Journal articles: 'Amazon Alexa'

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Barceló-Armada, Rubén, Ismael Castell-Uroz, and Pere Barlet-Ros. "Amazon Alexa traffic traces." Computer Networks 205 (March 2022): 108782. http://dx.doi.org/10.1016/j.comnet.2022.108782.

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Lopatovska, Irene, Katrina Rink, Ian Knight, Kieran Raines, Kevin Cosenza, Harriet Williams, Perachya Sorsche, David Hirsch, Qi Li, and Adrianna Martinez. "Talk to me: Exploring user interactions with the Amazon Alexa." Journal of Librarianship and Information Science 51, no. 4 (March 7, 2018): 984–97. http://dx.doi.org/10.1177/0961000618759414.

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Amazon Alexa is a voice-controlled application that is rapidly gaining popularity. We examined user interactions with this technology, and focused on the types of tasks requested of Alexa, the variables that affect user behaviors with Alexa, and Alexa’s alternatives. The data about Alexa usage were collected from 19 participants via the online questionnaire and diary methods over the course of several days. The results indicate that across all age groups, Alexa was primarily used for checking weather forecasts, playing music, and controlling other devices. Several participants reported using Apple Siri and Google Now applications in addition to Alexa for similar purposes except for controlling other devices. Alexa uses over the weekends were more frequent than on weekdays, but its overall usage tended to decrease over time. The users reported being satisfied with Alexa even when it did not produce sought information, suggesting that the interaction experience is more important to the users than the interaction output. More work is required to understand whether users treat Alexa and similar voice-controlled applications as primarily a traditional information retrieval system, a casual leisure system, a control interface for smart home devices, or, simply, a new toy.

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Chung, Hyunji, Jungheum Park, and Sangjin Lee. "Digital forensic approaches for Amazon Alexa ecosystem." Digital Investigation 22 (August 2017): S15—S25. http://dx.doi.org/10.1016/j.diin.2017.06.010.

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West, Emily. "Amazon: Surveillance as a Service." Surveillance & Society 17, no. 1/2 (March 31, 2019): 27–33. http://dx.doi.org/10.24908/ss.v17i1/2.13008.

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This essay argues that Amazon, the leading e-commerce platform in many parts of the world, uses surveillance not just as a key tool in the platform logic of its growing constellation of businesses but also increasingly as a service to its consumers. In contrast to prevailing assumptions that platforms will obscure the surveillant aspects of their businesses and that users will resist the intrusive nature of corporate surveillance, Amazon’s business practices point to the rapid normalization, and even embrace, of surveillant logics by consumers. Given the importance of consumer data to its operations, Amazon increasingly designs services whose purpose is, at least in part, to collect more data about consumers. The zenith of Amazon’s surveillance capabilities of its customers is no doubt its family of Echo devices enabled by the artificial intelligence interactive-voice service Alexa, which connects to the cloud run by Amazon, itself, through Amazon Web Services. Alexa is similar to competing digital voice assistants like Apple’s Siri and Google’s Assistant, but with more cultural visibility, worldwide market penetration, and greater integration with a host of Internet-of-Things devices produced by a variety of manufacturers. Amazon seeks to make Alexa an indispensable service to consumers, one that sweetens the granular forms of surveillance in more private spaces and situations that it now has the capability to gather, relative to the company’s more established forms of surveillance. While a typical association with surveillance might be the alienation and disempowerment of social control, I suggest that Amazon’s practices of consumer surveillance cultivate a sense of intimacy, borne of being seen between consumer and brand. In other words, I advocate for recognizing the subjectification of contemporary practices of platform surveillance, in addition to its structural elements.

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Major, David, Danny Yuxing Huang, Marshini Chetty, and Nick Feamster. "Alexa, Who Am I Speaking To?: Understanding Users’ Ability to Identify Third-Party Apps on Amazon Alexa." ACM Transactions on Internet Technology 22, no. 1 (February 28, 2022): 1–22. http://dx.doi.org/10.1145/3446389.

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Many Internet of Things devices have voice user interfaces. One of the most popular voice user interfaces is Amazon’s Alexa, which supports more than 50,000 third-party applications (“skills”). We study how Alexa’s integration of these skills may confuse users. Our survey of 237 participants found that users do not understand that skills are often operated by third parties, that they often confuse third-party skills with native Alexa functions, and that they are unaware of the functions that the native Alexa system supports. Surprisingly, users who interact with Alexa more frequently are more likely to conclude that a third-party skill is a native Alexa function. The potential for misunderstanding creates new security and privacy risks: attackers can develop third-party skills that operate without users’ knowledge or masquerade as native Alexa functions. To mitigate this threat, we make design recommendations to help users better distinguish native functionality and third-party skills, including audio and visual indicators of native and third-party contexts, as well as a consistent design standard to help users learn what functions are and are not possible on Alexa.

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Phan, Thao. "Amazon Echo and the Aesthetics of Whiteness." Catalyst: Feminism, Theory, Technoscience 5, no. 1 (April 1, 2019): 1–38. http://dx.doi.org/10.28968/cftt.v5i1.29586.

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This article examines the figuration of the home automation device Amazon Echo and its digital assistant Alexa. While most readings of gender and digital assistants choose to foreground the figure of the housewife, I argue that Alexa is instead figured on domestic servants. I examine commercials, Amazon customer reviews, and reviews from tech commentators to make the case that the Echo is modeled on an idealized image of domestic service. It is my contention that this vision functions in various ways to reproduce a relation between device/user that mimics the relation between servant/master in nineteenth- and twentieth-century American homes. Significantly, however, the Echo departs from this historical parallel through its aesthetic coding as a native-speaking, educated, white woman. This aestheticization is problematic insofar as it decontextualizes and depoliticizes the historic reality of domestic service. Further, this figuration misrepresents the direction of power between user and devices in a way that makes contending with issues such as surveillance and digital labor increasingly difficult.

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Liverpool, Layal. "Audio attack blocks Amazon Alexa from hearing you." New Scientist 244, no. 3256 (November 2019): 12. http://dx.doi.org/10.1016/s0262-4079(19)32141-4.

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Wellsandt, Stefan, Mina Foosherian, and Klaus-Dieter Thoben. "Interacting with a Digital Twin using Amazon Alexa." Procedia Manufacturing 52 (2020): 4–8. http://dx.doi.org/10.1016/j.promfg.2020.11.002.

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Sorensen, Katelyn, and Jennifer Johnson Jorgensen. "“Hey Alexa, Let's Shop”." International Journal of E-Services and Mobile Applications 13, no. 1 (January 2021): 1–14. http://dx.doi.org/10.4018/ijesma.2021010101.

