User Interface Research Articles

Enfoque multitareas implementado en una minicomputadora para el control y monitoreo de un motor a pasos industrial

This paper presents the implementation of the multitasking approach on a Raspberry Pi single board minicomputer, with experimental results, of the control and monitoring of an industrial type Nema 34 stepper motor through a user interface that allows the configuration of the control operation and data visualization to interact with the physical devices. One of the tasks consists of generating a pulse width modulation that is sent to the DQ860HA driver to rotate the stepper motor considering 400 pulses per revolution, while the second task simultaneously counts the number of pulses produced by an encoder with a resolution of 100 pulses per revolution, which is coupled to the motor shaft. The use of low-cost computer equipment and open-source software makes the presented development can be considered as a frugal technology application for the control and monitoring of an industrial system, with experimental results that validate the correct operation of two tasks that must be executed simultaneously.

Capabilities of cellebrite universal forensics extraction device in mobile device forensics

The powerful digital forensics tool cellebrite universal forensics extraction device (UFED) extracts and analyzes mobile device data, helping investigators solve criminal and cybersecurity cases. Advanced methods and algorithms allow Cellebrite UFED to recover data from erased or obscured devices. Cellebrite UFED can pull data from call logs, texts, emails, and social media, providing valuable evidence for investigations. The use of smartphones and tablets in personal and professional settings has spurred the development of mobile device forensics. The intuitive user interface speeds up data extraction and analysis, revealing crucial information. It can decrypt encrypted data, recover deleted files, and extract data from multiple devices. The sector's best data extraction functionality, Cellebrite UFED, helps forensic analysts gather crucial evidence for investigations. Legal and ethical considerations are crucial in mobile device forensics. Legal considerations include allowing access to data, protecting privacy, and adhering to chain of custody protocols. Ethics include transparency, defamation, and information exploitation protection. Using Cellebrite UFED, researchers can navigate complex data on mobile devices more efficiently and precisely. Artificial intelligence (AI) and machine learning (ML) algorithms may automate data extraction in future tools. Examiners must train, maintain, and establish clear protocols for using Cellebrite UFED in forensic investigations.

Real-time segmentation and classification of whole-slide images for tumor biomarker scoring

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process.

A parameterized modeling method for analyzing welding residual stress in orthotropic steel deck

This paper presents a new parameterized method for establishing OSD models and analyzing WRS, and two plugins are developed based on the graphical user interface (GUI) of Abaqus. This parameterized method addresses the extremely high cost of manual modeling of OSD in Abaqus. In the two presented plugins, the OSD dimensions and welds size, the material properties, the welding heating source, and the mesh properties are parameterized, and the OSD model can be created automatically by the plugins without users’ pre-processing operations in Abaqus, which significantly facilitates the OSD modeling and the WRS analysis. The plugins developed in this study can provide the WRS analysis in the OSD with any dimensions and welding technologies, and the applicability of the plugins is verified by the consistency between the simulated WRS analysis results by plugins and those reported in the literature.

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

BiodiViz: Leveraging NER and RE for Automated Knowledge Graph Generation in Biodiversity Research

