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Navigation assistive technologies have been designed to support the mobility of people who are blind and visually impaired during independent navigation by providing sensory augmentation, spatial information and general awareness of their environment. This paper focuses on the extended Usability and User Experience (UX) evaluation of BlindRouteVision, an outdoor navigation smartphone application that tries to efficiently solve problems related to the pedestrian navigation of visually impaired people without the aid of guides. The proposed system consists of an Android application that interacts with an external high-accuracy GPS sensor tracking pedestrian mobility in real-time, a second external device specifically designed to be mounted on traffic lights for identifying traffic light status and an ultrasonic sensor for detecting near-field obstacles along the route of the blind. Moreover, during outdoor navigation, it can optionally incorporate the use of Public Means of Transport, as well as provide multiple other uses such as dialing a call and notifying the current location in case of an emergency. We present findings from a Usability and UX standpoint of our proposed system conducted in the context of a pilot study, with 30 people having varying degrees of blindness. We also received feedback for improving both the available functionality of our application and the process by which the blind users learn the features of the application. The method of the study involved using standardized questionnaires and semi-structured interviews. The evaluation took place after the participants were exposed to the system’s functionality via specialized user-centered training sessions organized around a training version of the application that involves route simulation. The results indicate an overall positive attitude from the users.

In China, a BCI competition was firstly organized by Tsinghua University in 2010. Since 2017, the BCI competition has been organized by China Electronics Society as part of World Robotics Conference. Turi et al. demonstrate that the emotional state of the user impacts performance when learning to control a four-class mental imagery BCI and present a multi-stage, user-centered training protocol to enable successful control, even in stressful situations, such as training for a competitive BCI race. Robinson et al. show that closed-loop BCI calibration paradigms with real-time feedback are more engaging for the pilot, achieve better online performance, and lead to more localized brain activation patterns when compared to conventional open-loop BCI calibration paradigms.

Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated. This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes. We inputted all 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual into ChatGPT and compared its accuracy on differential diagnoses, diagnostic testing, final diagnosis, and management based on patient age, gender, and case acuity. Accuracy was measured by the proportion of correct responses to the questions posed within the clinical vignettes tested, as calculated by human scorers. We further conducted linear regression to assess the contributing factors toward ChatGPT's performance on clinical tasks. ChatGPT achieved an overall accuracy of 71.7% (95% CI 69.3%-74.1%) across all 36 clinical vignettes. The LLM demonstrated the highest performance in making a final diagnosis with an accuracy of 76.9% (95% CI 67.8%-86.1%) and the lowest performance in generating an initial differential diagnosis with an accuracy of 60.3% (95% CI 54.2%-66.6%). Compared to answering questions about general medical knowledge, ChatGPT demonstrated inferior performance on differential diagnosis (β=-15.8%; P<.001) and clinical management (β=-7.4%; P=.02) question types. ChatGPT achieves impressive accuracy in clinical decision-making, with increasing strength as it gains more clinical information at its disposal. In particular, ChatGPT demonstrates the greatest accuracy in tasks of final diagnosis as compared to initial diagnosis. Limitations include possible model hallucinations and the unclear composition of ChatGPT's training data set.

Recently, due to physical aging, diseases, accidents, and other factors, the population with lower limb disabilities has been increasing, and there is consequently a growing demand for wheelchair products. Modern product design tends to be more intelligent and multi-functional than in the past, with the popularization of intelligent concepts. This supports the design of a new, fully functional, intelligent wheelchair that can assist people with lower limb disabilities in their day-to-day life. Based on the UCD (user-centered design) concept, this study focused on the needs of people with lower limb disabilities. Accordingly, the demand for different functions of intelligent wheelchair products was studied through a questionnaire survey, interview survey, literature review, expert consultation, etc., and the function and appearance of the intelligent wheelchair were then defined. A brain–machine interface system was developed for controlling the motion of the intelligent wheelchair, catering to the needs of disabled individuals. Furthermore, ergonomics theory was used as a guide to determine the size of the intelligent wheelchair seat, and eventually, a new intelligent wheelchair with the features of climbing stairs, posture adjustment, seat elevation, easy interaction, etc., was developed. This paper provides a reference for the design upgrade of the subsequently developed intelligent wheelchair products.

