Expert Users Research Articles

Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model.

Dynamic magnetic resonance imaging has emerged as a powerful modality for investigating upper-airway function during speech production. Analyzing the changes in the vocal tract airspace, including the position of soft-tissue articulators (e.g., the tongue and velum), enhances our understanding of speech production. The advent of various fast speech MRI protocols based on sparse sampling and constrained reconstruction has led to the creation of dynamic speech MRI datasets on the order of 80-100 image frames/second. In this paper, we propose a stacked transfer learning U-NET model to segment the deforming vocal tract in 2D mid-sagittal slices of dynamic speech MRI. Our approach leverages (a) low- and mid-level features and (b) high-level features. The low- and mid-level features are derived from models pre-trained on labeled open-source brain tumor MR and lung CT datasets, and an in-house airway labeled dataset. The high-level features are derived from labeled protocol-specific MR images. The applicability of our approach to segmenting dynamic datasets is demonstrated in data acquired from three fast speech MRI protocols: Protocol 1: 3 T-based radial acquisition scheme coupled with a non-linear temporal regularizer, where speakers were producing French speech tokens; Protocol 2: 1.5 T-based uniform density spiral acquisition scheme coupled with a temporal finite difference (FD) sparsity regularization, where speakers were producing fluent speech tokens in English, and Protocol 3: 3 T-based variable density spiral acquisition scheme coupled with manifold regularization, where speakers were producing various speech tokens from the International Phonetic Alphabetic (IPA). Segments from our approach were compared to those from an expert human user (a vocologist), and the conventional U-NET model without transfer learning. Segmentations from a second expert human user (a radiologist) were used as ground truth. Evaluations were performed using the quantitative DICE similarity metric, the Hausdorff distance metric, and segmentation count metric. This approach was successfully adapted to different speech MRI protocols with only a handful of protocol-specific images (e.g., of the order of 20 images), and provided accurate segmentations similar to those of an expert human.

An autonomous X-ray image acquisition and interpretation system for assisting percutaneous pelvic fracture fixation.

Percutaneous fracture fixation involves multiple X-ray acquisitions to determine adequate tool trajectories in bony anatomy. In order to reduce time spent adjusting the X-ray imager's gantry, avoid excess acquisitions, and anticipate inadequate trajectories before penetrating bone, we propose an autonomous system for intra-operative feedback that combines robotic X-ray imaging and machine learning for automated image acquisition and interpretation, respectively. Our approach reconstructs an appropriate trajectory in a two-image sequence, where the optimal second viewpoint is determined based on analysis of the first image. A deep neural network is responsible for detecting the tool and corridor, here a K-wire and the superior pubic ramus, respectively, in these radiographs. The reconstructed corridor and K-wire pose are compared to determine likelihood of cortical breach, and both are visualized for the clinician in a mixed reality environment that is spatially registered to the patient and delivered by an optical see-through head-mounted display. We assess the upper bounds on system performance through in silico evaluation across 11 CTs with fractures present, in which the corridor and K-wire are adequately reconstructed. In post hoc analysis of radiographs across 3 cadaveric specimens, our system determines the appropriate trajectory to within 2.8 ± 1.3mm and 2.7 ± 1.8[Formula: see text]. An expert user study with an anthropomorphic phantom demonstrates how our autonomous, integrated system requires fewer images and lower movement to guide and confirm adequate placement compared to current clinical practice. Code and data are available.

In-vial dried urine spot collection and processing for quantitative analyses.

