How do different parts of the brain communicate with each other during learning and memory formation? A new study by researchers at the University of California San Diego takes a first step at answering this fundamental neuroscience question.
Today, as the spread of vehicles equipped with autonomous driving functions increases, accidents caused by autonomous vehicles are also increasing. Therefore, issues regarding safety and reliability of autonomous vehicles are emerging. Various studies have been conducted to secure the safety and reliability of autonomous vehicles, and the application of the International Organization for Standardization (ISO) 26262 standard for safety and reliability improvement and the importance of verifying the safety of autonomous vehicles are increasing. Recently, Mobileye proposed an RSS model called Responsibility Sensitive Safety, which is a mathematical model that presents the standardization of safety guarantees of the minimum requirements that all autonomous vehicles must meet. In this article, the RSS model that ensures safety and reliability was derived to be suitable for variable focus function cameras that can cover the cognitive regions of radar and lidar with a single camera. It is
Acoustic range managers need a better system for identifying high-value decision points before conducting test events. When this research was conducted, a qualitative process model that represents the acoustic range decision process did not exist.
How do different parts of the brain communicate with each other during learning and memory formation? A new study by researchers at the University of California San Diego takes a first step at answering this fundamental neuro-science question.
The performance of persons who watch surveillance videos, either in real-time or recordings, can vary with their level of expertise. It is reasonable to suppose that some of the performance differences might be due, at least in part, to the way experts scan a visual scene versus the way novices might scan the same scene. For example, experts might be more systematic or efficient in the way they scan a scene compared to novices. Even within the same person, video surveillance performance can vary with factors such as fatigue. Again, differences in the way their eyes scan a scene might account for some of the differences. Full Motion Video (FMV) “Eyes-on” intelligence analysts, in particular, actively scan video scenes for items of interest for long periods of time.
In the stage of automobile industry transition from SAE level “0,1” low autonomous through “2,3,4” human-in-the-loop and ultimately “5” fully autonomous driving, advanced driving monitor system is critical to understand the status, performance, and behavior of drivers for next-generation intelligent vehicles. By making necessary warnings or adjustments, they could operate collaboratively to ensure a safe and efficient traffic environment. The performance and behavior can be viewed as a reflection of the driver’s cognitive workload, which corresponds as well to the environment of their driving scenarios. In this study, image features extracted from driving scenarios, as well as additional environmental features were utilized to classify driving workload levels for different driving scenario video clips. As a continuing study of exploring transfer learning capability, two transfer learning approaches for feature extraction, image segmentation mask transfer approach and image-fixation map
Andrew Grove (founder CEO Intel) defines strategic inflectionpoints as what happens to a business when a major event alters itsfundamentals. The Covid-19 pandemic is one such historic event thatis changing fundamental business assumptions in the Oil industry.Companies with a hunter-gatherer mindset will ride this wave withthe help of technologies that make their operations lean andefficient. Current developments in AI, specifically aroundCognitive Sciences is one such area that will empower the earlyadopters to a many-fold improvement in engineering and researchproductivity. This paper explores 'how to augment the humanintelligence with insights from engineering literature, leveragingCognitive AI techniques?'. The key challenge of acquiringknowledge from engineering literature (patents, books, journals,articles, papers etc.) is the sheer volume at which it growsannually (100s of millions existing and new papers growing at 40%year-on-year as per IDC). 6 million+ patents are filed every
This document addresses the operational safety and human factors aspects of unauthorized laser illumination events in navigable airspace. The topics addressed include operational procedures, training, and protocols that flight crew members should follow in the event of a laser exposure. Of particular emphasis, this document outlines coping strategies for use during critical phases of flight. Although lasers are capable of causing retinal damage, most laser cockpit illuminations, to date, has been relatively low in irradiance causing primarily startle reactions, visual glare, flashblindness and afterimages. Permanent eye injuries from unauthorized laser exposures have been extremely rare. This document describes pilot operational procedures in response to the visual disruptions associated with low to moderate laser exposures that pilots are most likely to encounter during flight operations. With education and training, pilots can take actions that safeguard both their vision and the
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