Touchscreen Devices Research Articles

Bio-Touchpass: Efficient Password Mechanism to Overcome Spyware Attacks

Abstract: This is a work exaggerated historical authentication structures supported the personal identification number (PIN) and one time password (OTP) through the fusion of biometric statistics as a second stage of person authentication. In this project, users can draw every digit of the parole on the touch screen device instead of usual method. A whole exploration of this bio metric gadgets is to drop a relative discriminative energy of every written notation and consequently the hardness once growing the size of the parole and consequently the variety of approached samples. The advanced eBioDigit data, that includes on line written notation from zero to nine, has been non heritable manipulation of the finger as enter on an android device. The data is employed within the experiments in accordance’s in the course of this work and it is on the market at the side of benchmark. Eventually, we have a tendency to discuss precise important points for the education of our innovation strategy on the present PIN and OTP systems, reaching outcomes with equal error rates (EERs) ca. 4.0% once the assaulter is aware of the parole. These outcomes inspire the practice of our innovation strategy in contract to historical PIN and OTP systems anywhere the assault would have a hundred percent success amount underneath steady faux situation. Keyword: Mobile User Authentication; Password; Biometrics; Handwriting; PIN; OTP; Touchscreen; Touch Interaction

A study of deep single sketch-based modeling: View/style invariance, sparsity and latent space disentanglement

Deep image-based modeling has received a lot of attention in recent years. Sketch-based modeling in particular has gained popularity given the ubiquitous nature of touchscreen devices. In this paper, we (i) study and compare diverse single-image reconstruction methods on sketch input, comparing the different 3D shape representations: multi-view, voxel- and point-cloud-based, mesh-based and implicit ones; and (ii) analyze the main challenges and requirements of sketch-based modeling systems. We introduce the regression loss and provide two variants of its formulation for the two most promising 3D shape representations: point clouds and signed distance functions. We show that this loss can increase general reconstruction accuracy, and the view- and style-robustness of the reconstruction methods. Moreover, we demonstrate that this loss can benefit the disentanglement of latent space to view-invariant and view-specific information, resulting in further improved performance. To address the figure-ground ambiguity typical for sparse freehand sketches, we propose a two-branch architecture that exploits sparse user labeling. We hope that our work will inform future research on sketch-based modeling.

Inclusion of information technology-based assessments of health-related quality of life in routine oncology practice in Uruguay

BackgroundPrevious research has shown that the inclusion of patient-reported outcomes measures in the patient’s visit to the oncologists might improve the quality of global health care. The aim of the study was to assess the feasibility, acceptance, and utility perceived by patients and oncologists of health-related quality of life (HRQL) assessments obtained prior to clinical visits, and to evaluate if this has an impact on patient’s well-being in a sample of Spanish-speaking patients from Uruguay.MethodsPatients assisted regularly in the Oncology Clinic were randomized into two groups: an intervention group that completed a set of questionnaires (European Organisation for Research and Treatment of Cancer-Quality of Life Questionnaire-C30 (EORTC QLQ-C30) and Hospital Anxiety and Depression Scale using a touch screen device and a control group that did not respond to these questionnaires. At 2 months, the responses of all the participants to the Functional Assessment of Cancer Therapy-General (FACT-G) were collected over a telephone to determine whether there were differences in the HRQL between the intervention and control groups. The graphed scores of the intervention group were included in the clinical history of the patient during consultation. Patients and physicians completed the questionnaires on the usefulness of these measurements.ResultsIn total, 58 patients participated in this study: 36 in the intervention group and 22 in the control group; 65% of the participants were female, and median age was 59 years (18–79). Regarding patients, 97% found the questionnaires easy to complete and thought that they included important questions. As for oncologists, 68.8% used the information and 87.5% found it useful for the consultation. There were no significant differences in the FACT-G scores between the intervention and control groups.ConclusionsThe routine HRQL assessments using an electronic device prior to the consultations were positively valued by almost all patients and physicians. This could significantly contribute to a better understanding of the patient's overall problems during consultation. These results confirm the benefits of integrating the patient’s self-reported quality of life outcomes into consultations.

