<p>Adults aged 65 years and older have become the fastest-growing age group worldwide and are known to face perceptual, cognitive, and physical challenges in later stages of life. Automation may help to support these various age-related declines. However, many current automated systems often suffer from design limitations and occasionally require human intervention. To date, there is little guidance on how to design human-machine interfaces (HMIs) to help a wide range of users, especially older adults, transition to manual control. Multimodal interfaces, which present information in the visual, auditory, and/or tactile sensory channels, may be one viable option to communicate roles in human-automation systems, but insufficient empirical evidence is available for this approach. Also, the aging process is not homogenous across individuals, and physical and cognitive factors may better indicate one’s aging trajectory. Yet, the benefits that such individual differences have on task performance in human-automation systems are not well understood. Thus, the purpose of this dissertation work was to examine the effects of 1) multimodal interfaces and 2) one particular non-chronological age factor, engagement in physical exercise, on transitioning from automated to manual control dynamic automated environments. Automated driving was used as the testbed. The work was completed in three phases. </p><p><br></p> <p>The vehicle takeover process involves 1) the perception of takeover requests (TORs), 2) action selection from possible maneuvers that can be performed in response to the TOR, and 3) the execution of selected actions. The first phase focused on differences in the detection of multimodal TORs between younger and older drivers during the initial phase of the vehicle takeover process. Participants were asked to notice and respond to uni-, bi- and trimodal combinations of visual, auditory, and tactile TORs. Dependent measures were brake response time and maximum brake force. Overall, bi- and trimodal warnings were associated with faster responses for both age groups across driving conditions, but was more pronounced for older adults. Also, engaging in physical exercise was found to be correlated with smaller maximum brake force. </p><p><br></p> <p>The second phase aimed to quantify the effects of age and physical exercise on takeover task performance as a function of modality type and lead time (i.e., the amount of time given to make decisions about which action to employ). However, due to COVID-19 restrictions, the study could not be completed, thus only pilot data was collected. Dependent measures included decision making time and maximum resulting jerk. Preliminary results indicated that older adults had a higher maximum resulting jerk compared to younger adults. However, the differences in decision-making time and maximum resulting jerk were narrower for the exercise group (compared to the non-exercise group) between the two age groups. </p><p><br></p> <p>Given COVID-19 restrictions, the objective of phase two shifted to focus on other (non-age-related) gaps in the multimodal literature. Specifically, the new phase examined the effects of signal direction, lead time, and modality on takeover performance. Dependent measures included pre-takeover metrics, e.g., takeover and information processing time, as well as a host of post-takeover variables, i.e., maximum resulting acceleration. Takeover requests with a tactile component were associated with the faster takeover and information processing times. The shorter lead time was correlated with poorer takeover quality.</p><p><br></p> <p>The third, and final, phase used knowledge from phases one and two to investigate the effectiveness of meaningful tactile signal patterns to improve takeover performance. Structured and graded tactile signal patterns were embedded into the vehicle’s seat pan and back. Dependent measures were response and information processing times, and maximum resulting acceleration. Overall, in only instructional signal group, meaningful tactile patterns (either in the seat back or seat pan) had worse takeover performance in terms of response time and maximum resulting acceleration compared to signals without patterns. Additionally, tactile information presented in the seat back was perceived as most useful and satisfying.</p><p><br></p> <p>Findings from this research can inform the development of next-generation HMIs that account for differences in various demographic factors, as well as advance our knowledge of the aging process. In addition, this work may contribute to improved safety across many complex domains that contain different types and forms of automation, such as aviation, manufacturing, and healthcare.</p>

Abstract There are multiple factors simultaneously affecting speech perception in elderly people. These factors include hearing acuity, aging of the auditory system, and changes in both perception and cognitive processes, all of which can interfere with speech comprehension, especially in cognitively demanding situations. The aim of this study is to clarify which factors influence the use of an automatic phone service system designed for elderly (N = 36) people. More specifically, the aim is to investigate whether it is the factors connected to the system itself or the factors connected to the elderly users and their actions with the system that are the most crucial for using the system successfully. Both quantitative and qualitative methods are used in the study. There were four people who performed as speakers in the system. Analysis of the prosodic features of their speech was performed using acoustic analysis software. The variables connected to the elderly participants (n = 30) were investigated using interviews, pure-tone and speech audiometric tests, the Mini-Mental State Examination test (MMSE), and the Token Test for speech comprehension. Statistical analyses were used to explore whether there was a statistical connection between the acoustic measurements or the variables connected to participants themselves and their performance in usability test situation. In addition, the elderly participants’ actions in the test situation were observed using a material-based, qualitative video-analysis. The individuals who performed as speakers in the system were observed to use features of elderspeak in their speech. However, these speaker characteristics had little effect on the participants’ performance in the tasks. It was the voice-menu that contained the most semantically complex text structure that proved to be the most difficult for participants. Both low scores in the Token test and poor word recognition were connected to poor performance in the tasks. It was found based on the qualitative analysis that in addition to speech comprehension, there were other cognitive processes that were important for completing the tasks successfully, i.e. remembering the instructions given (memory), and the ability to direct, divide and maintain attention during the tasks. Poor performance in the tasks and in the Token Test, as well as problems in executive functions observed in the test situation, were found to be factors predicting dropping out of the next phase of the study the following year. Qualitative analysis of language use in cognitively demanding situations can be used in evaluation of high-level language performance. It may be useful for detecting mild changes in language skills that can be symptomatic of early stages of memory disorders. The results of this study can also be utilized when designing voice-based interfaces. In addition, it is important to consider both advantages and disadvantages of using elderspeak in the fields of nursing and speech therapy. / Tiivistelmä Ikääntyvien ihmisten puheen vastaanotossa vaikuttavat samanaikaisesti monet tekijät: kuulokyky, auditiivisen järjestelmän ikääntymismuutokset sekä havaintotoimintojen ja kognitiivisten toimintojen muutokset. Nämä voivat vaikeuttaa puheen ymmärtämistä erityisesti kognitiivisesti vaativassa tilanteessa. Tämän tutkimuksen tavoitteena on selvittää ikääntyneille osallistujille (N = 36) suunnitellun automaattisen puhelinpalvelujärjestelmän käyttöön liittyviä tekijöitä. Tavoitteena on selvittää se, missä määrin toisaalta kokeiltuun järjestelmään liittyvät tekijät ja toisaalta käyttäjien ominaisuudet sekä heidän toimintansa tutkimustilanteessa olivat yhteydessä järjestelmän menestykselliseen käyttöön. Tutkimuksessa käytetään kvantitatiivisia ja kvalitatiivisia menetelmiä. Järjestelmässä kokeiltiin neljän eri puhujan äänillä nauhoitettuja toimintaohjeita. Heidän puheensa prosodisia piirteitä analysoitiin äänen ja puheen analyysiohjelmilla. Ikääntyneisiin osallistujiin (n = 30) liittyviä muuttujia tutkittiin haastattelulla, kuulon tutkimuksilla (äänesaudiometria ja puheaudiometria), kognitiivisella seulontatestillä (Mini-mental state examination = MMSE) ja puheen ymmärtämistä mittaavalla Token-testillä. Mittaustulosten ja muuttujien yhteyttä tehtävistä suoriutumiseen tarkasteltiin tilastollisesti. Osallistujien toimintaa havainnoitiin järjestelmän käyttötilanteessa aineistolähtöisellä laadullisella videoanalyysillä. Järjestelmän puhujilla havaittiin ikääntyneille suunnatun puheen piirteitä. Tehtävistä suoriutuminen oli kuitenkin hyvin samanlaista puhujasta riippumatta. Semanttisesti monimutkaisin tekstivalikko oli osallistujille vaikein äänite. Matala Token-testin pistemäärä ja heikko puheen tunnistuskyky liittyivät heikkoon tehtävistä suoriutumiseen. Laadullisen analyysin perusteella puheen ymmärtämisen ohella keskeisiä kognitiivisia prosesseja tehtävissä menestymisen kannalta olivat seuraavat: ohjeiden muistaminen, huomion suuntaaminen, jakaminen ja ylläpito. Heikko suoriutuminen tehtävissä ja Token-testissä sekä tutkimustilanteessa havaitut toiminnan ohjauksen ongelmat ennustivat toisesta tutkimusvaiheesta poisjääntiä seuraavana vuonna. Kognitiivisesti vaativista kielen käyttötilanteista tehtävillä laadullisilla analyyseilla voidaan arvioida monimutkaisia kielellis-kognitiivisia toimintoja ja löytää mahdollisesti alkaviin muistisairauksiin liittyviä lieviä kielellisiä muutoksia. Tuloksia voidaan hyödyntää ääneen perustuvien käyttöliittymien suunnittelussa. Ikääntyneille suunnatun puheen etuja ja haittoja on tärkeää pohtia myös hoitotyön ja puheterapian näkökulmasta.

<div>Older adults aged 60+ represent the fastest growing segment of the US population, yet they are rarely seen as users of technology. Members of this age cohort often struggle with the material and conceptual requirements of computing—such as clicking small targets or remembering usernames and passwords for account logins—leading them to adopt technologies like smartphones and social media at much lower rates than their younger counterparts. Digital devices and interfaces are not typically designed with older adult users in mind, even though all users are always aging, and the “silver economy” represents a powerful, and often untapped, market for technological innovations. The little existing research in this area often conflates age with disability, framing elders according to a deficit model. While it is certainly important to consider the impacts that aging bodies have on technology use, they are not the sole factor shaping usage for older age cohorts. Moreover, if we reduce elder users to their “impairments,” we risk stereotyping them in ways that curtail design possibilities, as well as these users’ possibilities for full participation in digital life. For this reason, studies of technology users aged 60+ and their communities are necessary to shed light on the multifaceted needs of older age cohorts, and the interventions into technology design, documentation, and education that can help them reach their digital goals. </div><div><br></div><div>To build an understanding of the unique technology use of a group of the oldest Americans (aged 75+), as well as to assess their needs and desires for digital engagement, I conducted interviews and observations with computer users in a senior living community. Data collection revealed a great diversity of computing purposes and activities, ranging from social functions such as email and messaging, to managing finance and medicine, to art and design applications, and beyond. Moreover, participants’ accounts of how and where they developed their computing skills shed light on their motivations for engaging with technology, as well as their fears of technology’s intrusiveness. Analysis of participants’ performance on a series of digital tasks yielded insights into physical and cognitive factors, as well as a clear divide in forms of knowledge and mental models that older adults draw upon when attempting to engage with technology. To conclude, I provide recommendations for technology design and education, as well as future research to account for age as a factor mediating user experience.</div>