Echo Dot Research Articles

Validation of system usability scale as a usability metric to evaluate voice user interfaces.

In recent years, user experience (UX) has gained importance in the field of interactive systems. To ensure its success, interactive systems must be evaluated. As most of the standardized evaluation tools are dedicated to graphical user interfaces (GUIs), the evaluation of voice-based interactive systems or voice user interfaces is still in its infancy. With the help of a well-established evaluation scale, the System Usability Scale (SUS), two prominent, widely accepted voice assistants were evaluated. The evaluation, with SUS, was conducted with 16 participants who performed a set of tasks on Amazon Alexa Echo Dot and Google Nest Mini. We compared the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. Furthermore, we derived the confidence interval for both voice assistants. To enhance understanding for usability practitioners, we analyzed the Adjective Rating Score of both interfaces to comprehend the experience of an interface's usability through words rather than numbers. Additionally, we validated the correlation between the SUS score and the Adjective Rating Score. Finally, a paired sample t-test was conducted to compare the SUS score of Amazon Alexa Echo Dot and Google Nest Mini. This resulted in a huge difference in scores. Hence, in this study, we corroborate the utility of the SUS in voice user interfaces and conclude by encouraging researchers to use SUS as a usability metric to evaluate voice user interfaces.

Alexa Enabled IoT Device Simulation Using C# And AWS Lambda

Purpose: Nowadays, device operation using voice commands is becoming popular. Several voice command services are available. Among them, Alexa from Amazon is the most popular. Almost Every day, vendors are integrating Alexa into their new products worldwide. We need physical devices for the device operation or understanding of the process flow of Alexa. Sometimes, physical devices are costly, or availability is poor. Here, we demonstrated how to create a simulated Alexa-enabled IoT device. We used several Amazon cloud services to execute the process flow. Alexa's skill is developed inside the Alexa developer console. To process the command, we use the AWS Lambda function. AWS IoT cloud service is used to trigger IoT devices over MQTT. For simulated devices, we are using a C# MQTT client. The researcher trying to simulate an Alexa-enabled device into their project can get some reference information from this work. Design/Methodology/Approach: We create a graphical user interface (GUI) to interact with the user or display the device's status. The GUI is connected with the C# AWS IoT Device Shadow client. We created IoT Things in the AWS cloud. Under the IoT Shadow, we created a Device shadow. The Alexa service triggers the lambda function. The Lambda function updates the Shadow register, which resides inside the cloud. The c# shadow client receives a notification when the device Shadow updates and updates the GUI element that represents the equipment. We can use any Alexa device to send voice commands, like the Alexa mobile app, Echo Dot, or Alexa PC app. Findings/Result: Through this research work, we created and tested virtual devices that can be experiments or research work using Alexa voice commands. It has been tested for a long time. It performed well, and no issue was found. Using more code-level protection it can be robust for practical implementation. Originality/Value: We found several fragmented documents over the web to implement Alexa-enabled virtual devices. After lots of study, we did practical and included the procedure in this research work. From voice input to load trigger, there are lots of steps involved. The complete guidance is not available easily. So through this research work, if someone follows, they can easily create a voice-operated device. This research work may add value to the researcher experimenting with the Alexa command-activated device. Paper Type: Experimental-based Research.

From Data to Human-Readable Requirements: Advancing Requirements Elicitation through Language-Transformer-Enhanced Opportunity Mining

In this research, we present an algorithm that leverages language-transformer technologies to automate the generation of product requirements, utilizing E-Shop consumer reviews as a data source. Our methodology combines classical natural language processing techniques with diverse functions derived from transformer concepts, including keyword and summary generation. To effectively capture the most critical requirements, we employ the opportunity matrix as a robust mechanism for identifying and prioritizing urgent needs. Utilizing transformer technologies, mainly through the implementation of summarization and sentiment analysis, we can extract fundamental requirements from consumer assessments. As a practical demonstration, we apply our technology to analyze the ratings of the Amazon echo dot, showcasing our algorithm’s superiority over conventional approaches by extracting human-readable problem descriptions to identify critical user needs. The results of our study exemplify the potential of transformer-enhanced opportunity mining in advancing the requirements-elicitation processes. Our approach streamlines product improvement by extracting human-readable problem descriptions from E-Shop consumer reviews, augmenting operational efficiency, and facilitating decision-making. These findings underscore the transformative impact of incorporating transformer technologies within requirements engineering, paving the way for more effective and scalable algorithms to elicit and address user needs.

