User Perception Research Articles

Developing and Assessing a Scalable Digital Health Tool for Pretest Genetic Education in Patients With Early-Onset Colorectal Cancer: Mixed Methods Design.

National guidelines recommend germline genetic testing (GT) for all patients with early-onset colorectal cancer. With recent advances in targeted therapies and GT, these guidelines are expected to expand to include broader groups of patients with colorectal cancer. However, there is a shortage of genetic professionals to provide the necessary education and support for informed consent. As such, there is a pressing need to identify alternative approaches to facilitate and expedite access to GT. This study describes the development of a pretest education intervention, Nest-CRC, to facilitate the uptake of germline GT among patients with early-onset colorectal cancer. Patients with early-onset colorectal cancer and health care providers reviewed Nest-CRC, and their reactions and recommendations were captured using a nested mixed methods approach. Using the learner verification approach, we conducted 2 sequential phases of surveys and interviews with English- and Spanish-speaking patients with early-onset colorectal cancer and health care providers. The surveys assessed participants' experiences with genetic services and provided immediate feedback on the Nest-CRC genetic education modules. Semistructured interviews evaluated participants' perceptions of self-efficacy, attraction, comprehension, cultural acceptability, and usability of Nest-CRC. Survey data were analyzed using descriptive statistics (mean, median, and proportions), while interview data were analyzed through line-by-line coding of the transcribed interviews. After each phase, Nest-CRC was refined based on participants' recommendations. A total of 52 participants, including 39 patients with early-onset colorectal cancer and 13 providers, participated in the study. Of these, 19 patients and 6 providers participated in phase 1 (N=25), and 20 patients and 7 providers participated in phase 2 (N=27). Most participants (phase 1: 23/25, 92%, to 25/25, 100%; phase 2: 24/27, 89%, to 27/27, 100%) agreed that each of the 5 education modules was easy to understand and helpful; 13 patients reported no history of GT, with 11 (85%) expressing interest in GT and 2 (15%) remaining unsure after completing Nest-CRC. Participants reported that Nest-CRC provided sufficient information to help them decide about GT. The tool was deemed acceptable by individuals from diverse backgrounds, and participants found it visually attractive, easy to comprehend, and user-friendly. The findings revealed that Nest-CRC is a promising strategy for facilitating pretest education and promoting GT. Nest-CRC has been refined based on participant recommendations and will be re-evaluated.

Acceptance and adoption determinants of telemedicine in public healthcare institutions

Background: One of the challenges facing the usage of telemedicine technology in South Africa, particularly in the North West province (NWP), is lack of user acceptance by health care professionals which prevents piloted project to produce the desired outcomes.Objectives: The purpose of this study was to investigate the influential factors for the adoption of telemedicine by health care professionals from three selected hospitals (Bophelong, Taung and Klerksdorp) in the NWP.Method: The study adopted a case study approach and gathered data through questionnaires, which were distributed to the health care professionals of the three selected hospitals. In order to ensure that the instrument was accurate, a pilot study was carried out prior to the main investigation. Both the alpha and power values were set at 0.05 for the statistical analysis. The statistical tool used for the data analysis was SPSS v. 23.0.Results: Attitude towards the use of telemedicine technology (ATUTT), perceived usefulness (PU) and acceptance of telemedicine technology (ATT) were considered to be the influential factors in the adoption of telemedicine technology. The association between perceived ease of use (PEOU) and perceived usefulness (PU) (0.150, p = 0.034), PEOU and Attitude towards the use of telemedicine technology (ATUTT) (0.211, p = 0.002), PEOU and Acceptance of telemedicine technology (ATT) (0.245, p = 0.000), PU and (ATT) (0.212, p = 0.002), ATUTT and (ATT) (0.189, p = 0.005). However, PEOU was found to have an insignificant relationship with (0.048, p = 0.093). Hypotheses 1, 3, 4, 5, and 6 are supported while H2 was not supported.Conclusion: The study aims to fill the knowledge vacuum identified, helping poor countries effectively implement telemedicine technology to modernise the health care sector. In addition, results from this study shed insight on the varied impacts of individual, technical, clinical and multidimensional social influence variables on health care providers’ decisions to use telemedicine.Contribution: This study contributes to the body of knowledge by investigating the success factors for telemedicine technology adoption in South African public hospitals. These results have important implications for the public health care system in South Africa, both for the direction of future research and for the methods used to promote the use of telemedicine.

