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A novel autonomous vehicle interface for older adults with cognitive impairment
Abstract
The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.
As the population is ageing, the number of older adults with cognitive impairment (CI) is increasing. Automated vehicles (AVs) can improve independence and enhance the mobility of these individuals. This study aimed to: (1) understand the perception of older adults (with and without CI) and stakeholders providing services and supports regarding care and transportation about AVs, and (2) suggest potential solutions to improve the perception of AVs for older adults with mild or moderate CI. A survey was conducted with 435 older adults with and without CI and 188 stakeholders (e.g. caregivers). The results were analysed using partial least square – structural equation modelling and multiple correspondence analysis. The findings suggested relationships between older adults’ level of cognitive impairment, mobility, knowledge of AVs, and perception of AVs. The results provided recommendations to improve older adults’ perception of AVs including education and adaptive driving simulation-based training.
Many individuals with mild cognitive impairment (MCI) struggle with the decision to cease driving prematurely due to cognitive deficiencies in processing speed, memory, attention, judgment, or visuospatial skills. Highly automated vehicles (AVs) can be used as assistive technologies for individuals with MCI, performing all driving tasks for them, and extending their safe and independent mobility. However, use of highly AVs introduces a different set of challenges than manual driving. These challenges rely more heavily on memory and decision-making abilities of its users. Therefore, the objective of this study was to investigate the barriers that individuals with MCI face when interacting with highly AVs to support the design of in-vehicle interfaces that will help users with non-driving related travel tasks. Specifically, we aimed to design a system for providing reminders and other guidance to individuals with MCI during solo trips in personally owned or private AVs. To achieve this goal, we conducted individual interviews with experts in driving rehabilitation, rehabilitation professionals, and academics with a focus on assistive technologies, rehabilitation sciences, engineering, and inclusive design ( N = 7). The thematic analysis of the data from these subject matter experts highlighted the necessity for reminders, defined as system-initiated prompts that assist individuals with remembering or acknowledging a specific piece of information, and resulted in a set of user needs. We then created a set of prototype interfaces based on these user needs that help individuals with MCI complete their trips by providing reminders of important trip related information. The reminders system was designed to be displayed on a central dashboard display placed in front of the passenger’s seat and present important information that address the users’ difficulties with prospective memory, remembering and understanding the features of the highly AV, and understanding the current trip status. This study serves as an initial investigation into ensuring that the experience of using highly AVs is inclusive and can support the needs of individuals with MCI. The designed interactions proposed by the reminders system can serve as a platform for future in-vehicle interfaces.
Background and objectives:
Driving cessation is a complex challenge with significant emotional and health implications for people with dementia, which also affects their family care partners. Automated vehicles (AVs) could potentially be used to delay driving cessation and its adverse consequences for people with dementia and their care partners. Yet, no study to date has investigated whether care partners consider AVs to be potentially useful for people with dementia.
Research design and methods:
This mixed-methods study assessed the views of 20 former or current family care partners of people with dementia on AV use by people with dementia. Specifically, questionnaires and semistructured interviews were used to examine care partners' acceptance of AV use by people with dementia and their views about the potential usefulness of AVs for people with dementia.
Results:
The results demonstrated that care partners identified possible benefits of AV use by people with dementia such as their anticipated higher social participation. However, care partners also voiced major concerns around AV use by people with dementia and reported significantly lower levels of trust in and perceived safety of AVs if used by the person with dementia in their care compared to themselves. Care partners' concerns about AV use by people with dementia included concerns around the driving of people with dementia that AVs are not designed to address; concerns that are specific to AVs but are not relevant to the nonautomated driving of people with dementia; and concerns that arise from existing challenges around the nonautomated driving of people with dementia but may be exacerbated by AV use.
Discussion and implications:
Findings from this study can inform future designs of AVs that are more accessible and useful for people with dementia.