Let Us Create an Alexa-Enabled IoT Device Using C#, AWS Lambda, and ESP Module

Purpose: Voice-operated query is becoming popular. Alexa Such a device that is now widely used. Several vendors provide built-in Alexa services to their devices. In our fast day-to-day life, it is the most relevant Technology. It is safe and convenient also. Doing multiple work at a time is a common scenario. We can quickly get weather updates while cooking. We use our voice to operate AC, Fan, light, and electronic gadgets. Several tech giants like Amazon made Technology easy for us. Our non-Alexa devices can easily convert into Alexa enabled. We can operate our device or electric equipment just over voice. Here we provide the procedure for creating an Alexa-enabled device quickly. Using several AWS services like Alexa Skill, Lambda, and IoT, We can quickly build an Alexa-enabled custom device for us. Design/Methodology/Approach: The Alexa-enabled devices can be built following a series of processes. To create the Alexa-enabled device, we develop Alexa skills inside the Alexa developer console, write and upload command processor Lambda function using C# language inside the Microsoft visual studio, and configure the IoT profile inside the AWS IoT cloud server. Furthermore, finally, create ESP firmware to operate the equipment. To send the voice command, we need any Alexa-enabled devices. It may be an echo dot, mobile phone, or PC application. Findings/Result: Combining Amazon Alexa with IoT devices presents a potent synergy, bringing immense potential for improved user experiences and expanded automation and control capabilities. Developing an Alexa-enabled IoT device with C# and AWS Lambda is relatively uncomplicated, provided you possess some programming skills and familiarity with cloud computing. The device gains internet connectivity by leveraging an ESP module, facilitating seamless communication with the Alexa Voice Service, and empowering voice-based commands and responses. The versatility of this device spans a wide array of applications, encompassing the management of lights, switches, appliances, and more, all of which can be further tailored to specific user requirements. Originality/Value: Several documents are available to create Alexa-enabled devices. Here we provide the procedure with a block diagram and data flow so that the researcher can easily create and integrate it into their research work. All information is practical-oriented. We can quickly build Alexa-enabled devices using the practical example following this work. Paper Type: Experimental-based Research.

How do Older Adults Set Up Voice Assistants? Lessons Learned from a Deployment Experience for Older Adults to Set Up Standalone Voice Assistants.

While standalone Voice Assistants (VAs) are promising to support older adults' daily routine and wellbeing management, onboarding and setting up these devices can be challenging. Although some older adults choose to seek assistance from technicians and adult children, easy set up processes that facilitate independent use are still critical, especially for those who do not have access to external resources. We aim to understand the older adults' experience while setting up commercially available voice-only and voice-first screen-based VAs. Rooted in participants observations and semi-structured interviews, we designed a within-subject study with 10 older adults using Amazon Echo Dot and Echo Show. We identified the values of the built-in touchscreen and the instruction documents, as well as the impact of form factors, and outline important directions to support older adult independence with VAs.

An IoT-Based Multimodal Real-Time Home Control System for the Physically Challenged: Design and Implementation

Physical impairments affect a significant proportion of the global populace, emphasizing the need for assistive technologies to increase the ability of these individuals to perform daily activities autonomously. This study discusses the development and implementation of a multimodal home control system, designed to afford physically challenged individuals greater control over their home environments. This system utilizes the Internet of Things (IoT) for its functionality. The system is primarily based on the utilization of the Amazon Alexa Echo Dot, which facilitates speech-based control, and a sequential clap recognition system, both made possible through an internet connection. These methods are further supplemented by an additional manual switching option, thereby ensuring a diverse range of control methods. The processing core of this system consists of an Arduino Uno and an ESP32 Devkit module. In conjunction with these, a sound detector is employed to discern and process a variety of clap patterns, which is set to function at a predefined threshold. The Amazon Alexa Echo Dot serves as the primary interface for voice commands and real-time information retrieval. Furthermore, an Android smartphone, equipped with the Alexa application, provides alternate interfaces for appliance control, through both soft buttons and voice commands. Based on an analysis of this system, it is suggested that it is not only viable but also effective. Key attributes of the system include rapid response times, aesthetic appeal, secure operation, low energy consumption, and most importantly, increased accessibility for physically disabled individuals.