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Millennials quickly adapt to new technologies and have been found to use voice technology daily. This study follows the technology acceptance model (TAM) developed by Davis to explain the relationships between perceived ease of use (PEOU), perceived usefulness (PU), perceived enjoyment (PE), and perceived innovativeness (PI) to behavioral intention (BI) for Millennials. An online survey generated 204 usable responses through Amazon Mechanical Turk. Multiple regression analyses supported the relationship of PEOU to PU, PU, PE, PI to BI, and PEOU was not found to influence BI in this study. The findings of this study indicate that consumers are ready to purchase through voice-activated technologies, but the current platform needs to be adapted so that it is easier to use.

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Hooda, Ashish, Matthew Wallace, Kushal Jhunjhunwalla, Earlence Fernandes, and Kassem Fawaz. "SkillFence." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 1 (March 29, 2022): 1–26. http://dx.doi.org/10.1145/3517232.

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Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

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Chung, Arlene E., Ashley C. Griffin, Dasha Selezneva, and David Gotz. "Health and Fitness Apps for Hands-Free Voice-Activated Assistants: Content Analysis." JMIR mHealth and uHealth 6, no. 9 (September 24, 2018): e174. http://dx.doi.org/10.2196/mhealth.9705.

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Background Hands-free voice-activated assistants and their associated devices have recently gained popularity with the release of commercial products, including Amazon Alexa and Google Assistant. Voice-activated assistants have many potential use cases in healthcare including education, health tracking and monitoring, and assistance with locating health providers. However, little is known about the types of health and fitness apps available for voice-activated assistants as it is an emerging market. Objective This review aimed to examine the characteristics of health and fitness apps for commercially available, hands-free voice-activated assistants, including Amazon Alexa and Google Assistant. Methods Amazon Alexa Skills Store and Google Assistant app were searched to find voice-activated assistant apps designated by vendors as health and fitness apps. Information was extracted for each app including name, description, vendor, vendor rating, user reviews and ratings, cost, developer and security policies, and the ability to pair with a smartphone app and website and device. Using a codebook, two reviewers independently coded each app using the vendor’s descriptions and the app name into one or more health and fitness, intended age group, and target audience categories. A third reviewer adjudicated coding disagreements until consensus was reached. Descriptive statistics were used to summarize app characteristics. Results Overall, 309 apps were reviewed; health education apps (87) were the most commonly occurring, followed by fitness and training (72), nutrition (33), brain training and games (31), and health monitoring (25). Diet and calorie tracking apps were infrequent. Apps were mostly targeted towards adults and general audiences with few specifically geared towards patients, caregivers, or medical professionals. Most apps were free to enable or use and 18.1% (56/309) could be paired with a smartphone app and website and device; 30.7% (95/309) of vendors provided privacy policies; and 22.3% (69/309) provided terms of use. The majority (36/42, 85.7%) of Amazon Alexa apps were rated by the vendor as mature or guidance suggested, which were geared towards adults only. When there was a user rating available, apps had a wide range of ratings from 1 to 5 stars with a mean of 2.97. Google Assistant apps did not have user reviews available, whereas most of Amazon Alexa apps had at least 1-9 reviews available. Conclusions The emerging market of health and fitness apps for voice-activated assistants is still nascent and mainly focused on health education and fitness. Voice-activated assistant apps had a wide range of content areas but many published in the health and fitness categories did not actually have a clear health or fitness focus. This may, in part, be due to Amazon and Google policies, which place restrictions on the delivery of care or direct recording of health data. As in the mobile app market, the content and functionalities may evolve to meet growing demands for self-monitoring and disease management.

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Ochoa-Orihuel, Javier, Raúl Marticorena-Sánchez, and María Consuelo Sáiz-Manzanares. "Moodle LMS Integration with Amazon Alexa: A Practical Experience." Applied Sciences 10, no. 19 (September 29, 2020): 6859. http://dx.doi.org/10.3390/app10196859.

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The frequency of interaction between teachers and students through Learning Management Systems (LMSs) is continuously rising. However, recent studies highlight the challenges presented in current LMSs to meet the specific needs of the student, regarding usability and learnability. With the motivation to support the research of effectiveness when using a Voice User Interface (VUI) for education, this paper presents the work done (RQ1) to build the basic architecture for an Alexa skill for educational purposes, including its integration with Moodle, and (RQ2) to establish whether Moodle currently provides the necessary tools for voice-content creation for develop voice-first applications, aiming to provide new scientific insight to help researchers on future works of similar characteristics. As a result of this work, we provide guidelines for the architecture of an Alexa skill application integrated with Moodle through safe protocols, such as Alexa’s Account Linking Web Service, while our findings ratify the need for additional tooling within Moodle platform for voice-content creation in order to create an appealing voice experience, with the capabilities to process Moodle data structures and produce sensible sentences that can be understood by users when spoken by a voice device.

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Gautam, Neetima. "Opinion Mining of Amazon Data for Alexa Review Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3814–19. http://dx.doi.org/10.22214/ijraset.2021.37203.

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The fast expansion in piles of unstructured literary information joined by multiplication of devices to investigate them has opened up extraordinary freedoms and difficulties for text mining research. The programmed naming of information is hard in light of the fact that individuals regularly express feelings in complex manners that are here and there hard to fathom. The marking interaction includes tremendous measure of endeavours and mislabelled datasets typically lead to erroneous choices. In this paper, we plan a frame work for sentiment analysis with opinion mining for the instance of Amazon Alexa. Most accessible datasets are not named which presents a great deal of works for scientists as tolls text information pre-preparing task is concerned. Also, supposition datasets are frequently profoundly area touchy and difficult to make since assumptions are sentiments like feelings, mentalities and conclusions that are ordinarily overflowing with phrases, sound to word imitations, homophones, phonemes, similar sounding word usages and abbreviations. The proposed system is named feeling extremity that naturally readies a supposition dataset for preparing and testing to extricate impartial assessments of inn administrations from surveys to find a reasonable AI calculation for the grouping segment of the structure.

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Wiggen, Simon. "Alexa: Etwas Gutes für mich! Wie das Bistum Essen mit dem Amazon-Lautsprecher neue Zielgruppen erreichen will." Communicatio Socialis 53, no. 1 (2020): 109–14. http://dx.doi.org/10.5771/0010-3497-2020-1-109.

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Mit Beginn der Fastenzeit 2019 veröffentlichte das Bistum Essen seinen ersten eigenen Alexa-Skill, also eine Art App für den smarten Lautsprecher Alexa von Amazon. Dieser Skill ermöglicht es den Nutzer_innen, sich passend zur eigenen aktuellen Gemütslage Beiträge der Radiosendung „Kirche in 1Live“ vorspielen zu lassen. Mit dem Skill „Etwas Gutes für mich“ sollen die Nutzer_innen mit einem guten Gefühl den smarten Lautsprecher nutzen und sich danach hoffentlich etwas besser fühlen. Das erste Fazit fällt positiv aus. Auch, weil das Angebot Menschen anspricht, die keine Berührungspunkte mit der Seelsorge in Pfarreien und Gemeinden haben.