Trust is an important factor in building acceptance of autonomous vehicles within our society, but the complex nature of trust makes it challenging to design for an appropriate level of trust. This can lead to instances of mistrust and/or distrust between users and AV’s. Designing for calibrated trust is a possible option to address this challenge. Existing research on designing for calibrated trust focuses on the human machine interaction (HMI), while from literature we infer that trust creation beings much before the first interaction between a user and an AV. The goal of our research is to broaden the scope of calibrated trust, by exploring the pre-use phase and understand the challenges faced in calibration of trust. Within our study 16 mobility experts were interviewed and a thematic analysis of the interviews was conducted. The analysis revealed the lack of clear communication between stakeholders, a solutionism approach towards designing and lack of transparency in design as the prominent challenges. Building on the research insights, we briefly introduce the Calibrated Trust Toolkit as our design solution, and conclude by proposing a sweet spot for achieving calibration of trust between users and autonomous vehicles.
Emerging evidence suggests that atypical changes in driving behaviors may be early signals of mild cognitive impairment (MCI) and dementia. This study aims to assess the utility of naturalistic driving data and machine learning techniques in predicting incident MCI and dementia in older adults. Monthly driving data captured by in-vehicle recording devices for up to 45 months from 2977 participants of the Longitudinal Research on Aging Drivers study were processed to generate 29 variables measuring driving behaviors, space and performance. Incident MCI and dementia cases (n = 64) were ascertained from medical record reviews and annual interviews. Random forests were used to classify the participant MCI/dementia status during the follow-up. The F1 score of random forests in discriminating MCI/dementia status was 29% based on demographic characteristics (age, sex, race/ethnicity and education) only, 66% based on driving variables only, and 88% based on demographic characteristics and driving variables. Feature importance analysis revealed that age was most predictive of MCI and dementia, followed by the percentage of trips traveled within 15 miles of home, race/ethnicity, minutes per trip chain (i.e., length of trips starting and ending at home), minutes per trip, and number of hard braking events with deceleration rates ≥ 0.35 g. If validated, the algorithms developed in this study could provide a novel tool for early detection and management of MCI and dementia in older drivers. View Full-Text
Keywords: aging; Alzheimer’s disease and related dementias; artificial intelligence; dementia; driving patterns; machine learning; mild cognitive impairment; naturalistic driving study; random forests; screening
As the population across the globe continues to dramatically increase, the prevalence of cognitive impairment and dementia will inevitably increase as well, placing increasing burden on families and health care systems. Technological advancements over the past decade provide potential benefit in not only relieving caregiver burden of caring for a loved one with dementia, but also enables individuals with dementia to age in place. Technological devices have served to improve functioning, tracking and mobility. Similarly, smartphones, tablets and the ubiquitous world wide web have facilitated the dissemination of health information to previously hard to reach populations largely through use of various social media platforms. In this review, we discuss the current and future uses of technology via devices and social media to promote healthy aging in individuals with dementia, and also limitations and challenges to consider in the future.
This paper presents a study on user experience (UX) design and evaluation of warning systems intended for older adults in semi-autonomous cars. We used combinations of visual, auditory, and speech modalities to design the warning alerts and created three low-fidelity, video-based prototypes. We conducted user tests with elderly drivers, both in the lab and remotely, within a test-and-refine approach involving three experiments. The methods used for data collection included Wizard of Oz, standard questionnaires and interviews. We
collected qualitative user feedback and self-reported ratings of user experience and cognitive load. We report on the iterative development of our design solution, findings from these user studies, and our methodological insights, that UX researchers and practitioners could use in similar settings.