Development and content validity testing of patient-reported outcome (PRO) items to assess chest congestion associated with the common cold for use in children and adolescents

BackgroundThis article describes qualitative interviews conducted with children (aged 6–11), adolescents (aged 12–17), and adults with the common cold as well as parents/caregivers of the 6–8-year-old children. The aim was to support the refinement and content validity testing of patient-reported outcome (PRO) items assessing chest congestion that could be used as pediatric clinical trial endpoints. Feasibility and acceptability of administering the PRO items electronically on a hand-held touch-screen device were also evaluated. The sample included children aged 6–8 years (n = 14), 9–11 years (n = 13), adolescents aged 12–17 years (n = 12), and adults (n = 10), all of who had current (n = 38) or recent (n = 11) cold. Both concept elicitation (CE) and cognitive debriefing (CD) interviews were conducted with all of these participants, conducted over in two rounds. Ten parents/caregivers of participants aged 6–8 years were also interviewed (separately from their child) regarding how they thought their children would understand the items. The CE interviews explored the qualitative experience of having chest congestion and related symptoms of the common cold. Following their CE interview, participants completed draft items on an electronic patient-reported outcome (ePRO) device twice daily for 2–5 days prior to their CD interview. During the CD interview participants were asked about relevance, understanding and interpretation of the draft PRO items. Qualitative analysis of the interview data and descriptive analyses of the ePRO data were conducted following both rounds of interviews, with modifications to the items implemented following Round 1 and tested in Round 2.ResultsEight symptoms were reported by children during concept elicitation. Findings from the child, adolescent, and adult/parent interviews supported revisions to the items and enabled the selection of the best performing items. The results provided evidence that the final items were well understood by participants and relevant to their experiences of chest congestion as part of a common cold. Findings also provide support for using the same items across age groups.ConclusionsThe results of the CE and CD interviews provide evidence supporting the content validity of new PRO items assessing the experience of chest congestion symptoms associated with common cold experienced by children, adolescents, and adults.

Touchscreen device size suitable for non-visual icon search by blind users

Blind smartphone users are on the rise and they need to know which device is easiest for them to use. To explore the optimal touchscreen size for devices used by the blind, we conducted an experiment in which 15 blind participants searched for target icons with four touchscreen devices ranging in size from 4 to 7.9 inches. We analyzed the search time, search strategies, and subjective evaluations and found that the 4.7-inch screen device produced the shortest search time and obtained the highest subjective rating among the four devices. These findings are consistent with those in a previous study with sighted participants. One interesting difference is that the blind participants in this study found the icons about 20% to 40% faster than the sighted participants. Another important finding is that the search strategy had a larger effect than the screen size on the search time. These findings are useful for blind people and their supporters at the stage of obtaining new smartphones and learning how to use them.

The impact of touchscreen devices on consumers’ choice confidence and purchase likelihood

The impact of touchscreen devices on consumers’ choice confidence and purchase likelihood

Design and Application of Interactive Algorithm for Advertising Media Screen Based on Smart Sensor

Touch is one of the most important human senses. With the popularization of touch-screen mobile phones, tablet computers, and other devices, touch-screen interactive technology has become a norm in people’s daily lives, and advertisements that were once dominated by vision and hearing have added an interactive experience in the dimension of touch. Traditional advertising media screens can only complete simple information dissemination functions and cannot interact with users in a two-way manner. They can only receive information one-way and passively and lack interactivity. Touch-screen interactive advertising forms a good interaction with the target audience, thereby disseminating advertising information to achieve the purpose of promotion or brand image building. This paper designs a set of advertising media screen interaction systems based on smart sensors, including a gesture interaction module, a remote interaction module, and a touch interaction module. The gesture interaction module can recognize 5 static gestures and send gesture commands to control the advertising media screen. The remote interaction module can remotely control the advertising media screen, and the touch interaction module can control the advertising media screen through the touch screen. According to the functional requirements, the overall design of software and hardware is given, and the technical background of each module of the software is introduced. Next, the depth image-based gesture recognition method is studied. The number of fingers and the center distance feature are fused as feature vectors, and the weighted template matching method is used to classify and recognize gestures. Finally, the design and implementation of the interactive system are introduced.