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Meza, Andrés, Gustavo López, Luis Quesada, and Luis A. Guerrero. "Architecture to Design Booking Appointment Applications for the Smart Personal Assistant Alexa." Proceedings 31, no. 1 (November 20, 2019): 17. http://dx.doi.org/10.3390/proceedings2019031017.

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The intelligent smart assistants are becoming more interactive and helpful for everyday tasks. The Amazon Echo has potential for advanced voice interactions and as a tool for conducting complex tasks. The potential of the Amazon Echo in the area of booking appointments is not being fully exploited by developers. A flexible architecture for developing appointment booking applications for the Amazon Echo was proposed. The architecture serves as guide for developers without experience working with Voice User Interfaces and saves development time by abstracting the complexity of voice interactions. A prototype skill was developed following the architecture principles and evaluated by a group of users. The skill successfully defines how an appointment booking skill should be.

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Declercq, Lindee, Keegan Dalal, Megan Piché, Nicholas Burton, and Michael Naraine. "Hey Alexa, Launch Twitch: Using Sport Sponsorship to Drive Business Development." Case Studies in Sport Management 10, no. 1 (January 1, 2021): 50–53. http://dx.doi.org/10.1123/cssm.2020-0025.

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In this case study, students will explore how sport sponsorship can be used to drive business development. They will follow the fictitious story of Amazon, developing a plan to expand its operations into the Middle East through the eSports platform Twitch. Twitch, a video game livestreaming site has contributed to the rise popularity of eSports. Thanks to its appeal to the youth demographic, it is revealed Twitch offers a unique platform that can give Amazon a competitive advantage. This aligns with the Middle East’s increasing interest in becoming a global sport leader. After further exploring the Middle East market, the potential value of this sponsorship will be determined. In addition, business-to-consumer strategies will be consulted to justify the plan put forward by Amazon. Learning objectives include understanding the role of new media and being able to understand the early phases of a sponsorship plan.

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Rößing, Sabine. "Sprachgestützte Such-Technologien: „Alexa, was sind die Symptome von Windpocken?“." kma - Klinik Management aktuell 24, no. 11 (November 2019): 46–47. http://dx.doi.org/10.1055/s-0039-3400586.

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Bilic, Leonardo, Markus Ebner, and Martin Ebner. "A Voice-Enabled Game Based Learning Application using Amazon's Echo with Alexa Voice Service: A Game Regarding Geographic Facts About Austria and Europe." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 03 (February 28, 2020): 226. http://dx.doi.org/10.3991/ijim.v14i03.12311.

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An educational, interactive Amazon Alexa Skill called “Österreich und Europa Spiel / Austria and Europe Game” was developed at Graz University of Technology for a German as well as English speaking audience. This Skills intent is to assist learning geographic facts about Austria as well as Europe by interaction via voice controls with the device. The main research question was if an educational, interactive speech assistant application could be made in a way such that both under-age and full age subjects would be able to use it, enjoy the Game Based Learning experience overall and be assisted learning about the Geography of Austria and Europe. The Amazon Alexa Skill was tested for the first time in a class with 16 students at lower secondary school level. Two further tests were done with a total of five adult participants. After the tests the participants opinion was determined via a questionnaire. The evaluation of the tests suggests that the game indeed gives an additional motivational factor in learning Geography.

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Taib, Abidah Mat. "Voice Integration on Smart Indoor Garden (VoISIG) Using Amazon Alexa." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.3 (June 25, 2020): 27–33. http://dx.doi.org/10.30534/ijatcse/2020/0591.32020.

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Emerling, Christopher R., Sohyun Yang, Richard A. Carter, Ling Zhang, and Tiffany Hunt. "Using Amazon Alexa as an Instructional Tool During Remote Teaching." TEACHING Exceptional Children 53, no. 2 (October 28, 2020): 164–67. http://dx.doi.org/10.1177/0040059920964719.

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Alagha, Emily Couvillon, and Rachel Renee Helbing. "Evaluating the quality of voice assistants’ responses to consumer health questions about vaccines: an exploratory comparison of Alexa, Google Assistant and Siri." BMJ Health & Care Informatics 26, no. 1 (November 2019): e100075. http://dx.doi.org/10.1136/bmjhci-2019-100075.

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ObjectiveTo assess the quality and accuracy of the voice assistants (VAs), Amazon Alexa, Siri and Google Assistant, in answering consumer health questions about vaccine safety and use.MethodsResponses of each VA to 54 questions related to vaccination were scored using a rubric designed to assess the accuracy of each answer provided through audio output and the quality of the source supporting each answer.ResultsOut of a total of 6 possible points, Siri averaged 5.16 points, Google Assistant averaged 5.10 points and Alexa averaged 0.98 points. Google Assistant and Siri understood voice queries accurately and provided users with links to authoritative sources about vaccination. Alexa understood fewer voice queries and did not draw answers from the same sources that were used by Google Assistant and Siri.ConclusionsThose involved in patient education should be aware of the high variability of results between VAs. Developers and health technology experts should also push for greater usability and transparency about information partnerships as the health information delivery capabilities of these devices expand in the future.

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Sanford, Jon. "Creating Inclusive Design Experiences Through Engaging Seniors With Disabilities in Student Hackathons." Innovation in Aging 4, Supplement_1 (December 1, 2020): 600. http://dx.doi.org/10.1093/geroni/igaa057.2021.

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Abstract Although voice-activated assistants (e.g., Amazon Alexa) have become smarter, faster, more personalized, and more ubiquitous, little is known about their potential to promote aging in place for people with disabilities. Partnering with Amazon’s Alexa team, a 2-month long design competition and hackathon was conducted to inspire college students to develop innovative voice-activated solutions to support successful aging with disabilities. This presentation will cover the specific inclusive experiences used to immerse student teams in the daily lives of the target population to ensure that design solutions responded to real needs of real people in real environments. These included: lectures on current research findings about the everyday needs and challenges of the target users as well as universal design approaches to solving those problems; a survey of individuals currently using voice-activated assistants to understand their benefits and potential uses; and providing target with Alexa-enabled devices and embedding them into the hackathon teams.

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Kawiswara, Rakha Paleva, and Farid Thalib. "Implementasi Algoritma Convolutional Neural Network Pada Algoritma K-Means Untuk Kategorisasi Data Teks." Jurnal Teknologi 7, no. 2 (May 28, 2020): 151–62. http://dx.doi.org/10.31479/jtek.v7i2.48.