This article presents a systematic review on autism care, diagnosis, and intervention based on mobile apps running on smartphones and tablets. Here, the term “intervention” means a carefully planned set of activities with the objective of improving autism symptoms. We guide our review on related studies using five research questions. First, who benefits the most from these mobile apps? Second, what are the primary purposes of these mobile apps? Third, what mechanisms have been incorporated in these mobiles apps to improve usability? Fourth, what guidelines have been used in the design and implementation of these mobile apps? Fifth, what theories and frameworks have been used as the foundation for these mobile apps to ensure the intervention effectiveness? As can be seen from these research questions, we focus on the usability and software development of the mobile apps. Informed by the findings of these research questions, we propose a taxonomy for the mobile apps and their users. The mobile apps can be categorized into autism support apps, educational apps, teacher training apps, parental support apps, and data collection apps. The individuals with autism spectrum disorder (ASD) are the primary users of the first two categories of apps. Teachers of children with ASD are the primary users of the teacher training apps. Parents are the primary users of the parental support apps, while individuals with ASD are usually the primary users of the data collection apps and clinicians and autism researchers are the beneficiaries. Gamification, virtual reality, and autism-specific mechanisms have been used to improve the usability of the apps. User-centered design is the most popular approach for mobile app development. Augmentative and alternative communication, video modeling, and various behavior change practices have been used as the theoretical foundation for intervention efficacy.

Traditional rehabilitation research often excludes the voices of individuals with lived experience of traumatic brain injury (TBI), resulting in interventions that lack relevance, accessibility, and effectiveness. Community-based participatory research (CBPR) offers an alternative framework that emphasizes collaboration, power sharing, and sustained engagement with patients, caregivers, and clinicians. This study aimed to apply CBPR to guide front-end design (empathy interviews, empathy mapping, personas) and to evaluate the sociotechnical-pedagogical usability of the Electronic Problem-Solving Training (ePST) mobile health (mHealth) intervention with TBI partners. A multistep, mixed methods design case methodology was adopted, guided by CBPR principles and learning experience design. Participatory mechanisms included a 33-member Community Advisory Board and 10 Community Engagement Studios that engaged TBI survivors, caregivers, clinicians, and researchers throughout the Discover, Define, Develop, and Deliver phases of the Double Diamond model. Iterative activities included empathy interviews (n=14), persona development (n=10), rapid prototyping, and usability testing with 5 participants with TBI using think-aloud protocols and the Comprehensive Assessment of Usability for Learning Technologies instrument. The co-design process successfully translated community feedback into an empathy-informed, user-centered prototype and systematically identified design considerations that single-partner approaches overlook. TBI-specific design requirements emerged, including the need for linear content progression over branching navigation, higher technical performance standards, and explicit content signaling with clarity prioritized over novel interface design. Think-aloud protocols revealed that participants struggled with mobile navigation and branching structures but excelled with sequential content progression. In addition, the input from individuals with TBI, caregivers, clinicians, and researchers led to practical refinements such as shorter microlearning lessons (5-12 min), clearer voiceover tone, and simplified navigation, directly addressing the study's objective of improving accessibility and emotional resonance. Overall usability was high, measured using the Comprehensive Assessment of Usability for Learning Technologies (CAUSLT), with an average score of 4.25 out of 5 (SD 0.72; 95% CI 3.36-5.15; n=5). Knowledge accuracy was 80% (8/10 items; 95% CI 49%-94%; n=5 participants; 2 items each), indicating that the system effectively supported learning and comprehension. Module completion was 100% (5/5; 95% CI 56.6%-100%). Average time-on-task for 10 lesson completions was 11.47 (SD 5.28; range 4.6-21.42) minutes per lesson, demonstrating strong task efficiency and engagement. Highest ratings were observed in the pedagogical usability domain, reflecting that the interface was clear, intuitive, and conducive to learning. Collectively, these findings suggest that applying CBPR across all design stages produced a technically sound, easy-to-use, and pedagogically meaningful mHealth tool specifically tailored for individuals with TBI. Sustained CBPR across full design and development cycles resulted in high usability for ePST for individuals with TBI. Ultimately, this study operationalized a full-cycle pipeline that links sustained community partnership to measured usability outcomes, producing community-informed design principles and a reproducible mixed methods approach for formative mHealth development for TBI.