Automated vehicles are anticipated to have benefits for older adults in maintaining their mobility and autonomy. These anticipated benefits can only be realized if this technology is accepted and thus used by older adults. However, it remains unclear how certain factors affect older adults' acceptance of automated vehicles. This study investigated the extent to which older adults' acceptance of fully automated vehicles are affected by exposure to automated vehicle technology (pre-vs. post-exposure), driving style (manual style relative to automated style), driving conditions (clear, rain, traffic), and age. Thirty-six older adults (M = 73.25, SD = 5.96) completed non-automated (manual) and fully automated driving scenarios under different driving conditions in a high-fidelity driving simulator. The fully automated driving scenarios were designed to be reliably driven by the system in a conservative driving style. Driving conditions included clear daytime, rain, and high-traffic. Pre-and post-exposure to the simulated fully automated driving experience, participants rated their comfort level with fully automated vehicles (FAVs). Additionally, after each driving condition, participants answered a validated questionnaire on their acceptance of the simulated fully automated experience for each respective driving condition. Age and driving style were found to have a significant effect on older adults' acceptance of FAVs, with older age and greater dissimilarity of an individual's manual driving style from the FAV's driving style being associated with lower acceptance. The results suggest that if reliability of fully automated vehicles is ultimately ensured and is demonstrated to the older adults, their acceptance of fully automated vehicles is generally high, particularly if the FAV is operated in a style similar to their own.
Population ageing is already having an impact on societies. This study briefly reviews associated technologies employed within the framework of elderly care, namely: robotic nursing, ambient assisted living and assistive robotics. Their current status is considered together with their potential and the associated implementation challenges.
In the evolution of technical systems, freedom from error and early adoption plays a major role for market success and to maintain competitiveness. In the case of automated driving, we see that faulty systems are put into operation and users trust these systems, often without any restrictions. Trust and use are often associated with users' experience of the driver-vehicle interfaces and interior design. In this work, we present the results of our investigations on factors that influence the perception of automated driving. In a simulator study, N=48 participants had to drive a SAE level 2 vehicle with either perfect or faulty driving function. As a secondary activity, participants had to solve tasks on an infotainment system with varying aesthetics and usability (2x2). Results reveal that the interaction of conditions significantly influences trust and UX of the vehicle system. Our conclusion is that all aspects of vehicle design cumulate to system and trust perception.
A major promise of automated vehicles is to render it possible for drivers to engage in nondriving related tasks, a setting where the execution pattern will switch from concurrent to sequential multitasking. To allow drivers to safely and efficiently switch between multiple activities (including vehicle control in case of Take-Over situations), we postulate that future vehicles should incorporate capabilities of attentive user interfaces, that precisely plan the timing of interruptions based on driver availability. We propose an attention aware system that issues Take-Over Requests (1) at emerging task boundaries and (2) directly on consumer devices such as smartphones or tablets. Results of a driving simulator study (N=18), where we evaluated objective, physiological, and subjective measurements, confirm our assumption: attention aware Take-Over Requests have the potential to reduce stress, increase Take-Over performance, and can further raise user acceptance/trust. Consequently, we emphasize to implement attentive user interfaces in future vehicles.
Many social science studies are based on coded in-depth semistructured interview transcripts. But researchers rarely report or discuss coding reliability in this work. Nor is there much literature on the subject for this type of data. This article presents a procedure for developing coding schemes for such data. It involves standardizing the units of text on which coders work and then improving the coding scheme’s discriminant capability (i.e., reducing coding errors) to an acceptable point as indicated by measures of either intercoder reliability or intercoder agreement. This approach is especially useful for situations where a single knowledgeable coder will code all the transcripts once the coding scheme has been established. This approach can also be used with other types of qualitative data and in other circumstances.
Qualitative data analysis software (QDAS) programs are well-established research tools, but little is known about how researchers use them. This article reports the results of a content analysis of 763 empirical articles, published in the Scopus database between 1994 and 2013, which explored how researchers use the ATLAS.ti™ and NVivo™ QDAS programs.* The analysis specifically investigated who is using these tools (in terms of subject discipline and author country of origin), and how they are being used to support research (in terms of type of data, type of study, and phase of the research process that QDAS were used to support). The study found that the number of articles reporting QDAS is increasing each year, and that the majority of studies using ATLAS.ti™ and NVivo™ were published in health sciences journals by authors from the United Kingdom, United States, Netherlands, Canada, and Australia. Researchers used QDAS to support a variety of research designs and most commonly used the programs to support analyses of data gathered through interviews, focus groups, documents, field notes, and open-ended survey questions. Although QDAS can support multiple phases of the research process, the study found the vast majority of researchers are using it for data management and analysis, with fewer using it for data collection/creation or to visually display their methods and findings. This article concludes with some discussion of the extent to which QDAS users appear to have leveraged the potential of these programs to support new approaches to research.
