User Study Research Articles

The usability of a wireless vocal fold vibration switch for individuals with complex communication needs: a caregiver-inclusive iterative home-based evaluation

Purpose: A vocal fold vibration switch is a type of access technology that detects voluntary vibrations of the vocal cords. In two sequential usability studies, we evaluated successive prototypes of a novel wireless vocal fold vibration switch. Methods: Each usability study enroled 7 dyads consisting of individuals with complex communication needs and their caregivers. Each study entailed a 2-week period of independent home use flanked by initial and final usability tests. Each usability test comprised nine tasks including donning and configuration of the switch, connecting it to other devices, and actual activation of the switch. Perceived task difficulty was measured via nine 21-point scales, while caregiver impressions were captured through open-ended questions, both encapsulated within a usability questionnaire. Caregivers described their experience in an open-ended interview in the second usability study. Results: Perceived task difficulty was uniformly low with both prototypes. The vast majority of tasks could be completed either independently or with hints. A small number of contraindications for use were identified with the second prototype, which included episodes of dystonia and heavy breathing, and some aspects of the physical design and robustness were flagged as needing further improvement. Conclusions: The overall qualitative feedback was overwhelmingly positive with caregivers generally desiring to keep and eager to recommend the device to others. Additionally, caregivers expressed that the vocal fold vibration switch expanded the switch user’s capabilities, and they foresaw applications beyond the study setting. Overall, our findings suggest that the wireless vocal fold vibration switch is useable in the home setting.

Haptic2FA: Haptics-Based Accessible Two-Factor Authentication for Blind and Low Vision People

Two-factor Authentication (also known as 2FA or two-step verification) is an authentication method that provides an extra layer of protection to ensure online account security. 2FA methods are used along with other primary authentication methods like PINs and Passwords to verify that the person trying to access any digital account is the person they are claiming to be. However, 2FA methods can be inaccessible for blind and low vision (BLV) users due to the requirement of multiple steps, apps, and/or devices for authentication. In addition, it can be a security risk as screen readers may read out the verification codes to bystanders. To address this, we present Haptic2FA, a haptic-based authentication method to improve 2FA accessibility for BLV users. Here, as a part of the 2FA process, the users are sent a 'haptic pattern' (similar to a one-time passcode in traditional 2FA methods) that they are required to enter or select for verification. Through a usability study with 10 BLV participants, we evaluated haptic patterns and input methods for the haptic patterns in the Haptic2FA method. Through the findings, we discuss the accessibility and usability of the Haptic2FA method.

An Examination of Ultrasound Mid-air Haptics for Enhanced Material and Temperature Perception in Virtual Environments

Rendering realistic tactile sensations of virtual objects remains a challenge in VR. While haptic interfaces have advanced, particularly with phased arrays, their ability to create realistic object properties like state and temperature remains unclear. This study investigates the potential of Ultrasound Mid-air Haptics (UMH) for enhancing the perceived congruency of virtual objects. In a user study with 30 participants, we assessed how UMH impacts the perceived material state and temperature of virtual objects. We also analyzed EEG data to understand how participants integrate UMH information physiologically. Our results reveal that UMH significantly enhances the perceived congruency of virtual objects, particularly for solid objects, reducing the feeling of mismatch between visual and tactile feedback. Additionally, UMH consistently increases the perceived temperature of virtual objects. These findings offer valuable insights for haptic designers, demonstrating UMH's potential for creating more immersive tactile experiences in VR by addressing key limitations in current haptic technologies.

Snap&Nav: Smartphone-based Indoor Navigation System For Blind People via Floor Map Analysis and Intersection Detection

We present Snap&Nav, a navigation system for blind people in unfamiliar buildings, without prebuilt digital maps. Instead, the system utilizes the floor map as its primary information source for route guidance. The system requires a sighted assistant to capture an image of the floor map, which is analyzed to create a node map containing intersections, destinations, and current positions on the floor. The system provides turn-by-turn navigation instructions while tracking users' positions on the node map by detecting intersections. Additionally, the system estimates the scale difference of the node map to provide distance information. Our system was validated through two user studies with 20 sighted and 12 blind participants. Results showed that sighted participants processed floor map images without being accustomed to the system, while blind participants navigated with increased confidence and lower cognitive load compared to the condition using only cane, appreciating the system's potential for use in various buildings.