Analysis of dried urine spots (DUSs) is becoming an emerging technique in clinical, toxicological, and forensic chemistry due to the fully non-invasive collection, facile transportation, and simple storage of DUS samples. Correct DUS collection and elution is of the utmost importance because inadequate DUS sampling/processing may have direct consequences on quantitative DUS analyses and these aspects were, for the first time, comprehensively investigated in this contribution. Various groups of endogenous and exogenous species were selected as model analytes and their concentrations were monitored in DUSs collected on standard cellulose-based sampling cards. Strong chromatographic effects were observed for most analytes having a crucial impact on their distribution within the DUSs during sampling. Concentrations of target analytes were up to 3.75-fold higher in the central DUS sub-punch in comparison to the liquid urine. Consequently, substantially reduced concentrations of these analytes were determined in peripheral DUS sub-punches demonstrating that sub-punching, often applied to dried material spots, is not acceptable for quantitative DUS analyses. Hence, a simple, rapid, and user-friendly procedure was suggested, which employed an in-vial collection of a known urine volume on a pre-punched sampling disc (using a low-cost micropipette designed for patient-centric clinical sampling) and in-vial processing of the whole DUS. Excellent accuracy (0.20%) and precision (0.89%) of liquid transfers were achieved by the micropipette, which was also applied to remote DUS collection by laic and expert users. The resulting DUS eluates were analysed by capillary electrophoresis (CE) for the determination of endogenous urine species. The CE results demonstrated no significant differences between the two user groups, elution efficiencies of 88-100% (in comparison to the liquid urine), and precision better than 5.5%.

Simulating analogue film damage to analyse and improve artefact restoration on high‐resolution scans

AbstractDigital scans of analogue photographic film typically contain artefacts such as dust and scratches. Automated removal of these is an important part of preservation and dissemination of photographs of historical and cultural importance. While state‐of‐the‐art deep learning models have shown impressive results in general image inpainting and denoising, film artefact removal is an understudied problem. It has particularly challenging requirements, due to the complex nature of analogue damage, the high resolution of film scans, and potential ambiguities in the restoration. There are no publicly available high‐quality datasets of real‐world analogue film damage for training and evaluation, making quantitative studies impossible. We address the lack of ground‐truth data for evaluation by collecting a dataset of 4K damaged analogue film scans paired with manually‐restored versions produced by a human expert, allowing quantitative evaluation of restoration performance. We have made the dataset available at https://doi.org/10.6084/m9.figshare.21803304. We construct a larger synthetic dataset of damaged images with paired clean versions using a statistical model of artefact shape and occurrence learnt from real, heavily‐damaged images. We carefully validate the realism of the simulated damage via a human perceptual study, showing that even expert users find our synthetic damage indistinguishable from real. In addition, we demonstrate that training with our synthetically damaged dataset leads to improved artefact segmentation performance when compared to previously proposed synthetic analogue damage overlays. The synthetically damaged dataset can be found at https://doi.org/10.6084/m9.figshare.21815844, and the annotated authentic artefacts along with the resulting statistical damage model at https://github.com/daniela997/FilmDamageSimulator. Finally, we use these datasets to train and analyse the performance of eight state‐of‐the‐art image restoration methods on high‐resolution scans. We compare both methods which directly perform the restoration task on scans with artefacts, and methods which require a damage mask to be provided for the inpainting of artefacts. We modify the methods to process the inputs in a patch‐wise fashion to operate on original high resolution film scans.

Study Circle Connect 4U

Abstract: Knowledge is the key resource for most modern organisations. Without a platform to capture and share that knowledge, it’s putting your most valuable asset into the hands of luck. Sometimes, it is possible to meet eminent people online whose interest allign with ours. This system brings people having same interest together by breaking any geographical or social barriers. This Web Application will work as a bridge between people from different areas having same interest and will help to share their wisdom with each other. This Web Application will provide Easy to use User Interface which will bring not only the expert users to the platform but also the beginner level user. This application also tries to keep distraction as low as possible to maintain a closed ecosystem, Chatting, youtube &mock test is a method of using technology to bring people and ideas together despite of the geographical barriers. This system will help people with same interest to express their thoughts and ideas. The system is a project to connect people via all these things while keeping the distraction to the minimal.and if people want to do some relaxtion they can listen music on this web application

The HRA Organ Gallery affords immersive superpowers for building and exploring the Human Reference Atlas with virtual reality.