The prevalence of mild cognitive impairment (MCI) and mobility limitations is high among older adults. The aim of this study was to investigate the association between MCI status and both performance-based and self-report measures of mobility in community-dwelling older adults.
An analysis was conducted on baseline data from the Boston Rehabilitative Impairment Study in the Elderly study, a cohort study of 430 primary care patients aged 65 or older. Neuropsychological tests identified participants with MCI and further subclassified those with impairment in memory domains (aMCI), nonmemory domains (naMCI), and multiple domains (mdMCI). Linear regression models were used to assess the association between MCI status and mobility performance in the Habitual Gait Speed, Figure of 8 Walk, Short Physical Performance Battery, and self-reported Late Life Function and Disability Instrument's Basic Lower Extremity and Advanced Lower Extremity function scales.
Participants had a mean age of 76.6 years, and 42% were characterized with MCI. Participants with MCI performed significantly worse than participants without MCI (No-MCI) on all performance and self-report measures (p < .01). All MCI subtypes performed significantly worse than No-MCI on all mobility measures (p < .05) except for aMCI versus No-MCI on the Figure of 8 Walk (p = .054) and Basic Lower Extremity (p = .11). Moreover, compared with aMCI, mdMCI manifested worse performance on the Figure of 8 Walk and Short Physical Performance Battery, and naMCI manifested worse performance on Short Physical Performance Battery and Basic Lower Extremity.
Among older community-dwelling primary care patients, performance on a broad range of mobility measures was worse among those with MCI, appearing poorest among those with nonmemory MCI.
Drivers with dementia, like most other drivers, are reluctant to relinquish their driving privileges, making discussions about driving cessation difficult and of great concern to family. To better understand how driving decisions are made 13 focus groups were held with 65 participants including current drivers with dementia, their spouses, and spouses of former drivers with dementia. Results documented the use of compensation strategies by drivers with dementia and their families to maintain safe driving behaviors given declining skills, identified a lack of planning for driving cessation even in light of expectations of cessation, confirmed a desire that driving decisions be a responsibility shared between families and professionals, and showed that diagnostic delays hamper families in making long-term plans. Given the desires and needs of drivers and their spouses, a shared, consistent, and unified approach to driving decisions between professionals and families is needed.
Cognitive impairment manifests in changed out-of-home mobility. Until recently, the assessment of outdoor mobility relied on the reports of family care-givers and institutional staff and used observational approaches, activity monitoring or behavioural checklists. This article presents data for 41 mildly demented, mildly cognitively impaired and healthy men and women aged 64–90 years in Tel-Aviv (Israel) who took part in an interdisciplinary project, SenTra, that is studying the outdoor activities of elderly people using advanced tracking technologies and that involves researchers from geography, social work, gerontology, psychology and medicine. The participants were tracked for 28 consecutive days using a location kit that combined a global positioning system (GPS) with radio frequency identification. The high-resolution spatial and temporal data enabled detailed analysis of the differences in the timing and distance of the participants' daily outdoor mobility patterns. It was found that the spatial range of the mobility of elderly people with cognitive impairment is severely restricted, with most out-of-home time spent in close proximity to their residences. We conclude that GPS is an advanced research tool able to understand out-of-home behaviour better than was possible with previous methods.
This paper reports the results of a field experiment where a Near Field Communication (NFC) enabled mobile phone was used as a user interface element so as to enable home-dwelling elderly people to choose their meals to be delivered by means of a home care service. The primary research focus was on examining the suitability of a touch-based user interface in the everyday life activities of elderly users. The eight-week experiment took place in the autumn of 2006. The results show that the touch-based user interface was easy to learn and adopt and the users were able to successfully use it regardless of their physical or cognitive weaknesses.