EarMonitor: Non-clinical Assessment of Ear Health Conditions Using a Low-cost Endoscope Camera on Smartphones

Hearing loss affects 20% of the global population, a rate that is increasing dramatically as the world's population ages. Early prevention and identification of ear diseases can significantly reduce the risk of becoming disabled with hearing impairment. We propose EarMonitor, an interactive, vision-based ear health monitoring system that enables users to examine their ear conditions with a low-cost hand-held endoscope. EarMonitor can detect six ear health conditions suitable for self-assessment. It can particularly recognize complications from ear diseases, helping users better understand the results. In the wild, our computer vision algorithm achieves a detection sensitivity of 0.949 for earwax buildup and blockage in 100 external auditory canal photos; our deep learning model achieves an average detection sensitivity of 0.861 for the other five conditions considering complications in 350 tympanic membrane photos. We validated EarMonitor 's effectiveness through a user study involving 17 participants and two experts, leading to valuable insights regarding the design and interpretation of non-clinical assessment devices.

Informing the Design of Intervention Solutions for Body-Focused Repetitive Behaviors

Body-Focused Repetitive Behaviors (BFRBs), such as nail biting, impact a wide demographic, and can negatively affect physical, psychological, and social well-being. Although pharmacological and behavioral therapies are common treatments, many avoid seeking help, and not everyone responds fully to treatment. Recent advances in wearable sensing enable new digital solutions that can detect BFRB episodes and intervene to mitigate them. While BFRBs have been extensively studied in medical research, translating this knowledge into effective digital intervention solutions may not be straightforward, and the end user's perspective may be overlooked. We report a user study with 12 frequent nail biters, who shared their experiences about nail biting and expectations of intervention solutions in semi-structured qualitative interviews. We describe the progression of a nail biting episode from a nail biter's perspective and present a taxonomy of intervention strategies to mitigate nail biting. Our results inform the design of future digital BFRB intervention solutions.

Delusio - Plausible Deniability For Face Recognition

We developed an Android phone unlock mechanism utilizing facial recognition and specific mimics to access a specially secured portion of the device, designed for plausible deniability. The widespread adoption of biometric authentication methods, such as fingerprint and facial recognition, has revolutionized mobile device security, offering enhanced protection against shoulder-surfing attacks and improving user convenience compared to traditional passwords. However, a downside is the potential for third-party coercion to unlock the device. While text-based authentication allows users to reveal a hidden system by entering a special password, this is challenging with face authentication. We evaluated our approach in a role-playing user study involving 50 participants, with one participant acting as the attacker and the other as the suspect. Suspects successfully accessed the secured area, mostly without detection. They further expressed interest in this feature on their personal phones. We also discuss open challenges and opportunities in implementing such authentication mechanisms.

AudioMove: Applying the Spatial Audio to Multi-Directional Limb Exercise Guidance

Guiding users with limb exercise can assist in muscle training or physical recovery. However, traditional vision-based methods often require multiple camera angles to help users understand the motions and require them to be within the range of the screen. Therefore, we propose a non-visual system that can guide users with multiple-directional limb motions utilizing spatial audio, AudioMove , with commercial-off-the-shelf (COTS) devices (i.e., smartphones and earphones). The proposed system addresses the challenge of conveying directional information encompassing multiple planes in real-time. We conduct a mixed-method user study to evaluate the effectiveness of the system with three methods combining motion data with spatial audio perception. Additionally, a user interface is built to collect users' comments. The results conclude that spatial audio guidance could create a natural, pervasive, and non-visual exercise training solution in daily life.

Cognitive Assistance Systems in Intralogistics - User studies with augmented reality and an AI chatbot

Cognitive Assistance Systems in Intralogistics - User studies with augmented reality and an AI chatbot

ExTraCT - Explainable trajectory corrections for language-based human-robot interaction using textual feature descriptions.