The Human Reference Atlas (HRA, https://humanatlas.io) funded by the NIH Human Biomolecular Atlas Program (HuBMAP, https://commonfund.nih.gov/hubmap) and other projects engages 17 international consortia to create a spatial reference of the healthy adult human body at single-cell resolution. The specimen, biological structure, and spatial data that define the HRA are disparate in nature and benefit from a visually explicit method of data integration. Virtual reality (VR) offers unique means to enable users to explore complex data structures in a three-dimensional (3D) immersive environment. On a 2D desktop application, the 3D spatiality and real-world size of the 3D reference organs of the atlas is hard to understand. If viewed in VR, the spatiality of the organs and tissue blocks mapped to the HRA can be explored in their true size and in a way that goes beyond traditional 2D user interfaces. Added 2D and 3D visualizations can then provide data-rich context. In this paper, we present the HRA Organ Gallery, a VR application to explore the atlas in an integrated VR environment. Presently, the HRA Organ Gallery features 55 3D reference organs, 1,203 mapped tissue blocks from 292 demographically diverse donors and 15 providers that link to 6,000+ datasets; it also features prototype visualizations of cell type distributions and 3D protein structures. We outline our plans to support two biological use cases: on-ramping novice and expert users to HuBMAP data available via the Data Portal (https://portal.hubmapconsortium.org), and quality assurance/quality control (QA/QC) for HRA data providers. Code and onboarding materials are available at https://github.com/cns-iu/hra-organ-gallery-in-vr.

Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias.

Personalized, image-based computational heart modelling is a powerful technology that can be used to improve patient-specific arrhythmia risk stratification and ventricular tachycardia (VT) ablation targeting. However, most state-of-the-art methods still require manual interactions by expert users. The goal of this study is to evaluate the feasibility of an automated, deep learning-based workflow for reconstructing personalized computational electrophysiological heart models to guide patient-specific treatment of VT. Contrast-enhanced computed tomography (CE-CT) images with expert ventricular myocardium segmentations were acquired from 111 patients across five cohorts from three different institutions. A deep convolutional neural network (CNN) for segmenting left ventricular myocardium from CE-CT was developed, trained and evaluated. From both CNN-based and expert segmentations in a subset of patients, personalized electrophysiological heart models were reconstructed and rapid pacing was used to induce VTs. CNN-based and expert segmentations were more concordant in the middle myocardium than in the heart's base or apex. Wavefront propagation during pacing was similar between CNN-based and original heart models. Between most sets of heart models, VT inducibility was the same, the number of induced VTs was strongly correlated, and VT circuits co-localized. Our results demonstrate that personalized computational heart models reconstructed from deep learning-based segmentations even with a small training set size can predict similar VT inducibility and circuit locations as those from expertly-derived heart models. Hence, a user-independent, automated framework for simulating arrhythmias in personalized heart models could feasibly be used in clinical settings to aid VT risk stratification and guide VT ablation therapy. KEY POINTS: Personalized electrophysiological heart modelling can aid in patient-specific ventricular tachycardia (VT) risk stratification and VT ablation targeting. Current state-of-the-art, image-based heart models for VT prediction require expert-dependent, manual interactions that may not be accessible across clinical settings. In this study, we develop an automated, deep learning-based workflow for reconstructing personalized heart models capable of simulating arrhythmias and compare its predictions with that of expert-generated heart models. The number and location of VTs was similar between heart models generated from the deep learning-based workflow and expert-generated heart models. These results demonstrate the feasibility of using an automated computational heart modelling workflow to aid in VT therapeutics and has implications for generalizing personalized computational heart technology to a broad range of clinical centres.

Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England

Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. Here we report a new data-driven approach to quantify how "unusual" the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance.