Nondestructive Data Acquisition Methodology for IoT Devices: A Case Study on Amazon Echo Dot Version 2

The smart speaker is becoming a common part of the modern household, which usually includes an AI-powered Intelligent Voice Assistant to communicate with its users. Amazon Echo Dot is a popular smart speaker that extends the above-stated functionality by acting as a communication hub for other Internet of Things (IoT) and mobile devices within its local network. The nature and volume of data that an Echo Dot handles make it a potential source of evidence, if one is seized for a digital forensics investigation. Researchers and practitioners have explored various techniques to extract data from these IoT devices. However, traditional methods make changes to the physical device and/or its data, which is undesirable from a digital forensics perspective. The current work focuses on developing a nondestructive methodology for extracting data from IoT devices, with Amazon Echo Dot version 2 as an example, which use embedded Multimedia Card (eMMC)/embedded Multichip Package (eMCP) chips as their primary storage. We identify all in-system programming (ISP) pins using the computed tomography (CT) Scan imagery of the main printed circuit board (PCB) of the device. We created a 3-D fixture that accommodates pogo pin connectors to create contact with the already identified ISP taps on the main PCB. The 3-D Test Probe Jig can extract data from an IoT device’s memory chip using an eMMC reader. The proposed nondestructive solution is reproducible, portable, and affordable.

Voice Assistants as Consultants for Male Patients with Sexual Dysfunction: A Reliable Option?

The aim of this study was to evaluate the ability of Google Assistant, Alexa, and Siri to recognize and answer questions about male sexual health. Each VA was tested on a smart speaker: Alexa on Amazon Echo Dot 4th Gen., Google Assistant on Google Home Mini, and Siri on Apple HomePod. A pool of patients' frequently asked questions regarding erectile dysfunction (ED), premature ejaculation (PE), Peyronie's disease (PD), male infertility, and other aspects of male sexual health were identified by authors. The recognition of question was evaluated ("yes" or "not"). For each recognized question, the response characteristics (domains) were rated on a scale from 0 to 10 (according to the quality). We chose the recognition rate of the questions as the primary outcome and the quality of the answers as the secondary outcome. Overall, the best VA in recognizing questions was Siri, with a total of 83.3% questions compared with 64.0% for Alexa (p = 0.024) and 74.0% for Google Assistant (p = 0.061). Siri was associated with a significantly higher recognition rate than Alexa for PE (80% vs. 40%; p = 0.002) and PD (66.7% vs. 33.3%; p = 0.010). The quality of the responses was classified as low in 57 out of 105 cases (54.3%), intermediate in 46 cases (43.8%), and high in only 2 cases (1.9%), highlighting an overall intermediate-low quality of the answers. Male infertility was the condition associated with the highest mean scores in "Targeted response to the problem" (7.32 ± 2.57), "Scientific correctness of the answer", (5.9 ± 2.76) "Completeness of the answer" (5.14 ± 2.56), and "Understandability of the response for a patient" (5.3 ± 2.51) domains. Siri was associated with significantly higher scores than Alexa (p < 0.05) in several domains of all conditions evaluated. The question recognition rate of VAs is quite high; however, the quality of the answers is still intermediate-low. Siri seems superior to Alexa in both question recognition and response quality. Male infertility appears to be the sexual dysfunction best addressed by VAs.

Review on Alexa as a Hospital Receptionist

A hospital receptionist is the first point of contact between a hospital or medical facility and patients. These receptionists play an vital role in a medical facility because they perform customer service and administrative-related duties like scheduling appointments, answering patients' questions, answering phone calls and placing calls to confirm patients' appointments. A questionnaire was designed for the expectant patients, to design the Alexa. The inputs were captured to verify the feasibility, relevance, and technology acceptance for the use case. Chatbot designs are slowly changing from voice-to-voice communication like an alexa which was tested on Echo dot, a smart speaker device supported by Amazon Voice Service (AVS). Designing a chatbot on top of a custom Alexa skill allows developers to use multiple Amazon Web Services. The design has used AWS for hosting voice server and Azure for hosting Back-end which is created in c#. After that we connect Echo device with our created system. The concept of connecting smart devices, makes the chatbot solution accessible at any time and from anywhere.

ODP168 Can Virtual Assistants Help our Diverse Population of Diabetic Patients?