In human-robot interaction (HRI), understanding human intent is crucial for robots to perform tasks that align with user preferences. Traditional methods that aim to modify robot trajectories based on language corrections often require extensive training to generalize across diverse objects, initial trajectories, and scenarios. This work presents ExTraCT, a modular framework designed to modify robot trajectories (and behaviour) using natural language input. Unlike traditional end-to-end learning approaches, ExTraCT separates language understanding from trajectory modification, allowing robots to adapt language corrections to new tasks-including those with complex motions like scooping-as well as various initial trajectories and object configurations without additional end-to-end training. ExTraCT leverages Large Language Models (LLMs) to semantically match language corrections to predefined trajectory modification functions, allowing the robot to make necessary adjustments to its path. This modular approach overcomes the limitations of pre-trained datasets and offers versatility across various applications. Comprehensive user studies conducted in simulation and with a physical robot arm demonstrated that ExTraCT's trajectory corrections are more accurate and preferred by users in 80% of cases compared to the baseline. ExTraCT offers a more explainable approach to understanding language corrections, which could facilitate learning human preferences. We also demonstrated the adaptability and effectiveness of ExTraCT in a complex scenarios like assistive feeding, presenting it as a versatile solution across various HRI applications.

A Digital Intervention for Capturing the Real-Time Health Data Needed for Epilepsy Seizure Forecasting: Protocol for a Formative Co-Design and Usability Study (The ATMOSPHERE Study).

Epilepsy is a chronic neurological disorder affecting individuals globally, marked by recurrent and apparently unpredictable seizures that pose significant challenges, including increased mortality, injuries, and diminished quality of life. Despite advancements in treatments, a significant proportion of people with epilepsy continue to experience uncontrolled seizures. The apparent unpredictability of these events has been identified as a major concern for people with epilepsy, highlighting the need for innovative seizure forecasting technologies. The ATMOSPHERE study aimed to develop and evaluate a digital intervention, using wearable technology and data science, that provides real-time, individualized seizure forecasting for individuals living with epilepsy. This paper reports the protocol for one of the workstreams focusing on the design and testing of a prototype to capture real-time input data needed for predictive modeling. The first aim was to collaboratively design the prototype (work completed). The second aim is to conduct an "in-the-wild" study to assess usability and refine the prototype (planned research). This study uses a person-based approach to design and test the usability of a prototype for real-time seizure precipitant data capture. Phase 1 (work completed) involved co-design with individuals living with epilepsy and health care professionals. Sessions explored users' requirements for the prototype, followed by iterative design of low-fidelity, static prototypes. Phase 2 (planned research) will be an "in-the-wild" usability study involving the deployment of a mid-fidelity, functional prototype for 4 weeks, with the collection of mixed methods usability data to assess the prototype's real-world application, feasibility, acceptability, and engagement. This phase involves primary participants (adults diagnosed with epilepsy) and, optionally, their nominated significant other. The usability study will run in 3 rounds of deployment and data collection, aiming to recruit 5 participants per round, with prototype refinement between rounds. The phase-1 co-design study engaged 22 individuals, resulting in the development of a mid-fidelity, functional prototype based on identified requirements, including the tracking of evidence-based and personalized seizure precipitants. The upcoming phase-2 usability study is expected to provide insights into the prototype's real-world usability, identify areas for improvement, and refine the technology for future development. The estimated completion date of phase 2 is the last quarter of 2024. The ATMOSPHERE study aims to make a significant step forward in epilepsy management, focusing on the development of a user-centered, noninvasive wearable device for seizure forecasting. Through a collaborative design process and comprehensive usability testing, this research aims to address the critical need for predictive seizure forecasting technologies, offering a promising approach to improving the lives of individuals with epilepsy. By leveraging predictive analytics and personalized machine learning models, this technology seeks to offer a novel approach to managing epilepsy, potentially improving clinical outcomes, including quality of life, through increased predictability and seizure management. DERR1-10.2196/60129.