In biodiversity research, the integration of machine learning and data visualization is increasingly important for uncovering valuable insights from academic literature. This study introduces an innovative knowledge graph application, BiodiViz, designed to translate intricate text into intuitive visual representations, fostering a deeper comprehension of biodiversity relationships. BiodiViz uses the top-performing Named Entity Recognition (NER) and Relation Extraction (RE) models to automatically generate a comprehensive knowledge graph for biodiversity research. The NER model extracts and categorizes entities like organisms, phenomena, and habitats, while the RE model identifies relationships such as "have," "occur in," and "influence" from the BiodivNERE dataset (Abdelmageed et al. 2022). These entities and relationships are organized into nodes and edges within a graph. Researchers input text into BiodiViz, producing a visual knowledge graph that simplifies the analysis of complex biodiversity data, reducing manual effort and enhancing efficiency. Named Entity Recognition & Relation Extraction BiodiViz leverages advanced Bidirectional Encoder Representations from Transformers (BERT)-based Large Language Models (LLMs) (Rogers et al. 2020), fine-tuned specifically for NER and RE tasks using the BiodivNERE dataset. The fine-tuning process involved various models, including BERT (Devlin et al. 2019), ELECTRA (Clark et al. 2020), and BiodivBERT (Abdelmageed et al. 2023). These models were evaluated for performance using the results of their F1-score as the main metric, which is the harmonic mean of precision (the proportion of true positive results among all positive predictions) and recall (the proportion of true positive results among all actual positives), with BiodivBERT achieving an F1-score of 77.16% for the NER task, while BERT excelled in the RE task with an F1-score of 81.28%. Rigorous hyperparameter optimization further enhanced the performance of BiodivBERT in the RE task by 3.38%. The BiodivNERE corpora by Abdelmageed et al. (2022) were used to fine-tune several models for NER and RE tasks in the biodiversity domain. The first corpus from the BiodivNERE corpora is BiodivNER, which is a gold standard dataset (manually labelled test corpora) for evaluating NER tasks. The fine-tuning process employed the token classification method from the Hugging Face library (Hugging Face 2023b), which assigns labels to each token in a sequence. Experiments were conducted with a batch size of four, meaning the model processes four examples/rows of data at a time before making an update to improve its learning. This is due to the constraints of the NVIDIA® GeForce RTX™ 3060 graphics processor. (NVIDIA 2024) Model performance was evaluated using the seqeval library (Nakayama 2018), focusing on accuracy, precision, recall, and F1 scores. For text classification, the second corpus, BiodivRE, was utilized, following previous research recommendations to explore fine-tuning settings for BiodivBERT. Hyperparameter optimization (Feurer and Hutter 2019) was conducted using Hugging Face’s Trainer API with an Optuna backend (Hugging Face 2023a), concentrating on learning rate and the number of training epochs (i.e., the number of complete passes through the entire dataset during model training). The BiodiViz Knowledge Graph Application The fine-tuned NER and RE models with the best F1-scores—BiodivBERT and BERT, respectively—were integrated into the knowledge graph application. Fig. 1 illustrates the flowchart of the application pipeline. Each sentence in the input text will go through the NER model to identify and label the entities within the sentence. Subsequently, these labeled entities, together with the original sentence, will be input into the RE model. The RE model will analyze every pair of entities for a potential relation and output the type of relation they share. The application will then utilize this data to create a graph with appropriate labels and color-coding. An example of the application's user interface with the knowledge graph is shown in Fig. 2. This study highlights the practical application of machine learning and data visualization in advancing biodiversity research, emphasizing the importance of developing user-friendly tools to support scientific exploration and discovery. The BiodiViz application, including the code and resources, is available on GitHub*1, providing an accessible tool for biodiversity researchers to streamline their analyses.

User technology for AI-supported objective measurement of Work-related Quality of Life (MI-LQ)

Abstract Background Understanding and enhancing job satisfaction is crucial for improving Work-Related Quality of Life (WrQoL) in today’s office work environments. Our work uses multimodal data collection and analysis techniques to objectively assess WrQoL, thus mitigating respondent bias and survey fatigue. Leveraging machine learning (ML) and automated methods measuring WrQoL presents a promising approach in public health research, particularly concerning stress-related health outcomes. Methods Through a systematic literature search of 9,242 publications to identify WrQoL indicators, we selected over 100 publications for data extraction. A base model, including workload, commute, overtime, valence, and arousal was formulated. To amass a wide data set, Objective measurement tools (computers, smartphones, and wearables) complemented by questionnaire data. This data will inform ML algorithms to predict individual WrQoL indicators. Developed as an input/output data platform, an app-prototype will objectively assess WrQoL, facilitating a field study with office workers to refine the model. A mobile app prototype serves as a user interface and data relay for WrQoL metric analysis. Results Our research develops instruments for measuring WrQoL using smartphones, wearables, and specialized trackers for heart rate variability (HRV) analysis, alongside a software prototype for keyboard and mouse tracking for workload assessment of valence and arousal. Fitness trackers with photoplethysmography functionality are favored for HRV assessment. Conclusions The systematic search identified influences on WrQoL, translated into measurable indicators in a base model. Leveraging user tech for assessment and a ML and mobile app framework, this approach facilitates objective WrQoL determination. By presenting the relationship between digital service use and WrQoL indicators, this methodology promises new insights, informing stress and burnout prevention strategies within public health initiatives. Key messages • Progressive approach: ML-driven WrQoL assessment offers promising avenues for stress prevention in workplace health research. • Methodological Advancement: The novel framework for WrQoL assessment using digital tools contributes to effective workplace health promotion strategies, with potential for tailored interventions.