The Application of the Technology Acceptance Model and the Theory of Planned Behaviour in the Context of Green Banking

The implementation of green banking techniques in the banking sector is anticipated to yield advantages including minimised resource utilisation, lower operational expenses, and improved efficiency, while also fostering a smaller environmental impact. Green banking practices have environmental and financial benefits for the banking sector, but these benefits will only materialise if consumers adopt them. This research adopts the Technology Acceptance Model (TAM) developed by Davis (1989) and the Theory of Planned Behaviour (TPB) proposed by Ajzen (1991) to examine the interplay of External Variables, Perceived Ease of Use (PEOU), Perceived Usefulness (PU), and Perceived Behavioural Control (PBC). The external variables used are Time Availability, Convenience, and Social Norms. The SEM is applied to assess the significance of factors influencing perceived behavioural control about Green Banking. Online questionnaires were used to collect responses from 250 customers’ for the aim of this study. Results show that external variables, perceived ease of use, perceived usefulness significantly influences the perceived behavioural control

Investigating continuous intention to use metaverse in higher education institutions: a dual-staged structural equation modeling-artificial neural network approach

The current study explores metaverse adoption among higher education institutions (HEIs) in the light of a theoretical framework to empower future perspectives of the metaverse as a learning platform. Even though this technology was just recently introduced to the higher education sector, very few attempts have been made to evaluate its impact. The purpose of this research is to analyze the elements that influence the continuous intention (CI) to utilize the metaverse technology in learning. The technology acceptance model (TAM) and the self-determination theory (SDT) are both included in this study. A questionnaire was developed and distributed to students attending private universities in order to obtain the data that was needed for the proposed model. Using a hybrid approach that consists of partial least squares structural equation modeling (PLS-SEM) and an artificial neural network (ANN) model, which combines a linear PLS model with compensation and a nonlinear ANN model without compensation, the effect of CI on using the metaverse as a learning platform is investigated. This approach was chosen because it contains both of these types of models. When it comes to explaining the use of metaverse technology among students attending higher education institutions in Egypt, the research findings suggested that autonomy and perceived usefulness (PU) are major determinants. Nevertheless, the continuing intention was unaffected by the perceived ease of use (PEOU) of the product. Furthermore, according to the data provided by the ANN model, the most significant predictors are relatedness, PEOU, autonomy, and PU. It has been determined that the results obtained from the PLS-SEM and ANN modes are identical. Additionally, both theoretical and practical implications are discussed in this article.

Role of Cultural Graphics Strategies in Shaping Urban Identity: A Quantitative Study of User Perception in Qinzhou City

The paper attempts to investigate the role that street art, murals, and other signage play in forming and defining urban identity through research on Qinzhou City, Guangxi, China. The study employs a quantitative approach and structural equation modeling (SEM-PLS), which examines the interaction among cultural identity, social identity, media consumption, personal values, and beliefs and how these interact to affect cultural perception. Results of the study indicate that both cultural identity and social identity have significant influences on cultural perception, with media consumption and personal values and beliefs acting as critical mediators. Cultural Identity fosters the connection of personal beliefs strongly, whereas Social Identity tends to drive media consumption behaviors that affect cultural understanding. The study provides theoretical and practical implications for urban planners, policymakers, and media strategists in exploiting cultural graphics to improve urban cultural environments. Despite its limitations in geographical scope, the study provides useful insights into the intersection of cultural and social dynamics and offers a model for urban cultural policy and development strategies.

Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback

Job interviews play a critical role in shaping one's career, yet practicing interview skills can be challenging, especially without access to human coaches or peers for feedback. Recent advancements in large language models (LLMs) present an opportunity to enhance the interview practice experience. Yet, little research has explored the effectiveness and user perceptions of such systems or the benefits and challenges of using LLMs for interview practice. Furthermore, while prior work and recent commercial tools have demonstrated the potential of AI to assist with interview practice, they often deliver one-way feedback, where users only receive information about their performance. By contrast, dialogic feedback , a concept developed in learning sciences, is a two-way interaction feedback process that allows users to further engage with and learn from the provided feedback through interactive dialogue. This paper introduces Conversate, a web-based application that supports reflective learning in job interview practice by leveraging large language models (LLMs) for interactive interview simulations and dialogic feedback. To start the interview session, the user provides the title of a job position (e.g., entry-level software engineer) in the system. Then, our system will initialize the LLM agent to start the interview simulation by asking the user an opening interview question and following up with questions carefully adapted to subsequent user responses. After the interview session, our back-end LLM framework will then analyze the user's responses and highlight areas for improvement. Users can then annotate the transcript by selecting specific sections and writing self-reflections. Finally, the user can interact with the system for dialogic feedback, conversing with the LLM agent to learn from and iteratively refine their answers based on the agent's guidance. To evaluate Conversate, we conducted a user study with 19 participants to understand their perceptions of using LLM-supported interview simulation and dialogic feedback. Our findings show that participants valued the adaptive follow-up questions from LLMs, as they enhanced the realism of interview simulations and encouraged deeper thinking. Participants also appreciated the AI-assisted annotation, as it reduced their cognitive burden and mitigated excessive self-criticism in their own evaluation of their interview performance. Moreover, participants found the LLM-supported dialogic feedback to be beneficial, as it promoted personalized and continuous learning, reduced feelings of judgment, and allowed them to express disagreement.

Behavioural intention to adopt mobile trading apps: an integrated theoretical and digital framework, privacy concerns, and information richness model

This research investigates the transformative impact of mobile trading apps on the Indian financial landscape, particularly in the context of the unprecedented surge in DMAT (dematerialisation) accounts following the COVID-19 pandemic. Due to the advancement of online platforms and fast internet connectivity, stock exchanges across the globe have seen a dramatic inflow of retail investors and brokerage firms. The choice of using a particular mobile trading app draws significant importance because various factors determine the ability and ease of use of a specific app. The study underscores the role of FinTech services, particularly mobile trading apps, in revolutionizing stock trading by offering real-time access, increased trading activity, and enhanced features. Despite the proliferation of research on FinTech apps, a notable gap exists in understanding the adoption dynamics of mobile trading apps, especially in the Indian context. To address this gap, our research applies an adapted and extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT-3) framework to examine the factors influencing investors’ intentions and usage of mobile trading apps. We introduce novel elements such as information richness and privacy concerns, which are crucial in the financial domain. A convenient sample size of 573 actively brokerage app-using respondents was selected to investigate and conclude the consumers’ behavioural intention to use mobile trading apps. The findings highlight the significant impact of practical value, effort expectancy, social influence, hedonic motivation, trust, information richness, privacy concerns, facilitating conditions, and personal innovativeness in IT (Information technology) on investors’ intentions to use trading apps. These factors influence behavioural intentions and mediate the relationship between various constructs, emphasizing their multifaceted roles in shaping user perceptions. Theoretical implications of the research contribute to extending the UTAUT-3 model and providing a comprehensive framework for examining technology adoption in the financial domain. Moreover, practical implications guide developers, financial institutions, and policymakers in creating secure, user-friendly, and information-rich mobile trading systems. While acknowledging sampling and self-reported data limitations, this research lays the groundwork for future longitudinal studies. It encourages the exploration of diverse FinTech services to gain a holistic understanding of adoption dynamics in the evolving financial technology landscape. This study adds empirical knowledge to mobile trading app adoption and catalyzes further research, shaping the trajectory of FinTech studies and practical applications in the ever-evolving financial ecosystem.

Differences in User Perception of Artificial Intelligence-Driven Chatbots and Traditional Tools in Qualitative Data Analysis

Qualitative data analysis (QDA) tools are essential for extracting insights from complex datasets. This study investigates researchers’ perceptions of the usability, user experience (UX), mental workload, trust, task complexity, and emotional impact of three tools: Taguette 1.4.1 (a traditional QDA tool), ChatGPT (GPT-4, December 2023 version), and Gemini (formerly Google Bard, December 2023 version). Participants (N = 85), Master’s students from the Faculty of Electrical Engineering and Computer Science with prior experience in UX evaluations and familiarity with AI-based chatbots, performed sentiment analysis and data annotation tasks using these tools, enabling a comparative evaluation. The results show that AI tools were associated with lower cognitive effort and more positive emotional responses compared to Taguette, which caused higher frustration and workload, especially during cognitively demanding tasks. Among the tools, ChatGPT achieved the highest usability score (SUS = 79.03) and was rated positively for emotional engagement. Trust levels varied, with Taguette preferred for task accuracy and ChatGPT rated highest in user confidence. Despite these differences, all tools performed consistently in identifying qualitative patterns. These findings suggest that AI-driven tools can enhance researchers’ experiences in QDA while emphasizing the need to align tool selection with specific tasks and user preferences.