A hierarchical residual network with compact triplet-center loss for sketch recognition

With the widespread use of touch-screen devices, it is more and more convenient for people to draw sketches on screen. This results in the demand for automatically understanding the sketches. Thus, the sketch recognition task becomes more significant than before. To accomplish this task, it is necessary to solve the critical issue of improving the distinction of the sketch features. To this end, we have made efforts in three aspects. First, a novel multi-scale residual block is designed. Compared with the conventional basic residual block, it can better perceive multi-scale information and reduce the number of parameters during training. Second, a hierarchical residual structure is built by stacking multi-scale residual blocks in a specific way. In contrast with the single-level residual structure, the learned features from this structure are more sufficient. Last but not least, the compact triplet-center loss is proposed specifically for the sketch recognition task. It can solve the problem that the triplet-center loss does not fully consider too large intra-class space and too small inter-class space in sketch field. By studying the above modules, a hierarchical residual network as a whole is proposed for sketch recognition and evaluated on Tu-Berlin benchmark thoroughly. The experimental results show that the proposed network outperforms most of baseline methods and it is excellent among non-sequential models at present.

Characterization of English Braille Patterns Using Automated Tools and RICA Based Feature Extraction Methods.

Braille is used as a mode of communication all over the world. Technological advancements are transforming the way Braille is read and written. This study developed an English Braille pattern identification system using robust machine learning techniques using the English Braille Grade-1 dataset. English Braille Grade-1 dataset was collected using a touchscreen device from visually impaired students of the National Special Education School Muzaffarabad. For better visualization, the dataset was divided into two classes as class 1 (1–13) (a–m) and class 2 (14–26) (n–z) using 26 Braille English characters. A position-free braille text entry method was used to generate synthetic data. N = 2512 cases were included in the final dataset. Support Vector Machine (SVM), Decision Trees (DT) and K-Nearest Neighbor (KNN) with Reconstruction Independent Component Analysis (RICA) and PCA-based feature extraction methods were used for Braille to English character recognition. Compared to PCA, Random Forest (RF) algorithm and Sequential methods, better results were achieved using the RICA-based feature extraction method. The evaluation metrics used were the True Positive Rate (TPR), True Negative Rate (TNR), Positive Predictive Value (PPV), Negative Predictive Value (NPV), False Positive Rate (FPR), Total Accuracy, Area Under the Receiver Operating Curve (AUC) and F1-Score. A statistical test was also performed to justify the significance of the results.

Effect of Borophene and Graphene on the Elastic Modulus of PEDOT:PSS Film—A Finite Element Study

A finite element method (FEM) was employed to investigate the interaction of borophene nanoplatelets (BNPs) and graphene nanoplatelets (GNPs) on the mechanical properties of Poly(3,4-ethylene dioxythiophene):poly(styrene sulfonate) PEDOT:PSS film. A 3D random distribution of the inclusion into the PEDOT:PSS matrix was constructed by developing a 145 × 145 × 145 representative volume element (RVE) with a 4% volume fraction of BNPs and GNPs. In comparison to the pristine PEDOT:PSS, the calculated effective elastic moduli of the BNP-PEDOT:PSS and GNP-PEDOT:PSS nanocomposites exhibited 9.6% and 10.2% improvement, respectively. The predicted FE results were validated by calculating the elastic moduli of the nanocomposites using a modified Halpine-Tsai (H-T) model. The reinforcing effect of the inclusion into the PEDOT:PSS film offers a promising electrode with improved mechanical stability. Consequently, this intriguing result makes the BNP/PEDOT:PSS nanocomposite highly promising for further investigation and application in cutting-edge devices such as touchscreen, thermoelectric, light-emitting diode, electrochemical, photodiode, sensor, solar cell, and electrostatic devices.