Accessibility engineering in the mhealth industry - A multi-case study

Abstract Medical apps, i.e. apps that fall under the EU Medical Device Regulation (MDR), are increasingly becoming a relevant part of healthcare in European countries. This is particularly evident in the numerous partnerships between app vendors and insurers as well as in the launches of national reimbursement programs. However, when it comes to enabling as many patients as possible to benefit from the medical advantages associated with the use of these apps, funding is only one part of the equation. It is up to the app vendors to develop barrier-free user interfaces so that people - regardless of individual characteristics such as cognitive abilities, physical limitations or language skills - can interact successfully with the app. Little is known about how these companies approach the topic of accessibility, what challenges they face and how big the impact of medical device legislation and other regulations on accessibility is. Against this background, we are undertaking a case study in several companies offering apps on the German market that are certified as medical devices and targeted at patients. Through interviews with employees with various roles (e.g. business, engineering, design) we are aiming at reconstructing the development processes of the apps with a focus on accessibility. We plan to expand the results by interviews with experts, e.g., in the area of MDR conformity assessment. 20 interviews in four companies have revealed that accessibility only plays a minor role during development. As a result, the topic is not systematically addressed, but only a few features are implemented. The app’s compatibility with screen reader or voice control was not explicitly addressed in any of the companies. There was also no deliberate involvement of people with disabilities in user studies. The preliminary results also indicate that the influence of medical device legislation on the implementation of accessibility is negligible. Key messages • User interface accessibility has a very low priority in the mhealth companies we have examined so far. • The current regulations are not sufficient in enforcing the basic implementation of accessibility features requiring more guidance and detailed regulations, given our sample of companies.

A comparative ensemble approach to bedload prediction using metaheuristic machine learning

An adequate bedload prediction is a challenging task in hydraulic engineering because of the complex sediment transport processes and corresponding environmental factors. The current study introduces a novel comparative ensemble approach using metaheuristic machine learning (ML) models to enhance the accuracy of bedload prediction using data from laboratory flume experiments. To uncover key insights in bedload transport prediction, several models are employed, such as K-Nearest Neighbours (KNN), Extra Trees Regressor (ETR), Linear Regression (LR), Random Forest (RF), Bagging Regressor (BR), and XGBoost (XGB). The coefficient of determination (R2) and root mean square error (RMSE) values vary between various models; however, XGB showed R2 = 0.99 and RMSE = 0.11. The sensitivity analysis emphasizes the crucial role of the Shields parameter in bedload prediction, while the SHAP analysis highlights the substantial influence of the XGB model in enhancing predictive accuracy. The REC curves show that BR, XGB, and RF, outperformed KNN and LR models. Furthermore, a graphical user interface has also been developed to facilitate user interaction with the predictive models, allowing for easier visualization, analysis, and interpretation of bedload transport predictions. Additionally, k-fold cross-validation was performed to assess the performance, consistency and robustness of the ML models. Thus, it can be concluded from the current study that the utilization of ML algorithms can improve accuracy, providing valuable insights for hydraulic engineers and highlighting the importance of ML models in civil engineering practices, particularly in bedload transport prediction.