Abstract Background: Diabetes affects 34 million in the US and an additional 88 million are prediabetic. 1 According to the CDC, Diabetes affects Hispanic Americans (22%) and Asian Americans (19%) more than the Caucasian (12%) population. 2 Many of these patients do not speak English. In addition, 55% of US adults did not graduate from high school. 3 Health literacy is a barrier to health care. There are 4.2 billion Virtual Assistant AI devices, e. g. Amazon Echo Dot and Google Home Mini, which are used worldwide. Virtual assistant devices are used in nursing homes and assisted living facilities. 4 Purpose: To evaluate Virtual Assistant Devices, Alexa and Google, as a educational tool for our multicultural Diabetic Population Methods: Evaluation of Amazon Echo Dot (Alexa) and Google Home Mini (Google) with voice activation in English, Spanish and Mandarin was performed. Alexa did not have Mandarin Chinese speaking capability. Responses from both devices were recorded and then transcribed to text using Google Docs "speech to text" tool and assistance from native Mandarin Chinese and Spanish speakers. Transcribed English language responses were pasted into WebFx.com and readabilityformulas.com for readability analysis via Flesch-Kincaid Reading Ease (FRE), Flesch-Kincaid Grade Level Score (FGL), Simple Measure of Gobbledygook Index (SI), and the Dale-Chall Adjusted Grade Level (DCGL). Verbal Questions: 1) I have diabetes- what do I do? 2) I have Type 1 Diabetes Mellitus - What do I do? 3) I have Type 2 Diabetes - What do I do? 4) Does diabetes cause me to have problems seeing? 5) Does diabetes cause kidney problems? 6) Does diabetes cause problems with my feet? Results: Word Counts: English: Google (36.7 words +/- 10.4), Alexa (39.2 words +/- 25.2) Spanish: Google (31.2 words +/- 13.6), Alexa (45.2 words +/- 27.9) Chinese: Google (73.3 words +/- 31.6), Alexa (NA) No significant differences between the number of words for English vs. Chinese responses or English vs. Spanish responses. English Responses Readability: Avg FRE: Google (51.3 +/-12. 0), Alexa (71.1 +/- 9.9) (higher = easier reading) Avg FGL: Google (9.8 grade +/-1.7), Alexa (6.5 grade +/- 0.8) (higher = higher grade level) Avg SI: Google (9.5 +/-1.5), Alexa (6.5 +/-0.9) (higher = harder reading) Avg DCGL: Google (College), Alexa (Grades 9-10) Conclusion: Digital Voice Assistant Devices are helpful to patients who are visually impaired, have poor mobility or need language translation. Under physician guidance, these devices provide diabetes education for our patients and for the lay public.

Design and Implementation of a Real-Time Smart Home Management System Considering Energy Saving

One of the most challenging problems related to the operation of smart microgrids is the optimal home energy management scheme with multiple and conflicting objectives. Moreover, there is a noticeable increase in homes equipped with renewable energy sources (RESs), where the coordination of loads and generation can achieve extra savings and minimize peak loads. In this paper, a solar-powered smart home with optimal energy management is designed in an affordable and secure manner, allowing the owner to control the home from remote and local sites using their smartphones and PCs. The Raspberry Pi 4 B is used as the brain of the proposed smart home automation management system (HAMS). It is used to collect the data from the existing sensors and store them, and then take the decision. The home is monitored using a graphical interface that monitors room temperature, humidity, smoke, and lighting through a set of sensors, as well as PIR sensors to monitor the people movement. This action enables remote control of all home appliances in a safe and emission-free manner. This target is reached using Cayenne, which is an IoT platform, in addition to building some codes related to some appliances and sensors not supported in Cayenne from scratch. Convenience for people with disabilities is considered by using the Amazon Echo Dot (Alexa) to control home appliances and the charging point by voice, implementing the associated code for connecting the Raspberry pi with Alexa from scratch, and simulating the system on LabVIEW. To reach the optimal operation and reduce the operating costs, an optimization framework for the home energy management system (HEMS) is proposed. The operating costs for the day amounted to approximately 16.039 €. There is a decrease in the operating costs by about 23.13%. The consumption decreased after using the smart HAMS by 18.161 kWh. The results of the optimization also show that the least area that can be used to install solar panels to produce the desired energy with the lowest cost is about 118.1039 m2, which is about 23.62% of the total surface area of the home in which the study was conducted. The obtained results prove the effectiveness of the proposed system in terms of automation, security, safety, and low operating costs.