The article describes an approach of systematic, rule guided qualitative text analysis, which tries to preserve some methodological strengths of quantitative content analysis and widen them to a concept of qualitative procedure. First the development of content analysis is delineated and the basic principles are explained (units of analysis, step models, working with categories, validity and reliability). Then the central procedures of qualitative content analysis, inductive development of categories and deductive application of categories, are worked out. The possibilities of computer programs in supporting those qualitative steps of analysis are shown and the possibilities and limits of the approach are discussed. URN: urn:nbn:de:0114-fqs0002204
Studies have found that adults with possible mild cognitive impairment (MCI) exhibit decrements in everyday functioning (e.g., Wadley, V. G., Crowe, M., Marsiske, M., Cook, S. E., Unverzagt, F. W., Rosenberg, A. L., et al. (2007). Changes in everyday function among individuals with psychometrically defined mild cognitive impairment. Journal of the American Geriatrics Society, 55, 1192-1198). However, it is not known whether driving mobility and life space mobility are reduced in such individuals. The current study examined 5-year trajectories of mobility change in older adults (N = 2,355) with psychometrically defined MCI from the Advanced Cognitive Training for Independent and Vital Elderly trial. Mixed effect models evaluated group differences for the following mobility outcomes: driving space, life space, driving frequency, and driving difficulty. Relative to cognitively normal participants, participants with possible MCI showed reduced baseline mobility for all outcomes as well as faster rates of decline for driving frequency and difficulty. These results suggest that mobility declines could be features of MCI, and changes in mobility may be particularly important for researchers and clinicians to monitor in this population.
The objective is to lay out the rationale for multiple resource theory and the particular 4-D multiple resource model, as well as to show how the model is useful both as a design tool and as a means of predicting multitask workload overload.
I describe the discoveries and developments regarding multiple resource theory that have emerged over the past 50 years that contribute to performance and workload prediction.
The article presents a history of the multiple resource concept, a computational version of the multiple resource model applied to multitask driving simulation data, and the relation of multiple resources to workload.
Research revealed the importance of the four dimensions in accounting for task interference and the association of resources with brain structure. Multiple resource models yielded high correlations between model predictions and data. Lower correlations also identified the existence of additional resources.
The model was shown to be partially relevant to the concept of mental workload, with greatest relevance to performance breakdowns related to dual-task overload. Future challenges are identified.
The most important application of the multiple resource model is to recommend design changes when conditions of multitask resource overload exist.
Brief measures that accurately discriminate normal cognitive aging from very mild dementia are lacking. Cognitive tests often are insensitive to very mild dementia. Informant-based measures may be more sensitive in detecting early dementia.
To identify informant-reported clinical variables that differentiate cognitively normal individuals from those with very mild dementia.
A 55-item battery of informant queries regarding an individual's cognitive status was derived from a semistructured interview and a consensus panel of dementia experts. The battery was evaluated with informants for 189 consecutive participants of a longitudinal study of memory and aging and compared with an independently obtained Clinical Dementia Rating (CDR) score for the participant. Multiple regression and receiver operator characteristic curves assessed subsets of the items to discriminate between CDR 0 (no dementia) and CDR 0.5 (very mild dementia).
The final version (AD8) querying memory, orientation, judgment, and function was administered to an additional sample of 112 CDR 0 and 68 CDR 0.5 participants. Using a cut-off of two items endorsed, the area under the curve was 0.834, suggesting good to excellent discrimination, sensitivity was 74%, and specificity was 86% (prevalence of 0.38 for very mild dementia). Inclusion of 56 additional individuals with mild to severe dementia (increasing dementia prevalence to 0.53) increased sensitivity to 85%.
The AD8 is a brief, sensitive measure that reliably differentiates between nondemented and demented individuals. Use of the AD8 in conjunction with a brief assessment of the participant could improve diagnostic accuracy in general practice.
Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care.
To test the ability of patients to rate their own cognitive ability using the AD8 compared with informant and clinician ratings of cognitive status.
The AD8 was administered to 325 consecutive participant-informant dyads enrolled in a longitudinal study at Washington University School of Medicine between April 4, 2005, and December 15, 2005. The number of AD8 items endorsed by the participant was compared with informant answers and an independently derived Clinical Dementia Rating.