The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advanced technology

This study explored the role of technology systems in influencing nurses’ intentions to adopt medical applications that enhance their performance and how technology contributes to improvements in hospital systems. The study examines the intention to use technology through the mediating effects of perceived usefulness and perceived ease of use, with technology sophistication. A random sampling method was employed to gather 687 responses from nurses. The statistical analysis was conducted using AMOS version 25.0 and SPSS. The findings indicate a significant association between technology sophistication (TS), perceived usefulness (PU), perceived ease of use (PEU), and intention to use (IU). Additionally, PU and PEU positively mediate the relationship between TS and IU. This research will benefit policymakers aiming to enhance nurses’ performance by adopting modern technology. Authorities should consider introducing advanced technology systems to meet the goals of hospital administration and support nurses effectively.

The impact of labeling automotive AI as trustworthy or reliable on user evaluation and technology acceptance

This study explores whether labeling AI as either “trustworthy” or “reliable” influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as “trustworthy” did not significantly influence people’s judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions. The study provides insights into how specific labels affect adopting certain perceptions of AI technology.

Human–Robot Interactions: A Pilot Study of Psychoaffective and Cognitive Factors to Boost the Acceptance and Usability of Assistive Wearable Devices

Robotic technology to assist rehabilitation provides practical advantages compared with traditional rehabilitation treatments, but its efficacy is still disputed. This controversial effectiveness is due to different factors, including a lack of guidelines to adapt devices to users’ individual needs. These needs include the specific clinical conditions of people with disabilities, as well as their psychological and cognitive profiles. This pilot study aims to investigate the relationships between psychological, cognitive, and robot-related factors playing a role in human–robot interaction to promote a human-centric approach in robotic rehabilitation. Ten able-bodied volunteers were assessed for their anxiety, experienced workload, cognitive reserve, and perceived exoskeleton usability before and after a task with a lower-limb exoskeleton (i.e., 10 m path walking for 10 trials). Pre-trial anxiety levels were higher than post-trial ones (p < 0.01). While trait anxiety levels were predictive of the experienced effort (Adjusted-r2 = 0.43, p = 0.02), the state anxiety score was predictive of the perceived overall workload (Adjusted-r2 = 0.45, p = 0.02). High–average cognitive reserve scores were predictive of the perception of exoskeleton usability (Adjusted-r2 = 0.45, p = 0.02). A negative correlation emerged between the workload and the perception of personal identification with the exoskeleton (r = −0.67, p-value = 0.03). This study provides preliminary evidence of the impact of cognitive and psychoaffective factors on the perception of workload and overall device appreciation in exoskeleton training. It also suggests pragmatic measures such as familiarization time to reduce anxiety and end-user selection based on cognitive profiles. These assessments may provide guidance on the personalization of training.

Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques.

Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user's perception. As smartphones are widely used and come with high-quality cameras, a popular one was used for capturing images for this study. This study aims to predict Munsell soil colour (MSC) from the Munsell soil colour book (MSCB) by using deep learning techniques on mobile-captured images. MSCB contains 14 pages and 443 colour chips. So, the number of classes for chip-by-chip prediction is very high, and the captured images are inadequate to train and validate using deep learning methods; thus, a patch-based mechanism was proposed to enrich the dataset. So, the course of action is to find the prediction accuracy of MSC for both page level and chip level by evaluating multiple deep learning methods combined with a patch-based mechanism. The analysis also provides knowledge about the best deep learning technique for MSC prediction. Without patching, the accuracy for chip-level prediction is below 40%, the page-level prediction is below 65%, and the accuracy with patching is around 95% for both, which is significant. Lastly, this study provides insights into the application of the proposed techniques and analysis within real-world soil and provides results with higher accuracy with a limited number of soil samples, indicating the proposed method's potential scalability and effectiveness with larger datasets.

Attention-Aware Visualization: Tracking and Responding to User Perception Over Time.