Mock Up Based on Android Through Multimedia Development Live Cycle : Science Meaningfull Learning

Covid 19 has led to loss learning and decreased student motivation in learning so that learning is needed that can increase motivation and meaningfulness in learning. The purpose of this study was to develop an Android-based mock up using the Multimedia Development Live Cycle (MDLC) steps to develop meaningful science learning in elementary schools. This research is a development research using the MDLC stages followed by trials on material, language, media and user experts. The subjects of this study were experts, teachers and 5th grade students. Technical analysis used descriptive quantitative techniques. Data collection methods consist of questionnaires, interviews and tests. Instruments in the form of expert validation sheets, tests, interviews and teacher and student response questionnaires. The results of the study show that the media that has been developed has a material expert feasibility of 68% (appropriate), media expert 91% (very feasible), and language expert 94% (decent). The teacher's practicality assessment obtained a percentage of 87% (very practical) and students obtained a proportion of 77% with very practical criteria. Android-based mock up is feasible to use. This is also based on the results of an increase in the average pretest score of 63.3 and posttest of 77.7. Posttest scores also show scores above the Minimum Completeness Criteria (KKM). Android-based Mock Up can improve student learning outcomes and make science learning more meaningful for students.

Computational flow cytometry provides accurate assessment of measurable residual disease in chronic lymphocytic leukaemia.

Undetectable measurable residual disease (MRD) is associated with favourable clinical outcomes in chronic lymphocytic leukaemia (CLL). While assessment is commonly performed using multiparameter flow cytometry (MFC), this approach is associated with limitations including user bias and expertise that may not be widely available. Implementation of unsupervised clustering algorithms in the laboratory can address these limitations and have not been previously reported in a systematic quantitative manner. We developed a computational pipeline to assess CLL MRD using FlowSOM. In the training step, a self-organising map was generated with nodes representing the full breadth of normal immature and mature B cells along with disease immunophenotypes. This map was used to detect MRD in multiple validation cohorts containing a total of 456 samples. This included an evaluation of atypical CLL cases and samples collected from two different laboratories. Computational MRD showed high correlation with expert analysis (Pearson's r > 0.99 for typical CLL). Binary classification of typical CLL samples as either MRD positive or negative demonstrated high concordance (>98%). Interestingly, computational MRD detected disease in a small number of atypical CLL cases in which MRD was not detected by expert analysis. These results demonstrate the feasibility and value of automated MFC analysis in a diagnostic laboratory.

Harnessing Deep Learning for Omics in an Era of COVID-19

Omics data are multidimensional, heterogeneous, and high throughput. Robust computational methods and machine learning (ML)-based models offer new prospects to accelerate the data-to-knowledge trajectory. Deep learning (DL) is a powerful subset of ML inspired by brain structure and has created unprecedented momentum in bioinformatics and computational biology research. This article provides an overview of the current DL models applied to multi-omics data for both the beginner and the expert user. Additionally, COVID-19 will continue to impact planetary health as a pandemic and an endemic disease, with genomic and multi-omic pathophysiology. DL offers, therefore, new ways of harnessing systems biology research on COVID-19 diagnostics and therapeutics. Herein, we discuss, first, the statistical ML algorithms and essential deep architectures. Then, we review DL applications in multi-omics data analysis and their intersection with COVID-19. Finally, challenges and several promising directions are highlighted going forward in the current era of COVID-19.

Robot-assisted pelvic floor reconstructive surgery: an international Delphi study of expert users

BackgroundRobotic surgery has gained popularity for the reconstruction of pelvic floor defects. Nonetheless, there is no evidence that robot-assisted reconstructive surgery is either appropriate or superior to standard laparoscopy for the performance of pelvic floor reconstructive procedures or that it is sustainable. The aim of this project was to address the proper role of robotic pelvic floor reconstructive procedures using expert opinion.MethodsWe set up an international, multidisciplinary group of 26 experts to participate in a Delphi process on robotics as applied to pelvic floor reconstructive surgery. The group comprised urogynecologists, urologists, and colorectal surgeons with long-term experience in the performance of pelvic floor reconstructive procedures and with the use of the robot, who were identified primarily based on peer-reviewed publications. Two rounds of the Delphi process were conducted. The first included 63 statements pertaining to surgeons’ characteristics, general questions, indications, surgical technique, and future-oriented questions. A second round including 20 statements was used to reassess those statements where borderline agreement was obtained during the first round. The final step consisted of a face-to-face meeting with all participants to present and discuss the results of the analysis.ResultsThe 26 experts agreed that robotics is a suitable indication for pelvic floor reconstructive surgery because of the significant technical advantages that it confers relative to standard laparoscopy. Experts considered these advantages particularly important for the execution of complex reconstructive procedures, although the benefits can be found also during less challenging cases. The experts considered the robot safe and effective for pelvic floor reconstruction and generally thought that the additional costs are offset by the increased surgical efficacy.ConclusionRobotics is a suitable choice for pelvic reconstruction, but this Delphi initiative calls for more research to objectively assess the specific settings where robotic surgery would provide the most benefit.