Strength of association was measured with Spearman (rho) and intraclass correlation coefficients. Receiver operator characteristic curves assessed the discriminative properties of the AD8.
The mean age of participants and informants was 72.8 years (range, 43-104 years) and 66.4 years (range, 24-101 years), respectively. The Clinical Dementia Rating was correlated with both informant (rho = 0.75, P<.001) and participant (rho = 0.34, P<.001) AD8 scores. Participants' AD8 scores had adequate agreement with informants' AD8 scores (intraclass correlation coefficient, 0.53; 95% confidence interval, 0.41-0.62) and correlated with subjective complaints of memory problems (rho = 0.47, P<.001) but not with estimates of symptom duration. The area under the receiver operator characteristic curve for the informant AD8 was 0.89 (95% confidence interval, 0.86-0.93); for the participant AD8, it was 0.78 (95% confidence interval, 0.68-0.78).
The AD8 is a brief measure that, when completed by an informant, differentiates nondemented from demented individuals. We now demonstrate that a self-completed AD8 also differentiates nondemented from demented individuals, although the utility was better in mildly impaired individuals compared with more demented individuals. In the absence of a reliable informant, the AD8 may be asked of the participant to gain an understanding of their perception of cognitive status.
As vehicles become increasingly automated, drivers may allocate more of their attention to various non-driving tasks or experience suboptimal workload levels while monitoring the environment and/or automation. However, until fully automated vehicles are available in the market, drivers are still expected to allocate some level of attention to the driving environment and take over the driving task if necessary. Attentive user interfaces (AUIs) are interfaces that adapt based on the operator’s state and task/environmental demands to manage and direct the operator’s attention. In this chapter, we examine how AUIs can be used to modulate drivers’ attention in automated vehicles. We begin by discussing the frameworks that have been used to define AUIs, followed by the relationship between workload, arousal, and attention. We then review the research on AUIs in the driving context and discuss opportunities for further research.
The human body (especially the hand) can sense a wide variety of haptic (tactile and kinaesthetic) features e.g. edges, textures, contours and force feedback. Consequently, it is possible to convey a wide range of information of importance to a user of an automated vehicle without using the traditional and limiting visual or auditory systems. This chapter introduces the cutaneous (skin) senses and argues for their importance in the automotive design field. Different haptic-based technologies are described, pertaining to physical controls, touchscreens and gesture interfaces, and the numerous research studies investigating the various fundamental Human Factors/HCI issues such as discrimination, task compatibility, individual sensitivity, subjective preference etc. are presented. In this respect, the potential for the use of “in-the-air” haptic feedback via ultrasound is introduced based on recent and ongoing research. The chapter concludes by discussing several additional benefits associated with haptic-augmented contactless interaction (hygiene, trust etc.) and highlighting the future potential of ultrasound technology in the context of automated vehicles.
Older adults face outdoor mobility challenges due to age-related changes and problems linked to the transportation system. The advent of fully-autonomous vehicles (AVs) is expected to benefit older adults by facilitating their outdoor mobility. However, research exploring the implications of AVs from older adults' perspective is limited. This study explores older adults' perception of the opportunities and concerns regarding AVs, in relation to their outdoor mobility needs and challenges. Using a qualitative design, we explore these issues based on in-depth interviews conducted with participants (≥65 years) in Greater Manchester, UK. The results revealed that older adults perceive opportunities in AVs enhancing their physical activity, promoting social interaction and offering stress-free door-to-door mobility. They, however, expressed concerns including safety, cost, possible erosion of road etiquettes and a desirable driving culture as a result of automation, a lack of social interaction with a human driver, and possible difficulties in using AVs. The participants' most preferred use options (i.e., public transport and shared autonomous services) are grounded in their need for social interaction, physical activity, safe and less stressful, and affordable travel. The implications of these findings for policy at the intersection of mobility and healthy ageing in future cities are discussed.