Concept App Development for National Library of Virtual Manipulatives (NLVM): An Initiative

An NSF-supported project, the National Library of Virtual Manipulatives (NLVM), was started in 1999 to develop a library of entirely interactive and web-based virtual manipulatives or concept tutorials. It was mainly in Java applets for mathematics instruction (K-12 emphasis). The NLVM was a resource to develop mathematics classrooms from which teachers could independently draw upon. The materials were also significant in the mathematical training of both in-service and pre-service instructors. However, twenty years later, with the proliferation of touch-screen devices, this fantastic resource is in a crisis of becoming lost to future generations. It has been seen that many computers no longer run Java. Besides, the older version of Java used in the initial advancement of the NLVM is no longer supported. Hence, it is the right time to rethink the NLVM for current, widely available touch screen devices. This is essential to bolster the offerings for both prekindergarten, K- 12, and university settings. Keeping the situation in mind, we have constructed four virtual manipulative applications compatible with touch-screen devices based on the outdated java-script types of virtual manipulatives available through the NLVM. The four concept apps are being conducted with children in classroom settings starting in Spring 2021. It will allow for further revisions of the apps if necessary. Then, the apps from this project would be combined as part of a more significant, federally, or privately funded external grant proposal to re-imagine and broaden upon all the virtual manipulatives currently found in the NLVM.

Sketch-Based Image Retrieval Using Novel Edge Detector and Feature Descriptor

With the explosive increase of digital images, intelligent information retrieval systems have become an indispensable tool to facilitate users’ information seeking process. Although various kinds of techniques like keyword-/content-based methods have been extensively investigated, how to effectively retrieve relevant images from a large-scale database remains a very challenging task. Recently, with the wide availability of touch screen devices and their associated human-computer interaction technology, sketch-based image retrieval (SBIR) methods have attracted more and more attention. In contrast to keyword-based methods, SBIR allows users to flexibly manifest their information needs into sketches by drawing abstract outlines of an object/scene. Despite its ease and intuitiveness, it is still a nontrivial task to accurately extract and interpret the semantic information from sketches, largely because of the diverse drawing styles of different users. As a consequence, the performance of existing SBIR systems is still far from being satisfactory. In this paper, we introduce a novel sketch image edge feature extraction algorithm to tackle the challenges. Firstly, we propose a Gaussian blur-based multiscale edge extraction (GBME) algorithm to capture more comprehensive and detailed features by continuously superimposing the edge filtering results after Gaussian blur processing. Secondly, we devise a hybrid barycentric feature descriptor (RSB-HOG) that extracts HOG features by randomly sampling points on the edges of a sketch. In addition, we integrate the directional distribution of the barycenters of all sampling points into the feature descriptor and thus improve its representational capability in capturing the semantic information of contours. To examine the efficiency of our method, we carry out extensive experiments on the public Flickr15K dataset. The experimental results indicate that the proposed method is superior to existing peer SBIR systems in terms of retrieval accuracy.

Validating an abnormal situation prediction model for smart manufacturing in the oil refining industry

Human beings play an important role in a smart manufacturing economy. The repetitive and cognitive demanding task operations of smart manufacturing require the development of system models for measuring and predicting human performance, including oil refinery monitoring tasks. The main objective of this research was to validate the generalizability of a mathematical model for the prediction of refinery operators' detection of abnormal events. Moreover, we examined operators' visual behaviors in response to abnormal situations at different ages and with different task loads, task complexities, and input devices. We found that participants had lower mean fixation durations, total fixation numbers, and fixation/saccade ratios when they were in the condition of a touchscreen device. Moreover, we found that older adults had higher mean saccade durations and saccade amplitudes when they were in the condition of a touchscreen device. Finally, the statistical model borrowed from our prior paper was found to be generalizable to different task loads and age groups for the prediction of operators’ detection of abnormal events. Our results showed that visual behaviors can indicate specific internal states of participants, including their cognitive workload, attention, and situation awareness in a real-time manner. The findings provide additional support for the value of using visual behavior to predict responsiveness of oil refinery operators and for future applications of smart manufacturing monitoring systems.