A novel spatiotemporal prediction method based on fuzzy Transform: Application to demographic balance data

Many issues require the application of forecasting models applied to spatiotemporal data in Geographic Information Systems (GIS) to predict the spatial distribution and evolution of a specific feature. The use of soft computing techniques in the development of these forecasting models makes it possible to detect non-linear trends but has the disadvantage of increasing the computational complexity of the model. In this paper we present a GIS-based framework in which a fast soft computing forecasting model based on the multidimensional Fuzzy Transform (for short, MF-transform) is applied to evaluate the spatial distribution and the time evolution in a study area of a measurable entity (the feature). The study area is divided into homogeneous zones (the subzones) in which the feature was measured in each time frame. The time series of the feature are analyzed to assess the trend of the feature in subsequent time frames; furthermore, those sub-areas are detected in which the feature is higher than a maximum threshold (hot spots) or lower than a minimum threshold (cold spots) in this time range. A process of fuzzifying the values of the feature is carried out in order to facilitate the interpretation of the results by expert users. The framework was tested on a study area provided by the province of Naples (Italy) to predict and analyze the spatial distribution and temporal trend of the monthly rate of births compared to deaths. Furthermore, the thematic map of the hot and cold spots detected in the three months following the time period of measurements was built. The results show that our method provides reliable results both in terms of forecast error and similarity between the detected hot and cold spots and those who have really formed.

Are sticky users less likely to lurk? Evidence from online reviews

ABSTRACT Although many product providers deem user contributions (e.g. online reviews) important, providers often struggle to obtain them, i.e. most users are lurkers who are reluctant to post reviews. This study is conducted to understand better how to delurk users by investigating the role of user stickiness in review posting behaviour. By employing a large-scale dataset from TapTap, a Chinese mobile game community, and conducting a multimethod investigation, this study found that sticky users with a product are more likely to engage in review posting behaviour related to the product. Also, this positive stickiness-post effect varies for some user and product features: (a) the positive relationship between user stickiness and posting behaviour will be strengthened as user expertise rises, (b) the positive stickiness-post relationship will be alleviated when products are collaborative-consuming products (vs. private-consuming products), (c) and the positive stickiness effect on review posting behaviour will be stronger when product providers are small-scale (vs. large-scale). These findings provide a comprehensive understanding of biases in lurking/posting behaviour related to user stickiness and help product providers gain insights into delurking users and gathering user intelligence.

PrintAssist—a conversational human-machine interface for 3D printers

Abstract 3D printers are no longer found only in industry, universities or makerspaces but now are increasingly used in domestic settings. Personal fabrication will increase in the coming years, and 3D printing will play an important role in this process. Due to technology and price development, 3D printers are becoming established among casual users at home. However, there are still many hurdles in the use of 3D printers that interfere with their appropriation in everyday life. In this paper, we investigate how chatbots can overcome these hurdles and support onboarding to 3D printing. Furthermore, we explore how chatbots can be used as a human–machine interface and facilitate interaction with 3D printers for both novice and expert users. In a research-through-design approach, we have created a fully functional chatbot that introduces users to 3D printing and helps them perform typical tasks when operating 3D printers.