Users of Level 4–5 connected autonomous vehicles (CAVs) should not need to intervene with the dynamic driving task or monitor the driving environment, as the system will handle all driving functions. CAV human-machine interface (HMI) dashboards for such CAVs should therefore offer features to support user situation awareness (SA) and provide additional functionality that would not be practical within non-autonomous vehicles. Though, the exact features and functions, as well as their usability, might differ depending on factors such as user needs and context of use. The current paper presents findings from a simulator trial conducted to test the usability of a prototype CAV HMI designed for older adults and/or individuals with sensory and/or physical impairments: populations that will benefit enormously from the mobility afforded by CAVs. The HMI was developed to suit needs and requirements of this demographic based upon an extensive review of HMI and HCI principles focused on accessibility, usability and functionality [1, 2], as well as studies with target users. Thirty-one 50-88-year-olds (M 67.52, three 50–59) participated in the study. They experienced four seven-minute simulated journeys, involving inner and outer urban settings with mixed speed-limits and were encouraged to explore the HMI during journeys and interact with features, including a real-time map display, vehicle status, emergency stop, and arrival time. Measures were taken pre-, during- and post- journeys. Key was the System Usability Scale [3] and measures of SA, task load, and trust in computers and automation. As predicted, SA decreased with journey experience and although cognitive load did not, there were consistent negative correlations. System usability was also related to trust in technology but not trust in automation or attitudes towards computers. Overall, the findings are important for those designing, developing and testing CAV HMIs for older adults and individuals with sensory and/or physical impairments.
The 1999 Handbook of Assessment in Clinical Gerontology published by John Wiley and Sons had significant acclaim and sales success. I am proposing to update this by printing a larger, more comprehensive 2nd edition. The field of Clinical Gerontology has undergone enormous changes and expansions since the time of the 1999 book. Assessments for areas such as personality disorders, financial fraud and abuse, and household and neighborhood safety have exploded during the last decade. Updates on chapters previously written such as late life depression, dementia and delirium are needed because the field has undergone vast changes, with new scales and further validation studies available. The timing is right to present a state of the art, authoritative source for assessment across multiple domains of clinical gerontology. State of the art updates on assessing the effectiveness of the interdisciplinary team and examining how to include the wishes of the person with dementia into care planning are timely and topical subjects which would be included in this edition of the book. Similar to the first edition, I propose that each chapter should follow a structure that will make this the definitive source for clinicians. Specifically, there will be at least the following 5 elements in each chapter (1) an empirical review of assessment instruments, (2) the inclusion of 2-4 assessment scales in their totality, (3) a review of how these instruments are used in diverse populations, and (4) illustration of assessments through case studies, and (5) how to utilize re-assessment (ongoing assessment) in treatment and/or treatment planning. I propose a 28 chapter Handbook that will expand upon the first edition by updating relevant chapters and adding a significant number of new chapters. The most comprehensive source of up-to-date data on gerontological assessment, with review articles covering: psychopathology, behavioral disorders, changes in cognition, and changes in everyday functioning, *Consolidates broadly distributed literature into single source, saving researchers and clinicians time in obtaining and translating information and improving the level of further research and care they can provide Chapters directly address the range of conditions and disorders most common for this patient population - i.e. driving ability, mental competency, sleep, nutrition, sexual functioning, demntias, elder abuse, depression, anxiety disorders, etc *Fully informs readers regarding conditions most commonly encountered in real world treatment of an elderly patient population Each chapter cites case studies to illustrate assessment techniques *Exposes reader to real-world application of each assessment discussed Offering outstanding scholarship, each chapter is written by an expert in the topic area *Provides more fully vetted expert knowledge than any existing work.
The field of automotive user interfaces has developed rapidly over the last several years. To date, the field has primarily focused on creating user interfaces that promote safe driving, including when the driver is engaged in a secondary task in addition to operating the vehicle. However, researchers now need to prepare for a major change in the automotive domain: the automated driving revolution. The authors argue for a new research agenda that focuses on four challenges for automotive user interfaces: assuring safety in the age of automation, transforming vehicles into places for productivity and play, taking advantage of new mobility options made possible by automated vehicles, while throughout all this preserving user privacy and data security. This article is part of a special issue on smart vehicle spaces.