Stakeholder Perspectives on Implementation of Smart Speakers for Aging in Place in Low-Income Senior Housing: A Qualitative Study.

In the current study, a smart speakers-based system (Amazon® Echo Dot device, a safety pendant, and facility tailored functionalities) for supporting aging in place was implemented in one low-income senior housing property. We aimed to explore perceived benefits, concerns, and expectations about this system among three stakeholder groups (older adults, housing staff, and technology developers) to identify facilitators of and barriers to system implementation. A qualitative analysis of individual interviews using inductive coding methods was performed to identify emerging themes. All stakeholder groups perceived smart speakers as valuable for convenience in daily living and safety. Older adults and system developers identified user difficulty with setting up functions they wanted to use, suggesting the need for user-centered technology support. There were differences in priorities for smart speaker use among three stakeholder groups. This work contributes to the identification of factors affecting smart speaker adoption and use among low-income older adults who often have limited technology experience and support. [Journal of Gerontological Nursing, 48(10), 33-39.].

An Optimal Smart Tank Juice-level Monitoring System for Beverage Industries: A Case Study of Raha Beverages Company Limited, Arusha, Tanzania

Poor monitoring of levels in juice tanks is among the challenges that beverage industries face when pumping liquid from one tank to another. This leads to spilling fluids, faulty juice tests, and industrial accidents. To keep track of the liquid level in a tank, various approaches have been used. Existing technologies are costly and not interactive, and the majority do not benefit individuals with physical disabilities when manual monitoring is needed. The purpose of this paper is to present an optimal smart tank juice-level monitoring system that can be used in beverage industries. The system is able to monitor the juice level within a tank and regulate a pump using voice commands via Alexa and the Amazon Echo Dot. The proposed system was tested and validated, with key findings being that the developed prototype prevented overflowing, accidents, and changes in juice flavor during the dilution process. This paper contributes to the body of knowledge for food and beverage industries in that engineers and operators of beverage industries can monitor the level of juice in a tank, as well as enhance communication when pumping juice from one tank to another in real time.

Alexa Based Home Automation

It is an Iot based paper where designed and implemented smart home system. The purpose is behind of that paper that control the switching of home like fan on or off, lights on or off and specially control the all requirements in home. Alexa is used in this and it is work quickly within a second. Alexa is capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audio books, and providing weather, traffic, and other real-time information. Alexa can also control several smart devices using itself as a home automation hub. We will use on this project, the “Echo Dot”, which allows users to activate the device using a wake-word, such as “Alexa” or “Computer”, as in “Star Trek! In the home automation space, Alexa can interact with several different devices like Philips Hue, Belkin WeMo, Sonoff, etc. We emulated WeMo devices on previous projects, using the fauxmo ESP library, which seems outdated and not adequately maintained nowadays. Here we will use mat protocol that which helps for the communication.

“Alexa, You're Really Stupid”: A Longitudinal Field Study on Communication Breakdowns Between Family Members and a Voice Assistant

In this paper, we present the results of our long-term study on use of a voice assistant (Amazon Alexa via Amazon Echo Dot) in nine families with children and no previous experience with this technology. The study was conducted over the course of 5 weeks during which the families could interact with the device freely. Three house visits were made to collect empirical data from the adult participants in form of questionnaires. Additionally, conversational data from log files of the voice assistant were obtained. These data were annotated and analyzed with a focus on communication breakdowns during human-assistant interaction. We investigate user behavior for both adults and children in such situations, its reasons and consequences for user satisfaction. This article provides qualitative analysis of three particularly interesting breakdown cases, as well as statistical analysis along several hypotheses and research questions combining empirical and conversational data. Described cases of communication breakdown illustrate findings from existing literature on the topic. The statistical analysis paints a mixed picture, however, it helped us identify further avenues for research, some of which can be explored with our data set in the future. We found a significant negative effect of the number of abandoned failed requests on user satisfaction, contrary to the number of successfully repaired requests that had no influence on user satisfaction. We discovered that users are more inclined to use reformulation as repair strategy when they do not perceive the emergence of miscommunication as their fault. We could not identify a significant effect of internal reasons for the choice of other strategies, so we suggest that situational clues such as the immediate response of the voice assistant are more important for the choice of repair strategy. Our results also hint that users distinguish between repair strategies differently, as the self-perceived frequency of repetitions and abortions of requests were found to be positive predictors for the use of reformulation-based strategies. With regards to the long-term aspect of the study, use of repetition as a repair strategy by both children and adults significantly decreased with time, no other changes were found for other strategies. Additionally, no significant impact of age on the choice of repair strategy was found, as well as no interaction effect between age and time.