We propose the notion of attention-aware visualizations (AAVs) that track the user's perception of a visual representation over time and feed this information back to the visualization. Such context awareness is particularly useful for ubiquitous and immersive analytics where knowing which embedded visualizations the user is looking at can be used to make visualizations react appropriately to the user's attention: for example, by highlighting data the user has not yet seen. We can separate the approach into three components: (1) measuring the user's gaze on a visualization and its parts; (2) tracking the user's attention over time; and (3) reactively modifying the visual representation based on the current attention metric. In this paper, we present two separate implementations of AAV: a 2D data-agnostic method for web-based visualizations that can use an embodied eyetracker to capture the user's gaze, and a 3D data-aware one that uses the stencil buffer to track the visibility of each individual mark in a visualization. Both methods provide similar mechanisms for accumulating attention over time and changing the appearance of marks in response. We also present results from a qualitative evaluation studying visual feedback and triggering mechanisms for capturing and revisualizing attention.

Determinants of student engagement and behavioral intention towards mobile learning platforms

This study explores the factors influencing student engagement and behavioral intention towards mobile learning platforms, with a focus on widely used platforms in Indonesia, such as Ruangguru, Zenius, and Quipper. A total of 375 questionnaires were distributed, out of which 363 were deemed valid and used for analysis. The research employed structural equation modeling with partial least squares to analyze the data, aiming to uncover the key determinants driving the adoption of mobile learning. The findings highlight the significant impact of perceived usefulness (PU) on students’ attitudes toward mobile learning, emphasizing the crucial role of perceived utility in shaping positive attitudes. However, the study also reveals that the direct influence of PU on behavioral intention towards mobile learning is minimal, suggesting that attitude toward mobile learning plays a critical mediating role in this relationship. Additionally, the study demonstrates that perceived entertainment and facilitating conditions have substantial effects on shaping attitudes and behavioral intentions, underscoring the importance of enjoyment and support systems in fostering student engagement. The structural model developed in this research offers strong explanatory power, providing a comprehensive understanding of the factors that contribute to the success of mobile learning platforms. The insights gained from this study offer valuable guidance for educators and developers seeking to enhance mobile learning experiences and improve educational outcomes through targeted interventions that address these key determinants.

Exploring road users' awareness and perceptions of advanced traveler information systems (ATIS) in Kumasi, Ghana

Exploring road users' awareness and perceptions of advanced traveler information systems (ATIS) in Kumasi, Ghana

"Just-in-time" but a bit delayed: Personalizing digital nudges for healthier online food choices.

As food choices are increasingly made in contexts such as online supermarkets, nudging has been extrapolated to the digital sphere. Digitalization poses unique opportunities to enhance the promotion of healthier food choices online: Digital nudges can be delivered "just-in-time" (JIT), in response to the initial selection of an unhealthy product. Furthermore, digital JIT nudges can be personalized to match user characteristics of behavioral relevance, such as one's food and cognitive processing preferences. We examined whether personalizing by user-matching digital JIT nudges on content (i.e., emphasizing health versus price) and/or type (i.e., a text-based versus an image-based nudge) would increase nudge effectiveness and healthier food purchases, and assessed psychological mechanisms potentially underlying these enhanced effects. In a 2 (Nudge Content: Non-personalized versus Personalized) by 2 (Nudge Type: Non-personalized versus Personalized) randomized controlled trial, 200 healthy participants completed an online grocery shopping task on a mock supermarket app in which they first selected products from 10 different categories, followed by a checkout screen where they could replace products in the shopping basket. Personalizing nudge type increased nudge effectiveness (i.e., the proportion of accepted nudges when choosing products from categories). Personalizing nudge content seemed to exert a delayed effect of increasing healthier product replacements prior to checkout. User perceptions of JIT nudges did not vary with personalization. However, regardless of personalization, all JIT nudging conditions had more final healthier food purchases and greater satisfaction with food choices compared to the control condition. Collectively, this pre-registered "proof-of-principle" study demonstrates that personalizing the content and type of digital JIT nudges yields small positive benefits on the healthiness of online food choices and attests to the utility of JIT interventions for health promotion.

Analysis of the Effect of Website Quality on Customer Satisfaction Using the Webqual 4.0 Method Case Study: Villa Merah Picture Tutoring

This research aims to analyze the quality of the Villa Merah Picture Tutoring website on customer satisfaction using the Webqual 4.0 method. Using a quantitative approach, data was collected through a questionnaire distributed to users of the tutoring website. The Likert scale measures user perceptions of various aspects of website quality and customer satisfaction. Analysis was carried out to evaluate website quality based on the dimensions determined by the Webqual 4.0 method and to identify the relationship between website quality and customer satisfaction. It is hoped that the research results will provide better insight into the factors that influence customer trust in online tutoring services and provide guidance for business owners in improving website quality and strengthening relationships with customers.