Supporting motion-capture acting with collaborative Mixed Reality

Technologies such as chroma-key, LED walls, motion capture (mocap), 3D visual storyboards, and simulcams are revolutionizing how films featuring visual effects are produced. Despite their popularity, these technologies have introduced new challenges for actors. An increased workload is faced when digital characters are animated via mocap, since actors are requested to use their imagination to envision what characters see and do on set. This work investigates how Mixed Reality (MR) technology can support actors during mocap sessions by presenting a collaborative MR system named CoMR-MoCap, which allows actors to rehearse scenes by overlaying digital contents onto the real set. Using a Video See-Through Head Mounted Display (VST-HMD), actors can see digital representations of performers in mocap suits and digital scene contents in real time. The system supports collaboration, enabling multiple actors to wear both mocap suits to animate digital characters and VST-HMDs to visualize the digital contents. A user study involving 24 participants compared CoMR-MoCap to the traditional method using physical props and visual cues. The results showed that CoMR-MoCap significantly improved actors’ ability to position themselves and direct their gaze, and it offered advantages in terms of usability, spatial and social presence, embodiment, and perceived effectiveness over the traditional method.

Usability of Pseudo-Haptic Feedback for Manual Precise Manipulation of Objects in Augmented Reality

Manual precise manipulation of objects is a critical skill in daily life, and Augmented Reality (AR) is increasingly used to support such tasks. In this study, we propose a system utilizing pseudo-haptic feedback to support precise manipulation for six degrees of freedom (6DOF). Two types of AR instruction interfaces were developed: Visual Deviation Instruction Interface (VDI) and Pseudo-Haptic Instruction Interface (PHI). A user study with 18 participants compared the two instruction interfaces in terms of performance and user experience. The objective measures of performance (task completion time, deviation), and the subjective measures (system usability scale, NASA Task Load Index) were collected. Results show that both instruction interfaces effectively support manual precise manipulation, achieving position deviations under 2 mm and orientation deviations under 1°. PHI outperformed VDI in speed, mental effort, physical demand, performance, perceived workload, custom user experience elements and reduction deviations of manual precise manipulation. Finally, we discuss research limitations and future directions.

Using a Guidance Virtual Fixture on a Soft Robot to Improve Ureteroscopy Procedures in a Phantom

Manipulating a flexible ureteroscope is difficult, due to its bendable body and hand–eye coordination problems, especially when exploring the lower pole of the kidney. Though robotic interventions have been adopted in various clinical scenarios, they are rarely used in ureteroscopy. This study proposes a teleoperation system consists of a soft robotic endoscope together with a Guidance Virtual Fixture (GVF) to help users explore the kidney’s lower pole. The soft robotic arm was a cable-driven, 3D-printed design with a helicoid structure. GVF was dynamically constructed using video streams from an endoscopic camera. With a haptic controller, GVF can provide haptic feedback to guide the users in following a trajectory. In the user study, participants were asked to follow trajectories when the soft robotic arm was in a retroflex posture. The results suggest that the GVF can reduce errors in the trajectory tracking tasks when the users receive the proper training and gain more experience. Based on the NASA Task Load Index questionnaires, most participants preferred having the GVF when manipulating the robotic arm. In conclusion, the results demonstrate the benefits and potential of using a robotic arm with a GVF. More research is needed to investigate the effectiveness of the GVFs and the robotic endoscope in ureteroscopic procedures.