MODVORTEx: Computer Vision-Driven Automation for Magnetic Domain Wall Velocity Analysis

Abstract This paper presents Magneto Optic Domain Velocity Observation and Real-Time Extraction (MODVORTEx), a comprehensive software solution for automating domain wall (DW) velocity measurements in magnetic materials using magneto-optic Kerr effect (MOKE) microscopy. Building upon our previous work on bubble domain structures, we introduce a versatile graphical user interface (GUI) that accommodates arbitrary domain shapes and employs advanced computer vision techniques. The software provides different methods for DW detection and velocity calculation, catering to various domain structures of arbitrary shape. Our approach significantly reduces the time and effort required for data extraction, transforming a process that previously took days of manual work into a task completable within minutes. We provide the details of the algorithmic implementation which is organized into pre-processing, DW detection, displacement measurement, and velocity extraction. This tool is applicable to a wide range of scenarios, including bubble domain dynamics, Dzyaloshinskii-Moriya interaction studies in perpendicular magnetic anisotropy systems, current-driven DW motion in patterned strips etc. By providing both GUI and Application Programing Interface (API), our software offers flexibility for integration into existing measurement systems and adaptability for specific research needs. This automation promises to accelerate research in spintronics and magnetic materials, enabling more comprehensive and accurate studies of DW dynamics.

Improving Teacher Training Through Emotion Recognition and Data Fusion

ABSTRACTThe quality of education hinges on the proficiency and training of educators. Due to the importance of teacher training, the innovative platform Teacher Moments creates simulated classroom scenarios. In this scenario‐based learning, confusion is an important indicator to detect users who struggle with the simulations. Through Teacher Moments, we gathered 7975 audio recording responses from participants who self‐labelled their recordings according to whether they sounded confused. Our dataset stands out for its size, for not including actor‐generated audio, and for measuring confusion, a neglected emotion in artificial intelligence (AI). Our experiments tested unimodal approaches and feature‐level, model‐level and decision‐level fusion. Feature‐level fusion demonstrated superior performance to unimodal methods, achieving a balanced accuracy of 0.6607 on the test set. This outcome highlights the necessity for further investigation in the overlooked area of confusion detection, particularly employing realistic datasets like the one used in this study and exploring new methods. Beyond teacher training, the insights of this research also extend to other directions, such as other professionals making critical decisions, user interface design or adaptive learning systems.

Study on the Design of a Non-Contact Interaction System Using Gestures: Framework and Case Study

The interaction patterns are constantly changing as the rapid development in technology. The non-contact interaction system using gestures is becoming important in human-computer interaction nowadays. This paper illustrates a design framework of a non-contact interaction system using gestures, which fully considers the approach to fit non-contact gestures into the system. The corresponding user requirements were investigated by surveys, and the universal design guide of non-contact gestures was summarized by statistical analysis. A case study was illustrated for key points of the developed design framework. The scheme, including interaction architecture, gesture definition, and user interface design, was described. The rapid prototyping was built. Evaluations, including eye-tracking measurements and tests of system usability, were implemented and analyzed by statistical methods to present suggestions of iterative design. The feasibility of the developed framework was verified. By statistical methods, the relationship between the interaction usability and the user’s perception for information was discussed, and the user’s perception for the display of dynamic and static gestures was analyzed. The results provide a clear approach to satisfy user’s preferences and improve the interaction quality for non-contact interaction systems using gestures, and furnish empirical grounding for sustainably devising similar interaction systems.

Evaluation of root canal filling length on periapical radiograph using artificial intelligence.

This work proposes a novel method to evaluate root canal filling (RCF) success using artificial intelligence (AI) and image analysis techniques. 1121 teeth with root canal treatment in 597 periapical radiographs (PARs) were anonymized and manually labeled. First, RCFs were segmented using 5 different state-of-the-art deep learning models based on convolutional neural networks. Their performances were compared based on the intersection over union (IoU), dice score and accuracy. Additionally, fivefold cross validation was applied for the best-performing model and their outputs were later used for further analysis. Secondly, images were processed via a graphical user interface (GUI) that allows dental clinicians to mark the apex of the tooth, which was used to find the distance between the apex of the tooth and the nearest RCF prediction of the deep learning model towards it. The distance can show whether the RCF is normal, short or long. Model performances were evaluated by well-known evaluation metrics for segmentation such as IoU, Dice score and accuracy. CNN-based models can achieve an accuracy of 88%, an IoU of 79% and Dice score of 88% in segmenting root canal fillings. Our study demonstrates that AI-based solutions present accurate and reliable performance for root canal filling evaluation.

