Search

In the modern world of technological progress, systems and interfaces are becoming more and more complex. As a consequence, it is a crucial to design the human-computer interaction in the most optimal way to improve the user experience. The construct of Mental Workload is a valid metric that can be used for such a goal. Among the different ways of measuring Mental Workload, self-reporting procedures are the most adopted for their ease of use and application. This research is focused on the application of Machine Learning as an alternative to theory-driven approaches for Mental Workload measurement. In particular, the study is aimed at comparing the classification accuracy of a set of induced models, from an existing dataset, to the mental workload indexes generated by well-known subjective mental workload assessment techniques - namely the Nasa Task Load Index and the Workload profile instruments.

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.

This is the pre-print version of the Article - Copyright @ 2011 Taylor & Francis With nearly 60% of domestic energy consumption relating to space heating, the interaction between users and their heating controls is crucial in reducing consumption. Yet, many heating controls are complex and exclude people due to the demands placed upon their capabilities in terms of vision, reach, dexterity and thinking. This study explores the scale of and reasons for user exclusion in relation to digital programmable thermostats. The Exclusion Calculator was used to estimate the percentage of the population excluded from the use of three products. Full user testing was then conducted to elicit specific usability problems of the devices. The participants were a group of 14 younger users (aged 24–44) and 10 older users (aged 62–75). The exclusion calculations underestimated the actual exclusion significantly for both age ranges (p

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers' workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.

A pilot study is reported to identify an improved method of evaluating digital user interfaces in health care. Experience and developments from the aviation industry and the NASA-TLX mental workload assessment tools are applied in conjunction with Nielsen heuristics for evaluating an Electronic Health Record System in an Irish hospital. The NASA-TLX performs subjective workload assessments on operators working with various human-computer systems. Results suggest that depending on the cognitive workload and the working context of users, the usability will differ for the same digital interface. We conclude that incorporating the NASA-TLX with Nielsen's heuristics offers a more reliable method in design and evaluation of digital user interfaces in clinical environments, since the healthcare work context is taken into account. Improved interfaces can be expected to reduce medical errors and improve patient care.

Den tekniska utvecklingen har lett till att massiva mängder av information sänds, i högahastigheter. Detta flöde måste vi lära oss att hantera. För att maximera nyttan av de nyateknikerna och undkomma de problem som detta enorma informationsflöde bär med sig, börinteraktionskvalitet studeras. Vi måste anpassa gränssnitt efter användaren eftersom denneinte har möjlighet att anpassa sig till, och sortera i för stora informationsmängder. Vi måsteutveckla system som gör människan mer effektiv vid användande av gränssnitt.För att anpassa gränssnitten efter användarens behov och begränsningar krävs kunskaperom den mänskliga kognitionen. När kognitiv belastning studeras är det viktigt att en såflexibel, lättillgänglig och icke-påträngande teknik som möjligt används för att få objektivamätresultat, samtidigt som pålitligheten är av största vikt. För att kunna designa gränssnittmed hög interaktionskvalitet krävs en teknik att utvärdera dessa. Målet med uppsatsen är attfastställa en mätmetod väl lämpad för mätning av interaktionskvalitet.För mätning av interaktionskvalitet rekommenderas en kombinering av subjektiva ochfysiologiska mätmetoder, detta innefattar en kombination av Functional near-infraredspecroscopy; en fysiologisk mätmetod som mäter hjärnaktiviteten med hjälp av ljuskällor ochdetektorer som fästs på frontalloben, Electrodermal activity; en fysiologisk mätmetod sommäter hjärnaktiviteten med hjälp av elektroder som fästs över skalpen och NASA task loadindex; en subjektiv, multidimensionell mätmetod som bygger på kortsortering och mäteruppfattad kognitiv belastning i en sammanhängande skala. Mätning med hjälp av dessametoder kan resultera i en ökad interaktionskvalitet i interaktiva, fysiska och digitalagränssnitt. En uppskattning av interaktionskvalitet kan bidra till att fel vid interaktionminimeras, vilket innebär en förbättring av användares upplevelse vid interaktion. Technical developments have led to the broadcasting of massive amounts of information, athigh velocities. We must learn to handle this flow. To maximize the benefits of newtechnologies and avoid the problems that this immense information flow brings, interactionquality should be studied. We must adjust interfaces to the user because the user does nothave the ability to adapt and sort overly large amounts of information. We must developsystems that make the human more efficient when using interfaces.To adjust the interfaces to the user needs and limitations, knowledge about humancognitive processes is required. When cognitive workload is studied it is important that aflexible, easily accessed and non assertive technique is used to get unbiased results. At thesame time reliability is of great importance. To design interfaces with high interaction quality,a technique to evaluate these is required. The aim of this paper is to establish a method that iswell suited for measurement of interaction quality.When measuring interaction quality, a combination of subjective and physiologicalmethods is recommended. This comprises a combination of Functional near-infraredspectroscopy; a physiological measurement which measures brain activity using light sourcesand detectors placed on the frontal lobe, Electrodermal activity; a physiological measurementwhich measures brain activity using electrodes placed over the scalp and NASA task loadindex; a subjective, multidimensional measurement based on card sorting and measures theindividual perceived cognitive workload on a continuum scale. Measuring with these methodscan result in an increase in interaction quality in interactive, physical and digital interfaces.An estimation of interaction quality can contribute to eliminate interaction errors, thusimproving the user’s interaction experience.

Introduction: It is estimated that there are currently over 3 million patients receiving dialysis treatment worldwide. With effective pre-dialysis counselling, a majority of patients choose the home-based therapy peritoneal dialysis (PD) but only approximately 11% of prevalent dialysis patients use this modality. Connection-assist devices can overcome the challenges posed by decreased manual dexterity and/or visual acuity, and can allow more patients to be treated with home-based therapies. As part of the CE marking authorization, a connection device has been evaluated for safety and ease of use in a usability study. Methods: Fifteen patients and nine carers volunteered in this study, ranging from 23 to 86 years in age and from 0.3 to 24 years in experience in the PD therapy. The operating cycle consisted of eight tasks, each having several handling steps. The data analysis focused on the task effectiveness and the subjects’ subjective feedback from the NASA task load index (N-TLX) questionnaire and semi-structured interviews. Results: Of 1248 handling steps performed in total, 38 use errors were observed and discussed with the subjects. This equates to 97% of all handling steps being performed safely and easily. In all six dimensions of the N-TLX, more than 50 percent of subjects rated the task load 50 points or less on the scale. Regarding the handling of the device, 13 of 15 of the patients and 8 of 9 of the carers gave positive feedback. Discussion: Safety and ease of use was demonstrated by evaluating task effectiveness (97% SU), interviews and N-TLX. Additionally the study provided valuable individual user feedback, which will inform the final design of the system for PD. The majority of patients and carers gave positive feedback regarding use and handling of this connection device. Innovative connection devices in general promise to reduce the barriers to using this home-based dialysis treatment., Patient Preference and Adherence, 13, ISSN:1177-889X