Knowledge Graph-Based Integration of Conversational Advisors and Faceted Filtering

Abstract Modern e-commerce websites often provide users with a variety of components, such as faceted filters and conversational recommender systems, that act as product advisors to help them find relevant products. However, these components are often treated separately and presented as independent components, leading to increased cognitive load and disruption in the search process. Also, the reasoning behind the resulting product recommendations is often not transparent. To address these limitations, we propose a novel approach that relies on a knowledge graph structure to seamlessly integrate faceted filtering and conversational advisors based on graphical user interfaces (GUI). Concretely, the knowledge graph is used to suggest filter values and products based on the user’s answers in the advisor, and, conversely, to determine follow-up questions based on the user’s selected filter values. The user interface also visualizes and explains the underlying relationships between answers given to the advisor and relevant product features in the filter component in order to increase the transparency of the search process. We conducted two user studies with a total of 448 participants to compare a system that integrates the different components according to our approach with a baseline system in which the mechanisms operate separately. Sequence analysis of the logged interaction data provided insights into participants’ behavior as they interacted with both systems. The results indicate that displaying recommended products and related explanations directly in the filter component increases acceptance and trust in the system. Also, the combination of a conversational advisor with values displayed in a filter interface, along with explanations of the underlying relationships, significantly contributes to the knowledge and understanding of those product features that are important in terms of the current search goal.

Development and Delivery of an Integrated Digital Health Care Approach for Children With Juvenile Idiopathic Arthritis: Usability Study.

Juvenile idiopathic arthritis (JIA) is a chronic inflammatory disorder with no cure. Most children are prescribed several medications aimed at controlling disease activity, managing symptoms, and reducing pain. Physical activity is also encouraged to retain musculoskeletal function. The primary determinants of treatment success are maintaining long-term adherence, ongoing monitoring by a pediatric rheumatologist, and involvement of an interdisciplinary team. To support these goals, a new digital intervention was developed, InteractiveClinics, which aimed to prompt children to take their medications, report pain levels, and increase their physical activity. This study aims to evaluate the usability of InteractiveClinics among children with JIA. As part of this pediatric cross-sectional study, 12 children were asked to wear a smartwatch for 2 weeks, which was synchronized to the InteractiveClinics phone app and web-based platform. Personalized notifications were sent daily to the watch and phone, to prompt and record medication adherence and pain level assessment. Physical activity was automatically recorded by the watch. At the end of the study, all children and parents completed a postintervention survey. Written comments were also encouraged to gain further feedback. Descriptive statistics were used to summarize the survey results, and all qualitative data underwent thematic analysis. Twelve children aged 10 to 18 years (mean 14.2, SD 3.1 years; female: n=8, 66.7%) and 1 parent for each child (n=12; female: n=8, 66.7%) were enrolled in the study. Based on the highest and lowest agreement areas of the survey, most children and parents liked the smartwatch and web-based platform; they found it easy to learn and simple to use. They were also satisfied with the pain and physical activity module. However, usability and acceptability barriers that hindered uptake were identified in the phone app and medication module. Children required a unique in-app experience, and their suggestive improvements included more personalization within the app; simplification by removing all links not relevant to antirheumatic medications; flexibility in response times; improved conferment through gamification; additional comment fields for the input of more data, such as medication side effects or pain-related symptoms; more detailed graphical illustrations of the physical activity module, including a breakdown of metrics; and importantly, interconnections between modules, because medication adherence, pain levels, and physical activity can each influence the other. They were, overall, improving usefulness for children and parents. The usability of InteractiveClinics was positive. Children and parents liked the watch and web-based platform and were satisfied with the pain and physical activity module. However, children wanted a unique in-app experience through more personalization, simplification, flexibility, conferment, comment fields, graphical illustrations, a breakdown of metrics, and interconnections. Certainly, inclusions are needed to promote user adoption and advancement of new validated digital health interventions in pediatric rheumatology, to support the delivery of integrated care.

Zero-Configuration Alarms: Towards Reducing Distracting Smartphone Interactions while Driving

The rising ubiquity of smartphones for navigation and driver mode, among others, has increased their use significantly among drivers; however, there are growing numbers of road fatalities being reported due to distractions from the phone while driving. In contrast to the existing solutions that use a camera or other communication media on the car or need external setups, this article proposes a solution called ZeCA , where the smartphone itself can identify in real time with zero pre-configurations whether its user is driving while engaging in a high-distraction interaction with the phone. ZeCA runs as a smartphone background service and generates audio-visual alerts when the phone can distract the driver. A thorough evaluation and usability study of ZeCA with 50 different models of vehicles driven by 70 drivers over five countries indicates that the proposed solution can infer distracting smartphone interactions with greater than 80% accuracy and a 70% reduction in smartphone usage during driving.