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Internet merupakan keberlanjutan dari pesatnya perkembangan teknologi komputer. Dalam kenyataannya pengguna internet menghasilkan banyak data, khususnya data berupa teks seperti posting pada social media dan artikel-artikel. Untuk itu, diperlukan kategorisasi data untuk mengelompokkan data teks yang memiliki kategori tertentu. Bag of Words merupakan algoritma yang dapat merepresentasikan kalimat menjadi vektor. Namun Bag of Words seringkali menghasilkan dimensi data atau jumlah fitur yang sangat tinggi, sehingga memerlukan daya komputasi yang sangat tinggi. Pendekatan untuk mengatasi masalah tersebut adalah pembuatan model yang dapat merepresentasikan kalimat menjadi vektor. Model yang menggunakan algoritma Convolutional Neural Network (ConvNet) dapat dipakai untuk mempelajari kalimat dalam data teks untuk merepresentasikan kalimat dalam bentuk vektor. Pelatihan dan pengujian model menggunakan empat data teks yaitu SMS Spam, Komentar Bully, Amazon Alexa Reviews, dan Large Movie Reviews. Hasil vektor kalimat menggunakan ConvNet lebih efisien dalam waktu latih dan waktu uji dibanding dengan representasi menggunakan Bag of Words. Hasil pengujian vektor kalimat ConvNet dengan pengukuran Fowlkes-Mallow Index untuk data teks SMS Spam adalah 0.738, untuk data teks komentar Bully adalah 0.735, untuk data teks Amazon Alexa Reviews adalah 0.908 dan untuk data teks Large Movie Reviews adalah 0.680.

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Kudina, Olga. "Alexa Does Not Care. Should You?" Glimpse 20 (2019): 107–15. http://dx.doi.org/10.5840/glimpse2019207.

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This article explores the ethical dimension of digital voice assistants from the angle of postphenomenology and the technological mediation approach, whereby technology plays a mediating role in the human-world relations. Digital voice assistants, such as Amazon Echo’s Alexa or Google’s Home, increasingly form an integral part of everyday life for many people. Powered by Artificial Intelligence and based on voice interaction, voice assistants promise constant accompaniment by answering any questions people might have and even managing the physical space of their homes. However, while accompanying daily lives of people, voice assistants also seamlessly redefine the way people talk, interact and perceive each other. In view of their intentionalities, such as interaction by voice, command-based model of communication and development of attachment, digital voice assistant mediate the norms of interaction beyond their immediate use, the way people perceive themselves, those around and form consequent normative expectations. The article argues that understanding how technologies, such as digital voice assistants, mediate our moral landscape forms an essential part of media literacy in the digital age.

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Watters, Jacob D., April Hill, Melissa Weinrich, Cary Supalo, and Feng Jiang. "An Artificial Intelligence Tool for Accessible Science Education." Journal of Science Education for Students with Disabilities 24, no. 1 (September 26, 2021): 1–14. http://dx.doi.org/10.14448/jsesd.13.0010.

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One of the most important issues in accessible science education is creating a laboratory workspace accessible to blind students or students with visual impairments (VI). Although these students are often provided access to the science lectures, they are usually denied full participation in hands-on laboratory work. Current solutions to this problem focus on providing special accommodations such as asking sighted lab partners to complete the hands-on work. Although the accessibility of laboratory devices in modern science education has been improved in recent years, students with VI often remain passive learners. In this work, we developed a new artificial intelligence tool, the MSU Denver Virtual Lab Assistant (VLA), using Amazon Web Services (AWS), Amazon Alexa Skills Kit (ASK), Alexa smart speaker, and a microcontroller (Raspberry Pi). The VLA can be used as a virtual assistant in the lab in combination with other access technologies and devices. The VLA allows students with VI to perform the hands-on laboratory work by themselves simply using voice control. The VLA can be accessed through any smartphone or Amazon Echo device to assist general science lab procedures. The VLA is designed to be applicable to different science laboratory work. It is also compatible with other common accessible electronic devices such as the Talking LabQuest (TLQ). We believe that the VLA can promote the inclusion of learners with VI and be beneficial to general accessible science education work.

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Bogdan, Răzvan, Alin Tatu, Mihaela Marcella Crisan-Vida, Mircea Popa, and Lăcrămioara Stoicu-Tivadar. "A Practical Experience on the Amazon Alexa Integration in Smart Offices." Sensors 21, no. 3 (January 22, 2021): 734. http://dx.doi.org/10.3390/s21030734.

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Smart offices are dynamically evolving spaces meant to enhance employees’ efficiency, but also to create a healthy and proactive working environment. In a competitive business world, the challenge of providing a balance between the efficiency and wellbeing of employees may be supported with new technologies. This paper presents the work undertaken to build the architecture needed to integrate voice assistants into smart offices in order to support employees in their daily activities, like ambient control, attendance system and reporting, but also interacting with project management services used for planning, issue tracking, and reporting. Our research tries to understand what are the most accepted tasks to be performed with the help of voice assistants in a smart office environment, by analyzing the system based on task completion and sentiment analysis. For the experimental setup, different test cases were developed in order to interact with the office environment formed by specific devices, as well as with the project management tool tasks. The obtained results demonstrated that the interaction with the voice assistant is reasonable, especially for easy and moderate utterances.

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Fagundes Pase, André, Gisele Noll, Mariana Gomes da Fontoura, and Letícia Dallegrave. "Who Controls the Voice? The Journalistic Use and the Informational Domain in Vocal Transactors." Brazilian Journalism Research 16, no. 3 (December 29, 2020): 576–603. http://dx.doi.org/10.25200/bjr.v16n3.2021.1316.