Exploring the Influencing Factors on User Experience in Robot-Assisted Health Monitoring Systems Combining Subjective and Objective Health Data

As the population ages, the demand for care for older adults is increasing. To maintain their independence and autonomy, even with declining health, assistive technologies such as connected medical devices or social robots can be useful. In previous work, we introduced a novel health monitoring system that combines commercially available products with apps designed specifically for older adults. The system is intended for the long-term collection of subjective and objective health data. In this work, we present an exploratory user experience (UX) and usability study we conducted with older adults as the target group of the system and with younger expert users who tested our system. All participants interacted with a social robot conducting a health assessment and tested sensing devices and an app for data visualization. The UX and usability of the individual components of the system were rated highly in questionnaires in all sessions. All participants also said they would use such a system in their everyday lives, demonstrating the potential of these systems for self-managing users’ health. Finally, we found factors such as previous experience with social robots and technological expertise to have an influence on the reported UX of the users.

Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation

Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human-machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference.

How does the design of landmarks on a mobile map influence wayfinding experts’ spatial learning during a real-world navigation task?

ABSTRACT Humans increasingly rely on GPS-enabled mobile maps to navigate novel environments. However, this reliance can negatively affect spatial learning, which can be detrimental even for expert navigators such as search and rescue personnel. Landmark visualization has been shown to improve spatial learning in general populations by facilitating object identification between the map and the environment. How landmark visualization supports expert users’ spatial learning during map-assisted navigation is still an open research question. We thus conducted a real-world study with wayfinding experts in an unknown residential neighborhood. We aimed to assess how two different landmark visualization styles (abstract 2D vs. realistic 3D buildings) would affect experts’ spatial learning in a map-assisted navigation task during an emergency scenario. Using a between-subjects design, we asked Swiss military personnel to follow a given route using a mobile map, and to identify five task-relevant landmarks along the route. We recorded experts’ gaze behavior while navigating and examined their spatial learning after the navigation task. We found that experts’ spatial learning improved when they focused their visual attention on the environment, but the direction of attention between the map and the environment was not affected by the landmark visualization style. Further, there was no difference in spatial learning between the 2D and 3D groups. Contrary to previous research with general populations, this study suggests that the landmark visualization style does not enhance expert navigators’ navigation or spatial learning abilities, thus highlighting the need for population-specific mobile map design solutions.

Identifying the Needs of Older Adults Associated with Daily Activities: A Qualitative Study

By 2050, older adults will constitute 16% of the world population; hence, there is an urgent demand and challenge to design solutions (products and services) that meet the needs of this age group. This study sought to analyse the needs that impact the well-being of Chilean older adults and present possible solutions through the design of products. A qualitative study was used, where focus groups were held with older adults, industrial designers, health professionals, and entrepreneurs on the needs and design of solutions for older adults. A general map was obtained that linked the categories and subcategories related to the relevant needs and solutions, which were then classified in a framework. The resulting proposal places the needs in different fields of expertise; and thus, enables positioning, broadening, and expanding upon the map to share knowledge, between the user and key experts, to co-create solutions.

An Examination of Factors Determining User Privacy Perceptions of Voice-Based Assistants

Purpose: Voice-based assistants have become ubiquitous in our homes and all around us. We interact with voice-based assistants through smartphones, dedicated home devices, or other Internet-connected devices. The proliferation of these devices in our lives makes us question whether the data transmitted through these devices are secure and the role perception of privacy the users of these devices have in the usage of the device. This paper focuses on the user privacy issues specific to two voice-based assistants – Amazon Alexa and Google Assistant using quantitative and qualitative methods. The paper will answer three research questions regarding user privacy perceptions and how a user’s technology adoption, smartphone operating system, and demographics influence their perception of privacy. Study design/methodology/approach: This is a mixed research study where survey data was collected from faculty, staff, and students of a large University in the United States and professional connections globally. Findings: Privacy perceptions vary based on user age, educational level, gender, smartphone adoption, and technical awareness and expertise. Originality/value: This study is unique in highlighting the perception of privacy for voice-based assistants and assigning a numerical value based on survey data to the concern. Another feature of the study is its correlation of privacy perception with mobile phone operating systems and the level of technical expertise of voice-based assistant users.