Enhancing road safety of PLEVs by novel vehicle concepts: A comprehensive investigation on regulations, accident statistics, and perception of riders in Europe.

The rise of Personal Light Electric Vehicles (PLEVs), including electric bicycles and electric kick scooters, represents a relevant trend in current urban mobility. PLEVs offer economic, social, and environmental advantages, making them increasingly attractive for short-distance travel. Despite their benefits, concerns about the safety of PLEVs, particularly related to road accidents, have arisen due to their growing presence in urban areas. This paper examines various aspects of PLEV safety, focusing on equipment required by regulations, circulation aspects, and users' needs; based on such highlights, the objective is to derive a vehicle concept (in the form of a kick scooter) that integrates elements for PLEV safety maximization starting from the design phase. A review of PLEV-related regulation in European countries, and particularly for electric kick scooters, is initially provided to determine the mandatory equipment for their circulation in the widest possible number of countries. Analysis of accident data for PLEVs in the EU then reveals critical factors contributing to the occurrence of crashes, injuries, and casualties, as well as the type and severity of riders' injuries. Surveys on user perceptions shed light on safety concerns of daily riders, indicating the most relevant aspects to enhance from the user's perspective. Based on these findings, the study proposes a novel electric kick scooter concept integrating safety features derived from regulations, accident analysis, and user needs. Safety equipment aimed at increasing riding stability (high diameter wheel, pneumatic tyres) appears as the most promising solution for maximizing PLEV safety. Inclusion of turning light indicators can also increase riding stability, since the user can exhibit his/her intention of turning while maintaining the contact with the handlebar; as of today, this latter feature is mandatory only in Italy among all EU countries. The work additionally emphasizes the need for harmonized regulations, enhanced safety standards, specifically designed infrastructure, and user awareness campaigns to improve PLEV safety. This research is functional for the proposal of a sustainable and safe transportation solution that can increase the consensus towards PLEVs and the consequent spread of electric microvehicles.

Establishing thresholds for swing transparency at the knee during gait to inform exoskeleton design.

Knee exoskeletons have been developed to assist, stabilize, or improve human movement or recovery. However, exoskeleton designers must implement transparency (i.e., get out of the way) modes during the swing phase of locomotor tasks to avoid impeding movement. The problem is that it is not understood how sensitive people are to small knee torques or what level of knee impedance is acceptable (sufficiently transparent) during swing phase. Here, we (i) characterized the biomechanical consequences of knee stiffness and damping during swing, and (ii) leveraged user perceptions of being impeded and toe clearance to define transparency thresholds, below which the participants were sufficiently unimpeded during the swing phase of gait. We conducted a series of human subject experiments that involved walking and stair ascent/descent while wearing a modified knee brace with five stiffness values ranging from 0 to 4 Nm/rad and five damping values ranging from 0 to 0.77 Nm/rad/s. We measured changes to lower limb kinematics, knee flexor muscle activity, and participants' perception of being impeded during swing. Kinematics, muscle activity, and perceived impedance all changed in response to added stiffness and damping. For stiffness, we found the median transparency thresholds for walking and stairs to be 1.76 Nm/rad and 2.95 Nm/rad, respectively, which corresponds to peak knee moments during swing of around 2.3 and 5 Nm. For damping, we found the median transparency threshold for walking and stairs to be about the same, 0.29 Nm/rad/s, which corresponds to peak knee moments during swing of around 2.3 Nm. These values provide useful benchmarks for defining quantitative design requirements for knee exoskeletons intended for locomotor activities.

Impact of Regional Structure and Topography on the Effectiveness of Public Transportation Services

This study investigates the impact of urban structure and topography on public transportation effectiveness in developing countries. Using structural equation modeling analysis of survey data from 500 public transit users across five diverse cities, we examine how these geographical factors influence accessibility, reliability, comfort, and user satisfaction. Results indicate that both urban structure and topography significantly affect transportation effectiveness, with urban structure having a slightly stronger influence. Polycentric urban designs and moderate topography were associated with higher public transit effectiveness. User perceptions highlighted the importance of integrated planning approaches. The findings suggest policymakers should consider geographical contexts when designing public transportation systems, potentially through adaptive infrastructure and context-specific service models. This research contributes to a more nuanced understanding of public transit planning in varied urban environments of developing nations.