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This article aims to understand the transformations caused by new informational ecosystems in contemporary journalism. This analysis is performed based on news accessed through personal digital assistants embedded in smart speakers. As a methodological procedure, it adopts a multiple case study, defining the vocal transactors of Google (Nest Home/Google Assistant) and Amazon (Echo/Alexa) as its object. Therefore, this paper notes that the inclusion of algorithmic routines and the extension of news content to intelligent voice interfaces requires adaptation for the personalization of information, an ecosystem that is feedback by traditional vehicles, journalists, and people who interact with the artifacts.O presente artigo tem como objetivo compreender as transformações causadas por novos ecossistemas informacionais no jornalismo contemporâneo. Essa análise é realizada a partir de notícias acessadas através de assistentes pessoais digitais embarcados em alto-falantes inteligentes. Como procedimento metodológico, adota o estudo de caso múltiplo, definindo como objeto os transatores vocais da Google (Nest Home/Google Assistant) e da Amazon (Echo/Alexa). Observa, portanto, que a inclusão de rotinas algorítmicas e a extensão de conteúdo noticioso para interfaces de voz inteligentes demanda adaptação para a personalização das informações, ecossistema que é retroalimentado por veículos tradicionais, jornalistas e pessoas que interagem com os artefatos.Este artículo tiene como objetivo comprender las transformaciones causadas por los nuevos ecosistemas informativos en el periodismo contemporáneo. Este análisis se realiza en función de las noticias a las que se accede a través de asistentes digitales personales integrados en altavoces inteligentes. Como procedimiento metodológico, adopta un estudio de caso múltiple, definiendo los transactores vocales de Google (Nest Home/Google Assistant) y Amazon (Echo/Alexa) como su objeto. Señala, por lo tanto, que la inclusión de rutinas algorítmicas y la extensión del contenido de noticias a interfaces de voz inteligentes requiere adaptación para la personalización de la información, un ecosistema que es retroalimentado por vehículos tradicionales, periodistas y personas que interactúan con los artefactos.

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Shahin, Nada, and Mohamed Watfa. "Deaf and hard of hearing in the United Arab Emirates interacting with Alexa, an intelligent personal assistant." Technology and Disability 32, no. 4 (November 20, 2020): 255–69. http://dx.doi.org/10.3233/tad-200286.

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BACKGROUND: Intelligent Personal Assistants have been booming around the world since 2014, allowing millions of users to interact with different cloud-based software via speech. Unfortunately, the Deaf and Hard of Hearing individuals have been left out without recognizable accessibility to such technologies, although it might be used to make their daily life routine easier. OBJECTIVE: In this research, the researcher studies the interaction and perception of Amazon’s Alexa among the Deaf and Hard of Hearing in the United Arab Emirates in its current set up (Tap-to-Alexa accessibility option) in addition to Sign Language as an input method. The researcher expands on the Technology Acceptance Model to study the acceptance of Alexa as an assistive technology for the Deaf and Hard of Hearing. Additionally, the researcher discusses more suitable input methods and solutions to allow Alexa, and other Intelligent Personal Assistants, be more accessible for the Deaf and Hard of Hearing. METHODS: The mixed method is used in this research in terms of collecting primary data through hands-on experiments, surveys, and interviews with the Deaf and Hard of Hearing participants. RESULTS: The researcher found that the Deaf and Hard of Hearing in the United Arab Emirates perceive that Sign Language combined with a Live interpreter is better than the accessibility option “Tap-to-Alexa”, which is a solution provided by Amazon. The researcher also found that Sign Language combined with a Live interpreter is the most suitable input method to make the device accessible for the Deaf and Hard of Hearing, in addition to translating the “Tap-to-Alexa” to different languages. Finally, the researcher proposes a modification to the Technology Acceptance Model to suit the research study of the Deaf and Hard of Hearing perception of Alexa. CONCLUSIONS: The researcher concludes that the ideal scenario for the Deaf and Hard of Hearing to interact and benefit the most from Amazon’s Alexa, and IPAs in general, is to include Sign Language as an embedded input method in the device and provide live interpreters; this sheds light on the importance of the interpreters’ jobs around the world. Additionally, “Tap-to-Alexa” must be translated into different languages for a better perception of the input method.

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Vedula, Nikhita. "Modeling knowledge and functional intent for context-aware pragmatic analysis." ACM SIGWEB Newsletter, Winter (January 2021): 1–4. http://dx.doi.org/10.1145/3447879.3447882.

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Nikhita Vedula is an Applied Scientist at Amazon Alexa Science. She obtained her PhD in Computer Science and Engineering from the Ohio State University in August 2020, advised by Professor Srinivasan Parthasarathy. She received her bachelor's degree from the National Institute of Technology, Nagpur, India in 2015. Her research interests are at the intersection of data mining, natural language processing and social computing. Over the course of her PhD, her research involved designing efficient and novel machine learning and computational linguistic techniques that extract, interpret and transform the vast, unstructured digital content into structured knowledge representations in diverse contexts. She has worked with researchers from interdisciplinary fields such as emergency response, marketing, sociology and psychology. She performed research internships at Nokia Bell Laboratories, Adobe Research and Amazon Alexa AI. Her work has been published at several top data mining conferences such as the Web Conference, SIGIR, WSDM and ICDM. Her work on detecting user intentions from their natural language interactions won the Best paper award at the Web Conference 2020. She was a recipient of a Graduate Research Award (2020), a Presidential Fellowship (2019) and a University Graduate Fellowship (2015) at the Ohio State University. She was also selected as a Rising Star in EECS (2019).

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Khatri, Chandra, Anu Venkatesh, Behnam Hedayatnia, Raefer Gabriel, Ashwin Ram, and Rohit Prasad. "Alexa Prize — State of the Art in Conversational AI." AI Magazine 39, no. 3 (September 28, 2018): 40–55. http://dx.doi.org/10.1609/aimag.v39i3.2810.

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To advance the state of the art in conversational AI, Amazon launched the Alexa Prize, a 2.5 million dollar competition that challenges university teams to build conversational agents, or "socialbots", that can converse coherently and engagingly with humans on popular topics for 20 minutes. The Alexa Prize offers the academic community a unique opportunity to perform research at scale with real conversational data obtained by interacting with millions of Alexa users, along with user-provided ratings and feedback, over several months. This enables teams to effectively iterate, improve and evaluate their socialbots throughout the competition. Sixteen teams were selected for the inaugural competition last year. To build their socialbots, the students combined state-of-the-art techniques with their own novel strategies in the areas of Natural Language Understanding and Conversational AI. This article reports on the research conducted over the 2017-2018 year. While the 20 minute grand challenge was not achieved in the first year, the competition produced several conversational agents that advanced the state of the art, are interesting for everyday users to interact with, and help form a baseline for the second year of the competition.

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Hyma, J., M. Rama Krishna Murty, and A. Naveen. "Personalized privacy assistant for digital voice assistants: Case study on Amazon Alexa." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 3 (November 10, 2021): 291–97. http://dx.doi.org/10.3233/kes-210071.

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The advancements in modern technologies permit the invention of various digital devices which are controlled and activated by people’s gestures, touch and even by one’s voice. Google Assistant, iPhone Siri, Amazon Alexa etc., are most popular voice enabled devices which have grabbed the attention of digital gadget users. Their usage definitely makes the life easier and comfortable. The other side of these smart enabled devices is incredible violation of the privacy. This happens due to their continuous listening to the user and data transmission over a public network to the third-party services. The work proposed in this paper attempts to overcome the existing privacy violation problem with the voice enabled devices. The main idea is to incorporate an intelligent privacy assistant that works based on the user preferences over their data.