Evaluating User Experience and Data Quality in Gamified Data Collection for Appearance-Based Gaze Estimation

Appearance-based gaze estimation, which uses only a regular camera to estimate human gaze, is important in various application fields. While the technique faces data bias issues, data collection protocol is often demanding, and collecting data from a wide range of participants is difficult. It is an important challenge to design opportunities that allow a diverse range of people to participate while ensuring the quality of the training data. To tackle this challenge, we introduce a novel gamified approach for collecting training data. In this game, two players communicate words via eye gaze through a transparent letter board. Images captured during gameplay serve as valuable training data for gaze estimation models. The game is designed as a physical installation that involves communication between players, and it is expected to attract the interest of diverse participants. We assess the game’s significance on data quality and user experience through a comparative user study.

Early feasibility and usability study of a novel obstetric blood loss quantifying device

ObjectiveTo study the accuracy and usability of a novel obstetric blood loss quantifying tool in clinical settings. MethodsA mixed-methods study was conducted in an Irish tertiary maternity unit. The accuracy of measuring the blood content (hemoglobin concentration) of elective Caesarean section birth waste with a novel obstetric blood loss quantifying device was compared, using Bland-Altman and correlation analysis, with staff volumetry and a reference hemoglobinometer. Hospital staff (nurses, midwives and doctors) opinion of the usability of the device was studied by an anonymous questionnaire which generated a System Usability Scale (SUS) score. SUS scores range from 0 to 100 with higher scores reflecting greater usability. ResultsThe device was used by 19 different hospital staff members (nine nurses, four midwives and six doctors) in 19 elective Caesarean deliveries that had varying levels of PPH risk. Bland-Altman analysis produced mean biases of −0.7 ± 1.5 g/dL and 0.2 ± 1.2 g/dL for the device and staff measurements respectively. The width of the limits of agreement was narrower for device measurements than for staff measurements (4.5 g/dL and 5.7 g/dL respectively). The device’s measurements of hemoglobin content correlated more strongly with the hemoglobinometer rather than with hospital staff measurements (Spearman correlation coefficients of 0.9 and 0.6 respectively). This suggests that the device is more accurate in determining the blood content of the birth waste than hospital staff volumetric measurements. Hospital staff members assigned the device a mean SUS score of 82 which suggests that the device is highly usable. ConclusionAn early feasibility study in clinical settings suggests that a novel device for quantifying obstetric blood loss was more accurate than volumetry in measuring the hemoglobin content of birth waste. Health professionals also found the device highly usable. This data suggests that there is much potential in transitioning from a “human-made” to a “machine-made” assessment of blood loss. Future studies will entail additional testing of the device to assess its impact on the morbidities associated with postpartum hemorrhage.

User Experience of Visualizations in Motion: A Case Study and Design Considerations.

We present a systematic review, an empirical study, and a first set of considerations for designing visualizations in motion, derived from a concrete scenario in which these visualizations were used to support a primary task. In practice, when viewers are confronted with embedded visualizations, they often have to focus on a primary task and can only quickly glance at a visualization showing rich, often dynamically updated, information. As such, the visualizations must be designed so as not to distract from the primary task, while at the same time being readable and useful for aiding the primary task. For example, in games, players who are engaged in a battle have to look at their enemies but also read the remaining health of their own game character from the health bar over their character's head. Many trade-offs are possible in the design of embedded visualizations in such dynamic scenarios, which we explore in-depth in this paper with a focus on user experience. We use video games as an example of an application context with a rich existing set of visualizations in motion. We begin our work with a systematic review of in-game visualizations in motion. Next, we conduct an empirical user study to investigate how different embedded visualizations in motion designs impact user experience. We conclude with a set of considerations and trade-offs for designing visualizations in motion more broadly as derived from what we learned about video games. All supplemental materials of this paper are available at osf.io/3v8wm/.