Background Exergames are playful technology-based exercise programs. They train physical and cognitive functions to preserve independence in older adults (OAs) with disabilities in daily activities and may reduce their risk of falling. This study gathered in-depth knowledge and understanding of three different user groups’ experiences in and relevant needs, worries, preferences, and expectations of technology-based training, to develop an exergame training device for OAs. Methods We conducted a qualitative study using semi-structured focus group interviews of primary (OAs in geriatric or neurological rehabilitation) and secondary (health professionals) end users, as well as expert interviews of tertiary end users (health insurance experts or similar), exploring user perspectives on adjusting an existing exergame to OAs’ needs. Voice-recorded interviews were transcribed by researchers and analyzed using thematic analysis (TA) following an inductive, data-driven, iterative approach. Results We interviewed 24 primary, 18 secondary, and 9 tertiary end users at two rehabilitation centers in Austria and Switzerland. Our TA approach identified five to six themes per user group. Themes in the primary end user group reflected aspects of safety, training goals, individuality, game environment, social interactions, and physical and technical overload. Themes in the secondary end user group comprised facets of meaningfulness, distraction through the game environment, safety, gamification elements, the availability and accessibility of the exergame. Tertiary end users’ themes addressed aspects of financial reimbursement, suitable target populations, professional training for the handling of exergame devices, training goals, and concerns about the use of exergames in geriatric rehabilitation. Conclusions In conclusion, an exergame for OAs must be safe, motivating and fully adaptable to the target group while promoting the return to or preservation of autonomy and independence in daily life. Our findings contribute to developing hard- and software extensions for the ExerG training device. Further research is needed to expand the validity of our findings to larger populations.

Background Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. (“Lyssn”) a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform. Methods Phase I will work from an existing software prototype to develop a LyssnCBT user interface geared to the needs of community mental health (CMH) agencies. Core activities include a user-centered design focus group and interviews with community mental health therapists, supervisors, and administrators to inform the design and development of LyssnCBT. LyssnCBT will be evaluated for usability and implementation readiness in a final stage of Phase I. Phase II will conduct a stepped-wedge, hybrid implementation-effectiveness randomized trial ( N  = 1,875 clients) to evaluate the effectiveness of LyssnCBT to improve therapist CBT skills and client outcomes and reduce client drop-out. Analyses will also examine the hypothesized mechanism of action underlying LyssnCBT. Discussion Successful execution will provide automated, scalable CBT fidelity feedback for the first time ever, supporting high-quality training, supervision, and quality assurance, and providing a core technology foundation that could support the quality delivery of a range of EBPs in the future. Trial registration ClinicalTrials.gov; NCT05340738 ; approved 4/21/2022.

Due to an increasing number of robots in the working world, the interaction between humans and robots will become more and more frequent. Hence, the research field of human–robot collaboration is becoming progressively relevant and should therefore be included in students’ education as early as possible. This paper deals with the requirements of a basic course for university students of interdisciplinary studies like Engineering Psychology or Human Factors. The goal of the course is to provide an adequate training for the students in order to gain a technical basis to design human–robot collaboration in a user-centered way in their future working life. Following a user-centered approach a narrative description and analysis of the context of use with contextual interviews were conducted. Overall, 12 subjects of the identified user groups (1) students studying Engineering Psychology, (2) students with knowledge in robotics and (3) graduates of the Engineering Psychology program participated in the contextual interviews. With these interviews, 47 user and stakeholder needs and 39 user requirements were derived. Based on the results, relevant content of a basic student course was defined, structured and prioritized. The requirements and demands are summarized in a recommendation for action and further steps are presented.

This paper presents IART, a novel inertial wearable system for automatic detection of infringements and analysis of sports performance in race walking. IART algorithms are developed from raw inertial measurements collected by a single sensor located at the bottom of the vertebral column (L5–S1). Two novel parameters are developed to estimate infringements: loss of ground contact time and loss of ground contact step classification; three classic parameters are indeed used to estimate performance: step length ratio, step cadence, and smoothness. From these parameters, five biomechanical indices customized for elite athletes are derived. The experimental protocol consists of four repetitions of a straight path of 300 m on a long-paved road, performed by nine elite athletes. Over a total of 1620 steps (54 sequences of 30 steps each), the average accuracy of correct detection of loss of ground contact events is equal to 99%, whereas the correct classification of the infringement is equal to 87% for each step sequence, with a 92% of acceptable classifications. A great emphasis is dedicated on the user-centered development of IART: an intuitive radar chart representation is indeed developed to provide practical usability and interpretation of IART indices from the athletes, coaches, and referees perspectives. The results of IART, in terms of accuracy of its indices and usability from end-users, are encouraging for its usage as tool to support athletes and coaches in training and referees in real competitions.