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Bedford-Strohm, Jonas. "Voice First? Eine Analyse des Potentials von intelligenten Sprachassistenten am Beispiel Amazon Alexa." Communicatio Socialis 50, no. 4 (2017): 485–94. http://dx.doi.org/10.5771/0010-3497-2017-4-485.

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Jun Seo Park, Kim, Min Young, 임걸, and 김유진. "Gagné 수업이론에 기반한 AI스피커 교육콘텐츠 분석 및 발전방향 연구 : Amazon Alexa Skills를 중심으로." Journal of Knowledge Information Technology and Systems 14, no. 4 (August 2019): 345–58. http://dx.doi.org/10.34163/jkits.2019.14.4.004.

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Neville, Stephen. "Eavesmining: A Critical Audit of the Amazon Echo and Alexa Conditions of Use." Surveillance & Society 18, no. 3 (August 19, 2020): 343–56. http://dx.doi.org/10.24908/ss.v18i3.13426.

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The emergence of smart speakers and voice-activated personal assistants (VAPAs) calls for updated scrutiny and theorization of auditory surveillance. This paper introduces the neologism and concept of “eavesmining” (eavesdropping + data mining) to characterize a mode of surveillance that operates on the edge of acoustic space and digital infrastructure. In contributing to a sonic epistemology of surveillance, I explain how eavesmining platforms and processes burrow the voice as a medium between sound and data and articulate the acoustic excavation of smart environments. The paper discusses eavesmining in relation to theories of dataveillance, the sensor society, and surveillance capitalism before outlining the potential contributions offered by a theoretical alignment with sound studies literature. The paper centers on an empirical case study of the Amazon Echo and Alexa conditions of use. By conducting a discourse analysis of Amazon’s End User Agreements (EUAs), I provide evidence in support of growing privacy and surveillance concerns produced by Amazon’s eavesmining platform that are obfuscated by the illegibility of the documents.

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Chattopadhyay, Dr Ayan, and Mr Mukul Basu. "Unsupervised Learning Based Brand Sentiment Mining using Lexicon Approaches A Study on Amazon Alexa." Indian Journal of Data Mining 1, no. 3 (May 30, 2022): 15–20. http://dx.doi.org/10.54105/ijdm.c1619.051322.

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Consumer sentiment analysis has gained immense attention in the recent past. The abundance of data in today’s world, especially those generated from the social media platforms, has triggered sentiment exploration like never before. The analysis of consumer sentiments have indeed helped organizations in effective decision making worldwide. In the communication technology domain, voice activated virtual assistants (VAVAs) are one of the latest entrants and they are gaining immense popularity by the time. Brand sentiment studies on VAVAs being limited in number creates an opportunity to explore further. This study fits into the domain of sentiment mining and the purpose of the paper is to review the consumer sentiment towards the global leader brand in the voice activated virtual assistant product segment, Amazon Alexa. Of the various approaches available, the researchers chose unsupervised learning based lexicon approach to estimate the brand sentiment. Three popular lexicon based sentiment classifiers, TextBlob, VADER and AFINN, have been used in the present context for exploration purpose. To the best of the knowledge of the researchers, this research effort includes, for the first time, multiple lexicon based approaches in exploring the sentiment towards the brand Alexa. This study shows consumers to have a significantly positive sentiment towards the chosen brand. The output from the three comparative classifiers reveal similar results which also validates the robustness of the outcomes and that of the chosen methods. The study anticipates a bright sales potential of the brand. Also, the use of alternative lexicon approaches is expected to enrich the existing literature in the sentiment mining domain.

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Lessio, Nadine. "Making SAD Home: An exploration into developing an Alexa with depression." Virtual Creativity 10, no. 1 (April 1, 2020): 59–71. http://dx.doi.org/10.1386/vcr_00018_1.

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While chatbots are a space that has been researched and worked on for the past few decades, a renewed industry interest in artificial intelligence (AI) and the popularity of devices like Amazon Alexa and Google Home has pushed them back into the spotlight. According to Edison Research and National Public Media, an estimated 21 per cent of US households now use a voice-enabled smart device in some capacity. Similarly, the popularity of texting, technology-mediated communication and social media has laid the groundwork for the return of chatbots. Chatbots are even making inroads into areas like mental health, where they are being used to address the growing mental health concerns of wellness and loneliness. While this is an interesting development, the conversation of what is considered useful in a mental health chatbot is still very much driven by commercial applications. This article considers using natural language processing and networking technologies to explore a more DIY approach to mental-health-based chatbots, by documenting the development of an Alexa that experiences depression.

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Pawlaszczyk, D., J. Friese, and C. Hummert. "Alexa, tell me - A forensic examination of the Amazon Echo Dot 3 rd Generation." International Journal of Computer Sciences and Engineering 7, no. 11 (November 30, 2019): 20–29. http://dx.doi.org/10.26438/ijcse/v7i11.2029.

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Bogdan Gabriel, ȘTEFAN. "UNDERSTANDING SPUTNIK NEWS AGENCY INTERNET TRAFFIC ANALYSIS." SERIES VII - SOCIAL SCIENCES AND LAW 13(62), no. 1 (2020): 113–24. http://dx.doi.org/10.31926/but.ssl.2020.13.62.1.12.

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Sputnik news agency remains one of the main-channels used by Russia to conduct disinformation campaigns across its borders, affecting both Romania and the Republic of Moldova virtual communities. This research offers a practical methodological solution for measuring communication outcomes and describing audience and its behavior and it shows that, at the end of 2018, Sputnik was a peripheral news platform for the Romanian informational space and a growing threat for the Republic of Moldova, where it occupied a leading position. The evaluation was conducted with data extracted through Alexa service provided by Amazon and Gemius data - the Moldovan Audit Office of Circuits and Internet.

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Brown, Sarah. "Partnerships between health authorities and Amazon Alexa raise many possibilities — and just as many questions." Canadian Medical Association Journal 191, no. 41 (October 14, 2019): E1141—E1142. http://dx.doi.org/10.1503/cmaj.1095799.

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Davis, Jensen, Shannon Howard, Gregory King, Phanidar Boddu, Kiran Jyothi, and Joan McDowd. "ALEXA, ASSESS MY MEMORY: THE FEASIBILITY OF EXTENDED HEALTH MONITORING IN AN OLDER-ADULT-LIVING COMMUNITY." Innovation in Aging 3, Supplement_1 (November 2019): S337. http://dx.doi.org/10.1093/geroni/igz038.1224.