Touch Screen Device Usage and its Effect on Sleep among Young Children: Do Parents Delude Younger Generation Deliberately? Time to Ruminate and Impede this Technological Harm.

The American Academy of Pediatrics had recommended that children younger than 2 years should avoid digital media. However, in the Indian context, it was less explored. The study aimed to determine the relationship between touch screen device (TSD) usage and sleep of infants, toddlers. Using a cross-sectional design, 76 parents of infants and toddlers attending well-baby clinic of a tertiary care hospital in Eastern India were approached, and 57 parents who fulfilled the eligibility criteria were selected using a nonprobability convenient sampling technique. Data were collected using a pretested validated tool including TSD usage pattern, the brief infant sleep questionnaire. Descriptive statistics and Pearson correlation were used to analyze the data. The mean age of TSD usage was 15.6 ± 5.8 months with a mean duration of 65 ± 9.6 min/day. Mothers (51%) reported circumstances to offer TSD was feeding the child. There was a significant negative relationship between night sleep and TSD usage among children (r = -0.35, P < 0.01). TSD usage time significantly affects the nighttime duration of sleep of infants and toddlers. Parents to be educated on proper usage and ill effects of TSD to prevent sleep issues.

Map My Fitness App

Health is a state of complete physical, mental and social will and not merely the absence of disease or infirmity” (WHO, 1946). According to this World Health Organization definition, health means general well-being. The increasing prevalence of health problems resulting from modern lifestyles and limited medical resources has led several stakeholders to seek alternative medical methods. Physical activity has many positive health effects, but people often lack the intrinsic motivation to do so. The project is developed in Android Studios. Android has its own virtual machine "Dalvik Virtual Machine" which allows this Android application to run on any device, making it an open source application We used XML (Extensible Markup Language) for the user interface and Java programming for the logic. Android, an operating system developed for small devices such as mobile phones, has rapidly gained market share in dozens of smartphones and is now also used in devices such as tablets and TVs. This is a mobile operating system that uses a modified version of the Linux kernel. Android is developed by Google as part of the Open Handset Alliance. The Open Handset Alliance is a collective of over 30 mobile and technology companies committed to opening up the mobile phone atmosphere. Direct Manipulation relies mostly on his UI on Android, with touch gestures that loosely resemble real actions such as taps, swipes, and pinches for manipulating objects on the screen, and virtual touch gestures for text input use the keyboard. Overall, touchscreen devices, later Android TV for televisions and Android Auto for cars were developed by Google.

A Comparative Evaluation of Mechanical Vibration and Ultrasonic Vibration on Smartphones in Tactile Code Perception

To improve touchscreen accessibility, it has been proved efficient to integrate tactile feedback into touchscreen devices. Two typical techniques can be used to offer tactile feedback on smartphones, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mechanical vibration</i> (MV) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ultrasonic vibration</i> (UV). However, (a) whether MV and UV are equally perceived and evaluated on usability is unknown, and if not, (b) which technique/feedback provides better usability and satisfaction. In this study, a comparative user study was conducted to evaluate user performance on tactile codes perception generated by MV and UV techniques. 10 tactile codes were designed using the two techniques, and 16 sighted and 5 visually impaired people were invited to take part in a tactile code perception experiment. Tactile codes perception accuracy, response time, and satisfaction on the MV and UV were recorded during the experiment. The experimental results show that the user perception performance of MV was significantly better than UV for both sighted and visually impaired participants. Participants obtained an accuracy approximately 3% higher, with the response time at least 3 s faster, and user satisfaction significantly higher (6.1 vs. 4.6 on a 7-point Likert rating scale) when using MV. Both sighted and visually impaired participants assessed MV with a higher preference over UV. Our results suggest that MV fits better to applications that require precise tactile code perception, for which UV may not be as suitable due to its lower recognition efficiency.