Interactive Simulation of Nonpharmaceutical Interventions of Airborne Disease Transmission in Office Settings

The COVID-19 pandemic has caused major disruptions to workplace safety and productivity. A browser-based interactive disease transmission simulation was developed to enable managers and individuals (agents) to optimize safe office work activities during pandemic conditions. The application provides a user interface to evaluate the impact of non-pharmaceutical interventions (NPIs) policies on airborne disease exposure based on agents’ meeting patterns and room properties. Exposure is empirically calibrated using CO2 as a proxy for viral aerosol dispersion. For the building studied, the major findings are that the cubicles during low occupancy produce unexpectedly high exposure, upgrading meetings to larger rooms reduces total average exposure by 44%, and when all meetings are conducted in large rooms, a 79% exposure reduction is realized.

Comparative Bladder Cancer Tissues Prediction Using Vision Transformer.

Bladder cancer, often asymptomatic in the early stages, is a type of cancer where early detection is crucial. Herein, endoscopic images are meticulously evaluated by experts, and sometimes even by different disciplines, to identify tissue types. It is believed that the time spent by experts can be utilized for patient treatment with the creation of a computer-aided decision support system. For this purpose, in this study, it is evaluated that the performances of three models proposed using the bladder tissue dataset. The first model is a convolutional neural network (CNN)-based deep learning (DL) network, and the second is a model named hybrid cnn-machine learning (ML) or DL + ML, which involves classifying deep features obtained from a CNN-based network with ML. The last one, and the one that achieved the best performance metrics, is a vision transformer (ViT) architecture. Furthermore, a graphical user interface (GUI) is provided for an accessible decision support system. As a result, accuracy and F1 score values for DL, DL + ML, and ViT models are 0.9086-0.8971-0.9257 and 0.8884-0.8496-0.8931, respectively.

AgriScan: Next.js powered cross-platform solution for automated plant disease diagnosis and crop health management

Plant diseases present a formidable challenge to the agricultural sector worldwide, leading to significant losses, with the US experiencing annual losses amounting to one-third of crop production. Diagnosis of crop diseases through optical observation of leaf symptoms is particularly daunting for farmers with limited resources. Therefore, there is an urgent need for enhanced detection, monitoring, and prediction methods to mitigate agricultural losses effectively. Harnessing the power of computer vision and deep learning, this paper introduces a cross-platform system designed to automate plant leaf disease diagnosis. The system employs convolutional neural networks to classify 46 disease categories, trained on a dataset comprising 96,206 images of healthy and infected plant leaves. The user interface, accessible across multiple platforms including Android, iOS, Windows, and Linux, allows farmers to capture photos of infected leaves and receive real-time disease classification along with confidence percentages. By empowering farmers to maintain crop health and prevent the application of incorrect fertilizers, the system aims to optimize crop productivity. Performance evaluation includes metrics such as classification accuracy and processing time, with the model achieving an impressive overall accuracy of 93.45% across 46 common disease classes spanning 16 crop species.

Hey Building! Novel Interfaces for Parametric Design Manipulations in Virtual Reality

Parametric Design enables designers to formulate and explore new ideas through parameters, typically by manipulating numerical values. However, visualising and exploring the design space of an established parametric design solution is natively difficult through desktop displays for designers due to screen space constraints and requiring familiarity with visual-language programming interfaces. Thus, we sought to explore Virtual Reality (VR), inspired by Natural User Interfaces (NUI), to develop and explore new interfaces departing from traditional programming interfaces, that could complement the spatial and embodied affordances of contemporary VR devices. Informed by two industry-led focus groups with architects we developed and examined the usability of three different interfaces: 1) Paramaxes , an axes-based interface that allows designers to distribute and manipulate parameter visualisations around them in physical space; 2) ParamUtter , a Voice-based User Interface (VUI) that allows designers to manipulate parameter visualisations through natural languages; 3) Control Panel , which presents the parameters as sliders in a scrollable pane and acts as baseline comparison. We ran an exploratory study with experts and found that the Control Panel was ultimately the preferred interface for a design manipulation task. However, participants commented favorably towards qualities in the unconventional interfaces, with ParamUtter scoring highest in System Usability Scores (SUS), and participants valuing the potential of using physical space to explore design spaces with Paramaxes .