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Abstract The goal of most older adults is to live independently in their own homes, for as long as possible. There are many advantages to aging in place for the individual, but also challenges as changes in cognitive and physical health can occur over time. Especially for older adults living alone, tracking these changes is critical for early intervention and prevention. The relatively easy availability of consumer technology may provide one mechanism for monitoring older adults in their homes. We designed a pilot study to test the feasibility and acceptability of using wearable sensors (Fitbit sensors), in conjunction with automated interactive voice recognition technology (Amazon Echo), to monitor older adults’ physical and cognitive health during daily activities. Participants (7 females, 2 males; 65-80 years of age) were recruited from a housing complex for older adults with low income. They were interviewed about health monitoring technology before and after a 2-week measurement period during which they were expected to wear the Fitbit daily and interact with the Amazon Echo for 8 consecutive days. Feasibility challenges included limited skill in Echo interactions, remembering to do the assessments, and charging/uploading Fitbit data. Qualitative analysis of interviews revealed generally positive attitudes about technology, but low comfort operating the devices. These preliminary findings suggest that with additional training for older adults, sensors and voice recognition technologies could have significant roles in maintaining older adult quality of life by contributing to early detection of decline and timely intervention.

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Dizon, Gilbert, and Daniel Tang. "Intelligent personal assistants for autonomous second language learning: An investigation of Alexa." JALT CALL Journal 16, no. 2 (August 31, 2020): 107–20. http://dx.doi.org/10.29140/jaltcall.v16n2.273.

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The ubiquity of smartphones and the growing popularity of smart speakers have given rise to cloud-based, intelligent personal assistants (IPAs), such as Siri and Google Assistant. However, little is known about the use of IPAs for Autonomous Second Language Learning (ASLL). Thus, the aims of this study were twofold: to assess Japanese English as a Foreign Language (EFL) students’ perceptions towards IPAs, also known as virtual assistants, for ASLL, and to better understand learner behavior of these technologies. A total of 14 Japanese university students were given smart speakers and interacted with a companion IPA, Amazon Alexa, over a two-month period in their homes. Moreover, the participants completed a survey consisting of Likert-scale items and open-ended questions to obtain their views of the IPA for ASLL. While the results indicated that the students had mostly favorable views of Alexa for L2 learning, many of them did not actively engage with the virtual assistant during the data collection period. Furthermore, students tended to give up when faced with communication difficulties with the IPA. These findings highlight the potential of IPAs for ASLL and underscore the gap between what students say, and what they actually do, with language learning technology.

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Lopatovska, Irene. "Classification of humorous interactions with intelligent personal assistants." Journal of Librarianship and Information Science 52, no. 3 (December 18, 2019): 931–42. http://dx.doi.org/10.1177/0961000619891771.

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The study examined humorous interactions with intelligent personal assistants (IPAs, including Google Assistant, Amazon Alexa, Microsoft Cortana, Apple Siri) with the aim of classifying user utterances, IPA responses and user reactions of system responses. Data from online diaries and paper questionnaires were collected and analyzed using content analysis method. The findings suggest that the most frequent types of utterances include questions that test system “personality” and opinions. Joke requests are also frequent and produce pre-programmed humor that users generally find funny. The initial classification of humorous utterances has been validated and expanded using published datasets of humorous utterances for the four investigated IPAs. The findings can be used for immediate improvements to IPA performance as well as long-term development of IPA personas.

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Parvin, Nassim. "Look Up and Smile! Seeing through Alexa’s Algorithmic Gaze." Catalyst: Feminism, Theory, Technoscience 5, no. 1 (March 28, 2019): 1–11. http://dx.doi.org/10.28968/cftt.v5i1.29592.

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Echo Look is one latest product by Amazon built on the artificial intelligence agent Alexa designed to be a virtual fashion assistant. This paper draws on feminist theory to critically engage with the premises and promises of this new technology. More specifically, I demonstrate how the introduction of Echo Look is an occasion to think through ethical and political issues at stake in the particular space it enters, in this case no less than what is perceived of (women’s) bodies and what fashion is and does. In addition, the specific domain helps us see this category of technology anew, illuminating its taken-for-granted assumptions. More specifically, it serves as yet another reminder of what algorithms cannot do and of their oppressive potency.

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Ferrand, John, Ryli Hockensmith, Rebecca Fagen Houghton, and Eric R. Walsh-Buhi. "Evaluating Smart Assistant Responses for Accuracy and Misinformation Regarding Human Papillomavirus Vaccination: Content Analysis Study." Journal of Medical Internet Research 22, no. 8 (August 3, 2020): e19018. http://dx.doi.org/10.2196/19018.

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Background Almost half (46%) of Americans have used a smart assistant of some kind (eg, Apple Siri), and 25% have used a stand-alone smart assistant (eg, Amazon Echo). This positions smart assistants as potentially useful modalities for retrieving health-related information; however, the accuracy of smart assistant responses lacks rigorous evaluation. Objective This study aimed to evaluate the levels of accuracy, misinformation, and sentiment in smart assistant responses to human papillomavirus (HPV) vaccination–related questions. Methods We systematically examined responses to questions about the HPV vaccine from the following four most popular smart assistants: Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. One team member posed 10 questions to each smart assistant and recorded all queries and responses. Two raters independently coded all responses (κ=0.85). We then assessed differences among the smart assistants in terms of response accuracy, presence of misinformation, and sentiment regarding the HPV vaccine. Results A total of 103 responses were obtained from the 10 questions posed across the smart assistants. Google Assistant data were excluded owing to nonresponse. Over half (n=63, 61%) of the responses of the remaining three smart assistants were accurate. We found statistically significant differences across the smart assistants (N=103, χ22=7.807, P=.02), with Cortana yielding the greatest proportion of misinformation. Siri yielded the greatest proportion of accurate responses (n=26, 72%), whereas Cortana yielded the lowest proportion of accurate responses (n=33, 54%). Most response sentiments across smart assistants were positive (n=65, 64%) or neutral (n=18, 18%), but Cortana’s responses yielded the largest proportion of negative sentiment (n=7, 12%). Conclusions Smart assistants appear to be average-quality sources for HPV vaccination information, with Alexa responding most reliably. Cortana returned the largest proportion of inaccurate responses, the most misinformation, and the greatest proportion of results with negative sentiments. More collaboration between technology companies and public health entities is necessary to improve the retrieval of accurate health information via smart assistants.

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Orr, Douglas A., and Laura Sanchez. "Alexa , did you get that? Determining the evidentiary value of data stored by the Amazon® Echo." Digital Investigation 24 (March 2018): 72–78. http://dx.doi.org/10.1016/j.diin.2017.12.002.

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Малиновкин, В. А., Н. В. Валуйских, Н. Н. Шведов, С. Л. Кенин, and Н. И. Гребенникова. "OVERVIEW OF VOICE INTERFACE TOOLS AND SPEECH RECOGNITION TECHNOLOGIES." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 1 (March 14, 2022): 42–46. http://dx.doi.org/10.36622/vstu.2022.18.1.005.