SHORT-TERM IMPACTS OF MOBILE TOUCH-SCREEN DEVICE USE ON MUSCULOSKELETAL DISORDERS DURING COVID-19 PANDEMIC: RISK ASSESSMENT MODELLING AND VERIFICATION

The intensive and repetitive use of touch-screens may pose significant problems, such as ergonomic pain or musculoskeletal disorders. This research aims to study the effect of using mobile touch-screen devices on the human musculoskeletal system during the COVID-19 pandemic lockdown and to develop a model for classifying the effects of musculoskeletal stress (pain and discomfort) on the performance of educational activities. The Cornell musculoskeletal discomfort questionnaire was given to 544 participants (71% males and 29% females). An Association Rule Mining approach was applied to illustrate the correlation, and multiple machine-learning models – used to predict the impact of pain and discomfort on different body regions – were applied to determine risk levels that might interfere with the ability to perform daily activities. Most musculoskeletal disorders were reported in the neck region and lower back (64.33% and 55.33% respectively), followed by upper back (44.30%) and the right shoulder (38%). Analysis of association rules showed high positive correlation between the lower back and the neck (support = 43%, confidence = 77%). Additionally, it was found that the radial basis function network has the highest accuracy in prediction (84%). The results of the radial basis function model showed that interference in educational activities can be predicted by using pain indicators in body parts resulting from touch-screen device usage. Keywords association rule mining approach; machine learning; educational activities; mobile touch screen; musculoskeletal disorders

Trsper: a web-based application for Archimedes spiral analysis.

We built a web-based application of the Archimedes spiral exam that implements clinically validated spiral metrics and tested drawing instructions to define a clinical workflow. We designed an HTML5 and Javascript implementation of the spiral exam to run on mobile touchscreen devices. We then recruited 10 volunteers each for 2 experiments designed to validate the programmed spiral metrics and assess how instructions or drawing implement affect the results. In task one, volunteers drew 5 spirals each while following 6 different instruction sets (n=30 spirals each, n=300 spirals total) that varied by support of the drawing hand and tracing condition (either tracing a spiral template, drawing in-between it, or freehand). In task two, volunteers drew 5 spirals each while following 2 instruction sets and drawing using a stylus or their dominant index finger (n=20 spirals each, n=200 spirals total). Principal components analysis of calculated metrics revealed that the experiments grouped by instruction set and by subject. Mean Euclidean distance between experiments represented as 11-dimensional vectors revealed that consistency varied among instruction tasks and that drawing with a stylus produced more consistent results than did using the dominant index finger. Using experimental data and simulated abnormal spirals, we designed a decision support system that accurately identifies potentially abnormal spirals. We built and validated a robust digital implementation of the Archimedes spiral exam and recommend a sensitive and specific workflow on the basis of data gathered from healthy volunteers.

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.

Using Voice and Touchscreen Technology to Protect Vulnerable Clients in Residential Care Facilities

Due to the COVID-19 pandemic, the Centers for Medicare and Medicaid Services (CMS) issued a memorandum for long term care facilities to restrict visitation by all nonessential personnel, including guardianship case managers. To enhance caseworker access to their guardianship clients, an eastern seaboard state agency distributed Amazon Echo Show devices to clients in long term care facilities. These touchscreen and voice-activated devices have both video and audio capabilities. This study reports the results of the first phase of a comprehensive evaluation, pilot testing the devices via a group of “superuser” case managers to understand the potential challenges and benefits of using these devices. Sixteen case managers participated in two virtual focus groups before and after the installation of an Echo device with one of their guardianship clients. Participants were asked to discuss experiences in accessing clients and client engagement before and after device installation. The focus groups were audio-recorded and transcribed verbatim and two researchers independently identified themes using open and axial coding. Major themes identified included: challenges to device installation and use, strategies to overcome challenges, benefits to using Echo devices, and ethical concerns. These findings suggest that touchscreen or voice-activated devices with video capability can assist case managers in protecting vulnerable clients and ensuring their well-being when in-person access is restricted. Additionally, the devices can be used to enable isolated residents to connect to the outside world, including family, friends, and case managers through technology. Strategies for replication of this innovative program will be discussed.