This article reviewed both studies on general smart car technologies and human–computer interaction (HCI)/human–vehicle interaction studies that were published in journals and conferences so that the current status of research can be identified and future research directions can be suggested. Furthermore, previous studies on elderly drivers were reviewed, as these drivers could be the most vulnerable social group in terms of new technology acceptance. A total of 257 articles for HCI research and 45 articles for elderly drivers were selected and reviewed from 11,267 collected articles (2010–2014). According to the results, most articles were mainly related to safety and adaptive features (e.g., driver’s state recognition, vehicle surrounding monitoring, driver action-suggestion), and infotainment research in terms of HCI (e.g., information technology devices–vehicle interaction, vehicle–vehicle interaction) was relatively insufficient despite its high research demand. According to the results of the literature review and technological trends analysis based on previous technical road maps, from HCI/human factors engineering (HFE) perspectives, research related to “Assistance systems,” “Physiological & mental state recognition,” “Position sensor technology,” “Behavior recognition,” and “Infotainment” was suggested to HCI/HFE researchers for further research. In particular, HCI/HFE researchers need to focus on research on acceptable levels of automation, observing new driving behaviors, investigation of driver characteristics to develop personalized services, and new technology acceptance to develop and improve smart cars in the future.
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This article describes a survey undertaken to investigate whether industrial data processing professionals would be able to recognize serious interface problems in simple but realistic dialogues. Seventy-seven designers and programmers from industry and academia participated. Fifty-one were from industry, 10 were teachers or students from universities or high schools, and 16 had occupations that were not specified. Many of them were designers and programmers of administrative systems - the people who design, write, and maintain our daily programs. The survey and a number of conclusions from it are first presented. The exercise used in the survey, a dialogue that the participants were asked to evaluate is then given. The authors' annotated solution and a suggestion for an improved design are provided.
The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.
The population is becoming increasingly aged, and concomitantly, the prevalence of dementia is steadily rising. Persons aged 65 years and over are likely to continue driving for many years and often well into the dementia process.
Ontario Ministry of Transportation driving data, census data, and dementia prevalence data were combined to determine the number of persons with potential dementia who are driving, both now and in about 25 years' time.
Actual and projected Ontario figures show that the number of senior drivers will increase markedly from just under 500,000 in 1986 to nearly 2,500,000 in 2028. Similarly, the number of drivers with dementia is also increasing. Although not all drivers with dementia are necessarily dangerous, most are estimated to continue driving well into the disease process. By combining the above-mentioned data sets, a best estimate of the number of drivers with dementia in Ontario was derived. It is estimated that this group has grown from just under 15,000 in 1986 to about 34,000 in 2000 and will number nearly 100,000 in 2028.
Increasingly, the responsibility for identifying drivers with dementia has fallen on the health care system, a role for which it was never designed nor equipped to handle. The risks associated with the dramatically increasing number of drivers with dementia demand a psychometrically sensitive and efficient screening procedure.
The purpose of the present study was to examine the types of impairments in everyday function that accompany mild cognitive impairment (MCI). Data for this study was collected from 434 individuals consecutively evaluated at a university-based Alzheimer's Center. A total of 96 participants were diagnosed with MCI, 105 were cognitively normal, and 233 had dementia. Informant ratings of participants' abilities were obtained across different functional domains reflecting everyday abilities related to memory, language, visual spatial abilities, planning, organization, and divided attention. As expected, the demented group was significantly more impaired than the healthy control and MCI groups across all of the functional domains. The MCI group also showed significantly more functional impairment relative to healthy controls in all of the functional domains. Examination of the effect sizes as a measure of the magnitude of functional impairment in the MCI groups relative to controls showed that the greatest degree of impairment occurred within the Everyday Memory domain. The current findings suggest that individuals with MCI demonstrate deficits in a wide range of everyday functions but that the magnitude of these changes is greatest for those functional abilities that rely heavily on memory.
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