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Задача распознавания речи - одна из самых сложных и востребованных задач в настоящее время. Голосовые помощники, реализованные с помощью голосовых интерфейсов, заметно упрощают управление различными системами в ограниченных условиях. Такие интерфейсы должны обладать интуитивно понятным интерфейсом для комфортного пользования людьми с различного рода ограничениями. При разработке системы выполнение этого критерия является одной из главных задач при создании современных технических систем. В настоящее время мировой рынок распознавания речи имеет огромный объём и высокую динамику развития, в то время как рынок систем распознавания речи в России невелик, но имеет перспективы к развитию. Pассмотрены наиболее известные голосовые помощники: Google Assistant, Amazon Alexa, Microsoft Cortana, Siri, Яндекс Алиса, произведено сравнение по общим показателям. Были выявлены как положительные стороны, так и отрицательные. Преимущества обусловлены такими параметрами, как наличие высокого уровня «человечности», способность к самообучению, автоматическое воспроизведение операций. Среди недостатков наиболее критичны такие моменты, как несоответствие качества по времени отклика и предоставленным функционалом, отсутствие быстрой интеграции с другими системами и универсальной принадлежности Speech recognition task is one of the most difficult and demanded tasks at the present time. Voice assistants implemented using voice interfaces greatly simplify the management of various systems in limited conditions. Such interfaces should have an intuitive interface for comfortable use by people with various kinds of limitations. When developing a system, the fulfillment of this criterion is one of the main tasks in creating modern technical systems. Currently, the global speech recognition market is of huge volume and high dynamics of development, while the market for speech recognition systems in Russia is small but has prospects for development. This article reviewed the most advanced voice assistants: Goodle Assistand, Amazon Alexa, Microsoft Cortana, Siri, Yandex Alice. We made a comparison in terms of general indicators. Both positive and negative aspects were identified. The advantages are due to such parameters as: the presence of a high level of "humanity", the ability to self-learn, automatically reproduce operations. Among the drawbacks, the most critical are such moments as: mismatch in quality in terms of compliance with the response time and the provided functionality, the lack of quick integration with other systems and universal accessory

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Kadylak, Travis, Roshanak Khaleghi, Kenneth Blocker, Saahithya Gowrishankar, Widya Ramadhani, Lyndsie Koon, Ramavarapu Sreenivas, and Wendy Rogers. "Digital Home Assistant Health Applications for Older Adults With Long-Term Mobility Disabilities." Innovation in Aging 4, Supplement_1 (December 1, 2020): 600. http://dx.doi.org/10.1093/geroni/igaa057.2019.

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Abstract The aim of the current study was to evaluate the feasibility, usability, safety, and efficacy of digital home assistant health applications (e.g., meditation applications, medication reminders, hydration management) for older adults with mobility disabilities. We used a multi-pronged approach. First, we compiled, categorized, and assessed a list of commercially available health applications compatible with Amazon Alexa devices. We reviewed data from the National Health and Aging Trends Study and the ACCESS study to identify challenges that older adults with mobility disabilities face within the home. We also reviewed the literature on the acceptance and use of digital home assistant health applications by older adults. Lastly, we conducted user testing in a laboratory and in a home-simulation environment to assess usability of different health applications. Our results provide guidance for the implementation of digital home assistant health applications to support older adults with long-term mobility disabilities.

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Dunn, Mimi, Adam Landman, Jennifer Cartright, Anne Bane, Anne Brogan, Caroline Coy, and Haipeng Zhang. "Notes From the Field: A Voice-Activated Video Communication System for Nurses to Communicate With Inpatients With COVID-19." JMIR Formative Research 6, no. 3 (March 28, 2022): e31342. http://dx.doi.org/10.2196/31342.

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With the relaxing of telehealth regulations through the Health Insurance Portability and Accountability Act (HIPAA) waiver notification for Telehealth Remote Communications during the COVID-19 Nationwide Public Health Emergency, our organization had the opportunity to pilot an innovative virtual care solution using a modified consumer-grade voice-activated video communication system (Amazon Echo Show 8) within one inpatient COVID-19 unit. In this brief report, we describe our experiences with implementing the system and general feedback from clinicians, and discuss areas for future development required to enable future scaling of this solution. Our pilot demonstrates the feasibility of deploying a consumer-grade voice assistant device in COVID-19 patient rooms. We found the devices engaging due to the voice technologies and Alexa functionalities for both clinician and patient entertainment. To enable future deployment at scale, enhancements to the Echo Show and data analytics will need to be further explored.

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Pighin, Melissa, and Yong Kyung Choi. "The Evaluation of Smart Speaker Skills for Chronic Disease Management of Older Adults." Innovation in Aging 5, Supplement_1 (December 1, 2021): 689. http://dx.doi.org/10.1093/geroni/igab046.2588.

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Abstract A voice-activated smart speaker is an emerging technology that presents unique opportunities to support the chronic disease management of older adults. We identified the available health-related smart speaker skills in Amazon Alexa platform that support chronic disease management and assessed their functionalities to inform the development of a home-based lifestyle intervention program for older adults with cardiovascular disease and type 2 diabetes. From January to March 2021, we searched Alexa Skills using keywords related to diabetes, medication, blood pressure and nutrition management. Our search produced total 156 potentially relevant skills (63 diabetes, 57 medication, 11 blood pressure and 25 nutrition related), of which 22 skills met inclusion criteria. Apps were excluded if it was only informational, not relevant to the topic, had zero user rating, available in language other than English, and required an external device or a subscription to a specific health plan or service. 22 skills (4 diabetes, 8 medication, 3 blood pressure and 7 nutrition) were evaluated with Echo Show 8 device. The skills were evaluated using the modified version of IMS Institute for Healthcare Informatics app functionality scores and the score (0 to 11) was calculated accordingly. The median number of functionalities was 3.5 and 68% of skills (15/22) had 4 or fewer functions. The highest rated skill was a medication management app named myNurseBot having 6 out of 11 functionalities. The poor functionality score highlights a need for a more robust and comprehensive smart speaker skill to support the health management of older adults.

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Jansons, Paul, J. Dalla Via, R. M. Daly, J. J. Fyfe, E. Gvozdenko, and D. Scott. "Delivery of Home-Based Exercise Interventions in Older Adults Facilitated by Amazon Alexa: A 12-week Feasibility Trial." Journal of nutrition, health & aging 26, no. 1 (December 20, 2021): 96–102. http://dx.doi.org/10.1007/s12603-021-1717-0.

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Journal articles on the topic 'Amazon Alexa'

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