Forensic analysis for AI speaker with display Echo Show [formula omitted] generation as a case study

As the Internet of Things (IoT) era arrives, many Internet-connected devices are being released, and their use is increasing. One of these, the AI speaker, is designed to augment user convenience by using voice recognition. The best-known products are the Amazon Echo family, including Echo and Echo Dot and more recently, Echo Show with display. An AI speaker with display provides diverse functions such as surfing the Internet, taking pictures, making voice or video calls, and controlling smart home devices. To do this, Alexa cloud servers store a variety of configuration values and historical logs, and users can manage their own cloud-native data through interfaces (e.g., Web sites or mobile apps). For this reason, AI speakers with smart display are similar to PCs or smartphones, which can be very profitable from a digital forensic perspective. This paper focuses on detailed research on the second generation of Echo Show. The first step was to collect forensic artifacts stored inside the product by teardown, identifying eMMC flash memory chips and performing chip-off on Echo Show. Alexa app-related artifacts used on smartphones and how to automatically acquire data from the Alexa cloud were also investigated. From three sources including Echo Show, a companion client (smartphone), and the Alexa cloud, it was possible to acquire user credentials, traces of photos, records of watching videos, log files, and Internet histories with timestamp. The second step was to identify the possibility of inferring new information by correlating artifacts collected from different sources. Integrative analysis enables investigators to track suspect activity across digital devices. Third, this paper introduces an updated version of Cloud-based IoT Forensic Toolkit (CIFT) to support digital investigation of Echo Show. Based on the technical findings, this study proposes a digital forensic framework for a smart speaker with a display that can play an important role as a digital witness at a crime scene. Until now, there has been no multilevel approach to acquisition and analysis of Echo Show data in the field of digital forensics. Therefore, this study makes a contribution to the digital forensic community.

Medication Adherence Reminder System for Virtual Home Assistants: Mixed Methods Evaluation Study.

BackgroundMedication nonadherence is a global public health challenge that results in suboptimal health outcomes and increases health care costs. Forgetting to take medicines is one of the most common reasons for unintentional medication nonadherence. Research findings indicate that voice-activated virtual home assistants, such as Amazon Echo and Google Home devices, may be useful in promoting medication adherence.ObjectiveThis study aims to create a medication adherence app (skill), MedBuddy, for Amazon Echo devices and measure the use, usability, and usefulness of this medication-taking reminder skill.MethodsA single-group, mixed methods, cohort feasibility study was conducted with women who took oral contraceptives (N=25). Participants were undergraduate students (age: mean 21.8 years, SD 6.2) at an urban university in the Southeast United States. Participants were given an Amazon Echo Dot with MedBuddy—a new medication reminder skill for Echo devices created by our team—attached to their study account, which they used for 60 days. Participants self-reported their baseline and poststudy medication adherence. MedBuddy use was objectively evaluated by tracking participants’ interactions with MedBuddy through Amazon Alexa. The usability and usefulness of MedBuddy were evaluated through a poststudy interview in which participants responded to both quantitative and qualitative questions.ResultsParticipants’ interactions with MedBuddy, as tracked through Amazon Alexa, only occurred on half of the study days (mean 50.97, SD 29.5). At study end, participants reported missing their medication less in the past 1 and 6 months compared with baseline (χ21=0.9 and χ21=0.4, respectively; McNemar test: P<.001 for both). However, there was no significant difference in participants’ reported adherence to consistently taking medication within the same 2-hour time frame every day in the past 1 or 6 months at the end of the study compared with baseline (χ21=3.5 and χ21=0.4, respectively; McNemar test: P=.63 and P=.07, respectively). Overall feedback about usability was positive, and participants provided constructive feedback about the skill’s features that could be improved. Participants’ evaluation of MedBuddy’s usefulness was overwhelmingly positive—most (15/23, 65%) said that they would continue using MedBuddy as a medication reminder if provided with the opportunity and that they would recommend it to others. MedBuddy features that participants enjoyed were an external prompt separate from their phone, the ability to hear the reminder prompt from a separate room, multiple reminders, and verbal responses to prompts.ConclusionsThe findings of this feasibility study indicate that the MedBuddy medication reminder skill may be useful in promoting medication adherence. However, the skill could benefit from further usability enhancements.

