NASA Task Load Index (TLX) for IOS: an Essential Workload Measure in the Human Performance Toolbox
no abstract available
Gore, Brian F.↗
Engineering topics
Kato, Kenji
Publications and source records attributed to Kato, Kenji.
Machine Learning Prototype App For Recognition of Fruits
As the incidence of obesity and associated negative health consequences is rising, it becomes crucial to monitor the dietary choices of individuals. Unfortunately, traditional methods to collect this information involve collecting food frequency questionnaires from individuals using paper. Electronic food trackers have been developed to collect food data, but they require participants to manually label and describe the content of their meals, and which may be difficult for researchers to interpret in a standardized fashion. Machine learning, however, provides an easy and efficient method for both participants and researchers to label food items with standardized descriptions. This project aims to create a prototype phone application that can identify and label photos of apples. This is done by making a machine learning model through Turicreate, a python module, which is then implemented into an iOS app through Xcode and Swift. The modules used in Swift include CoreML and AVFoundation. This machine learning application will be incorporated with a MealLogger phone app that is also under development. The MealLogger app will be used to keep track of participants' calorie intake and other personal details throughout the sleep study. The machine learning model will present several potential identities of the foods found in the photo, and the user will only need to select the correct option. This will be a user-friendly method for participants to easily log their food consumption without the hard work of manually inputting each and every description. Some limitations to this project include the wide variety of food, including those within different cultures. To deal with this, the model will include the most generic food categories, which the participant may select, and produce a drop-down menu of more specific dishes under that specified category, with the option of self-input. Additional questionnaires may be implemented according to the food type selected This will allow the process to be quick and easy, but also specific for the purpose of analysis. The release of the application will require a much longer process, but the machine learning prototype presents a first step toward an application that may change data analysis for researchers interested in collecting food intake from individuals living in the real world.
Food tracker↗
Live Aircraft Encounter Visualization at FutureFlight Central
Researchers at the National Aeronautics and Space Administration (NASA) have developed an aircraft data streaming capability that can be used to visualize live aircraft in near real-time. During a joint Federal Aviation Administration (FAA)/NASA Airborne Collision Avoidance System flight series, test sorties between unmanned aircraft and manned intruder aircraft were shown in real-time at NASA Ames' FutureFlight Central tower facility as a virtual representation of the encounter. This capability leveraged existing live surveillance, video, and audio data streams distributed through a Live, Virtual, Constructive test environment, then depicted the encounter from the point of view of any aircraft in the system showing the proximity of the other aircraft. For the demonstration, position report data were sent to the ground from on-board sensors on the unmanned aircraft. The point of view can be change dynamically, allowing encounters from all angles to be observed. Visualizing the encounters in real-time provides a safe and effective method for observation of live flight testing and a strong alternative to travel to the remote test range.
Video Streaming↗
Why an Eye Limiting Display Resolution Matters and How to Achieve It
Many factors affect the suitability of an out-the-window simulator visual system. Contrast, brightness, resolution, field-of-view, update rate, scene content and a number of other criteria are common factors often used to define requirements for simulator visual systems. Since 2008, NASA has worked with the USAF on the Operational Based Vision Assessment Program. The purpose of this program has been to provide the USAF School of Aerospace Medicine with a scientific testing laboratory to study human vision and testing standards in an operationally relevant environment. It was determined early in the design that current commercial and military training systems weren't well suited for the available budget as well as the highly research oriented requirements. During various design review meetings, it was determined the OBVA requirements were best met by using commercial-off-the-shelf equipment to minimize technical risk and costs. In this paper we will describe how the simulator specifications were developed in order to meet the research objectives and the resulting architecture and design considerations. In particular we will discuss the image generator architecture, database developments to meet eye limited resolution, and future reusablity in other projects.
flight simulator↗