Research on the detection and recognition system of target vehicles based on fusion algorithm

<p>In modern society, the monitoring of vehicles is paramount for upholding traffic safety and order. This paper delves into a Graphical User Interface (GUI)-driven system for vehicle target detection and integration. This system seamlessly integrates three pivotal functionalities: vehicle type recognition, license plate recognition, and driver face recognition. It automates the vehicle inspection assessment process through an intuitive user interface. Specifically, for license plate recognition, the system leverages traditional computer vision techniques, including color and grayscale processing, radon transform, morphological operations, edge detection, character segmentation, and character recognition. The driver face recognition module incorporates methods such as image gray scaling, Gaussian filtering for denoising, face detection, and feature extraction. Meanwhile, vehicle type recognition is achieved by extracting Histogram of Oriented Gradients (HOG) features and utilizing a Support Vector Machine (SVM) classifier. Experimental findings highlight the system’s remarkable recognition accuracy, offering a highly efficient and dependable solution for the automated inspections of vehicles.</p>

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

Digital Storytelling for People With Cognitive Impairment Using Available Mobile Apps: Systematic Search in App Stores and Content Analysis.

Growing evidence suggests cognitive and social health benefits can be derived from digital storytelling for older adults with cognitive impairment. Digital storytelling apps offer the potential to serve as an on-demand, easy-to-access platform for enhancing cognitive abilities and promoting social well-being. Yet, despite the increasing quantity of such apps being available on the market, there is a gap in research investigating their quality. This app review aims to assess the digital storytelling apps available in the Chinese market and evaluate them in accordance with the Mobile Application Rating Scale (MARS). The goal was to identify key features and evaluate the overall quality in the context of cognitively impaired users. A systematic search was conducted in both the Google Play store (Google LLC) and iTunes store (Apple Inc), using English and Chinese keywords. Apps were chosen according to specific criteria that included features, including (but not limited to) memory capture, story saving, cue-based reminiscing, and the ability to share stories or memories with others. The MARS was used by 3 individual researchers to independently assess app quality across several domains, such as engagement, functionality, aesthetics, and information quality, for both Android and iOS apps. From an initial screening of 297 apps, only 9 (3%) met the criteria for detailed evaluation using MARS. The reviewed apps featured capture memory, save, reminisce, and share functions, which are critical in supporting cognitive functions and enhancing user engagement. The analysis revealed patterns in platform diversity and geographical distribution of developers, with apps available on both iOS and Android. Memoirs of Life and Memorize: Diaries, Memories, Notes, Ideas, Timelines, Categories (Fair Apps Mobile) had the highest mean MARS scores of 3.35, indicating strong engagement, functionality, and information quality, while the lowest score was 2.33. The overall mean score across all apps was only 3.03 (SD 0.60), highlighting significant variation, particularly in information quality. User feedback also showed considerable variability, ranging from 0 comments for apps such as Grand Storyteller (VarIT Inc) and PWI Storyteller (Project World Impact, LLC) to as many as 5361 comments for FamilySearch, which received extensive positive reviews. This wide range of user feedback underscores the importance of continuous improvement and user-centered design, particularly in enhancing information quality and content accuracy. The systematic search and evaluation highlight the diverse capabilities yet variable quality of digital storytelling apps available within the Chinese market, reflecting user experiences, satisfaction levels, and efficacy in supporting cognitively impaired users. While some apps excel in engagement and functionality, others need significant improvements in information quality and user interface design to better serve those with cognitive impairments. Future research is recommended to investigate regional limitations and features that would result in more inclusive and effective digital storytelling apps.