Personalized Activated Technology with Heart Failure Disease Specific Functionality

Background: Optimization of health care delivery in different settings with the goal of improving quality of life and reduction of heart failure (HF) morbidity continues to be an unmet need. Digital engagement between patients and providers is becoming more prevalent for care coordination and management of heart failure.Aims: To design and test a customized and interactive heart failure disease specific functionality with activated technology (Amazon's Echo Dot or an Avatar) as a tool for management of HF patients at home. To identify the effect of a voice activated technology in the reduction of heart failure hospitalizations, improvement of medication adherence, improvement in health-related quality of life and increase in comfort with voice activated technologies. Methods We compared two types of conversational agent technologies to standard of care, using speech recognition, to evaluate patient engagement in HF patients. The first technologyutilized the Amazon Echo Dot Alexa technology and only communicated with the patient through audio and the second was an application on a tablet using an Avatar, combining audio and visual. The conversational agent asks the HF patients a series of 11 questions, divided into three components: compliance (questions 1-3), mild HF symptoms (questions 4-6) and moderate/severe HF symptoms (questions 7-11). Participants completed questions regarding their use and comfort with technology. Inclusion criteria: admitted or treated for HF;18 or older; and resides with access to wifi. Exclusion criteria: heart transplant or ventricular assist device recipients. Participants were monitored for three months.Results: Thirty patients were enrolled in each intervention arm of the two studies with the Alexa or Avatar. Mean age was 54 years in the Alexa group and 56 years in the Avatar group. There were predominately males 61% and 63%, Alexa and Avatar, respectively. In the Alexa and Avatar groups race was predominately black, 64% and 63% followed by white 21% and 30%. The Alexa group was statistically more likely to complete the three compliance factors of weight checking (p=.030), salt monitoring (p=0.016) and medication adherence (p=0.005). Initial results suggest that older people and individuals using the smart phone answer the questionnaire at higher rates, and individuals taking an increased number of medications to treat HF had lower participation.Conclusion: Voice enabled, internet connected devices are poised to have profound impacts on the quality of life for in-home, independent, and assisted-living patients by vastly increasing the connectivity to healthcare providers.

Mainstream Technology as Basic Support for Individuals with Extensive Neuro-Motor Impairments and Absence of Verbal Skills

The present study was aimed at assessing a simplified technology-aided program to allow eight participants with extensive neuro-motor impairments and absence of verbal skills to access preferred leisure events and call the caregiver independently. The technology included mainstream commercial components such as an Echo Dot© with Amazon Alexa©, a Samsung smartphone, and two voice recording devices. Each voice recording device served to record and produce a verbal message, that is, “Alexa music” and “Alexa call the caregiver (NAME)” during the intervention phase of the study. The devices were displayed in front of the participants and could be activated by a simple hand-pressure response. Activation of the devices led the Alexa application of the Echo Dot to deliver a song and call the caregiver, respectively. During the baseline sessions (without the support of the voice recording devices), the participants failed to access songs or to call the caregiver. During the intervention phase (with the support of the voice recording devices), all participants managed to access songs and to call the caregiver independently. The cumulative mean frequency of songs accessed and calls made per 10-min session varied between slightly above 4 and nearly 6. The mean frequency of songs accessed tended to be predominant for all participants. The new technology-aided program may be a useful resource for supporting individuals like the participants of this study.

Real time Implementation of Home appliance control using ALEXA

In recent years, Internet of Things (IOT) is being playing a major role in automation of Industries. Smart home systems are being employed in buildings to improve energy efficiency and reduce power wastage. Voice recognition is the current trend in automation. Regular methods of switching systems are difficult for paraplegic persons and blind persons since blind people can’t use the SMS method of automation. So voice recognition methods are preferred. Voice recognition systems involve calibration and programming to accept the user commands and operate the appliance. Amazon Echo dot, a smart speaker has an internally programmed Artificial Intelligence known as Alexa. It offers dual interaction and can be used to connect to Smart devices. To remedy the high cost of smart devices, we register a name in Alexa server as a smart device which comprises a Wi-Fi module and a relay. The Smart speaker and the Wi-Fi module form a single way communication which helps in controlling of appliance.