End-user Experience Research Articles

Application Optimizing AI Performance on Edge Devices: A Comprehensive Approach using Model Compression, Federated Learning, and Distributed Inference

One major problem arises when AI models are run on edge devices because these have limited processing power, battery, and time constraints. This article explores methods to improve the performance of AI models in such settings so that they operate optimally and simultaneously and provide fast and accurate results. Some methods include model compression techniques such as pruning and quantizing, which make the model small sized to make the required computations with low energy utilization and knowledge distillation. Moreover, a special concern is checking the possibility of using federated learning as one of the ways of training AI models on devices spread across a distributed network while maintaining users’ privacy and avoiding the need to transfer the data to the central server. Another approach, distributed inference, in which the computations are suitably divided between different devices, is also investigated to enhance system performance and reduce latency. The use of these techniques is described in terms of the limited capabilities inherent to devices like smartphones, IoT sensors, and autonomous systems. In this work, efforts have been made to improve the inference and model deployment in edge AI systems, which is instrumental in enhancing the end user experience and smart energy usage by bringing sophisticated scale out edge-computing solutions closer to reality through application optimized edge AI models and frameworks.

Optimizing quality of service forecasting in mobile networks through modified walrus optimization and multivariate approaches

This paper presents Ensemble-based Service Quality Prediction (EAQP), an automated method for predicting service quality under changing mobile network conditions. EAQP incorporates data preparation methods such as transformation, purification, & imputation, and then performs feature extraction utilizing statistical, geographical, as well as temporal approaches. An improved feature selection method, using a unique weighting approach and optimized by a modified Walrus Optimization Algorithm, improves the accuracy of predictions. EAQP utilizes a variety of prediction models such as support vector regression, recurrent neural network models, bi-directional short-term long-term memory networks, extreme learning machines, along with multi-layer perceptron neural networks to enhance predictive accuracy. EAQP uses complex optimization algorithms and ensemble learning approaches to provide precise and dependable predictions about service quality in real-time. This helps in proactive network management as well as improvement. This comprehensive approach shows potential for boosting network efficiency, optimizing the distribution of resources, and enhancing the end-user experience when using mobile communications systems.

Enhancing corneal ectasia susceptibility detection: analysis of a new algorithm (BAD-D v4)

Accurate detection of post-refractive ectasia susceptibility is essential during preoperative evaluation for laser vision correction (LVC) due to the risk of progressive corneal ectasia and vision decline post-surgery. Despite improved screening and a reduced incidence from 0.66 to 0.033%, iatrogenic ectasia remains a concern due to the severe vision loss it can cause, highlighting the need for more accurate detection tools. A new optimized version of the Belin/Ambrósio Enhanced Ectasia Display version 4 (BAD-D v4) was developed and validated across 26 international centers to enhance the detection of keratoconus and very asymmetric ectasia and to assess the risk of post-refractive ectasia. Analyzing a dataset of 3,886 eyes from 3,351 patients, including normal, keratoconus (KC), and cases with very asymmetric ectasia (VAE) categories, having one eye with normal topography (VAE-NT and the fellow eye with clinical ectasia (VAE-E). The study utilized an optimized logistic regression algorithm improving diagnostic accuracy. The BAD-D v4 showed superior efficacy in differentiating normal eyes from ectatic conditions, with Area Under the Receiver Operating Characteristic Curve (AUROC) scores of 0.997 and 0.998 in training and testing samples for normal versus clinical ectasia. Additionally, in Normal vs. Disease (KC + VAE), the AUROC was 0.974 and 0.966, and in the challenging Normal vs. VAE-NT diverse group, it scored 0.905 and 0.858. These results outperformed the current version (BAD-D v3) and were comparable to the Pentacam Random Forest Index in all tested scenarios, highlighting the potential of BAD-D v4 in early ectasia detection, without altering the index scale or the end-user experience.

Application of Artificial Intelligence in Symptom Monitoring in Adult Cancer Survivorship: A Systematic Review

PURPOSE The adoption of artificial intelligence (AI) in health care may afford new avenues for personalized and patient-centered care. This systematic review explored the role of AI in symptom monitoring for adult cancer survivors. METHODS A comprehensive search was performed from inception to November 2023 in seven bibliographic databases and three clinical trial registries. This PROSPERO registered review (ID: CRD42023476027) assessed reports of empirical research studies of AI use in symptom monitoring (physical and psychological symptoms) across all cancer types in adults. RESULTS A total of 18,530 reports were identified, of which 41 met review criteria and were analyzed. Included studies were predominantly published between 2021 and 2023, originated in the United States (39.0%) and Japan (14.6%), and primarily used cohort designs (80.5%), followed by cross-sectional designs (12.2%). The mean sample size was 617.14 (standard deviation = 1,401.37), with most studies primarily including multiple tumor types (31.7%) or breast cancer survivors (26.8%). Machine learning algorithms (43.9%) was the most used AI method, followed by natural language processing (29.3%), AI-driven chatbots (17.1%), and decision support tools (9.8%). The most common inputs to the AI algorithms were textual data, patient-reported symptoms, and physiologic measurements. The most examined symptom was pain (34.2% of studies), followed by fatigue and nausea (17.1% of studies each). Overall, the review showed increasing AI technology use in the prediction and monitoring of cancer symptoms. CONCLUSION AI is being used to enhance symptom monitoring in various cancer settings. When considering integration into clinical practice, standardization of data capture, the use of analytics, investing in infrastructure, and the end-user experience should be considered for successful implementation and monitoring the improvement of patient outcomes.

A survey of end user and health care professionals experience of discomfort as related to intermittent catheterisation

A survey of end user and health care professionals experience of discomfort as related to intermittent catheterisation

Enhancing patient-centred chiropractic care in Canada: identifying barriers, enablers, and strategies through a qualitative needs assessment

BackgroundThe Canadian Chiropractic Association (CCA) initiated a quality improvement project to develop best practices aimed at enhancing the patient experience.Objectives(1) Identify and prioritise the key moments in the new patient experience that could be improved by providing chiropractors with focused support and resources; (2) explore views, barriers, and enablers to implementing these best practices; and (3) develop recommendations to facilitate the adoption of these practices.MethodsWe conducted a qualitative needs assessment using a human-centred design approach, focused on understanding the needs and experiences of end-users to create tailored solutions. The Theoretical Domains Framework (TDF) was employed to explore chiropractors’ knowledge use and behaviour change, and TDF domains were mapped to Behaviour Change Techniques (BCTs) to develop targeted strategies for addressing identified barriers and enablers. Thirteen chiropractors from across Canada participated in semi-structured interviews and related activities.ResultsThe key moments where participants felt they needed the most support were “treatment”, “report of findings”, “informed consent”, “physical examination”, and “before the appointment”. All participants agreed with the best practices seed statements. Key barriers included gaps in knowledge, communication skills, and resource availability, particularly in rural areas. Enablers included collaboration with other health professionals, mentorship, and access to practice tools. Recommendations include enhanced training in communication and treatment planning, increased access to resources in rural areas, and fostering collaborative relationships among health professionals.ConclusionUnderstanding the barriers and enablers to implementing best practices can inform targeted strategies to improve patient-centred care in chiropractic practice across Canada.

Self-reported public fast charging infrastructure demand: What do existing and potential electric vehicle adopters want and where?

Electric vehicles (EVs) promise emissions reduction, but adoption rates must increase to meet 2030 targets. Since public fast charging infrastructure (PFCI) is considered important to increase EV uptake, this research aims to assess its demand across existing and potential EV adopters in Ireland. Online mapping tools were used to capture 1635 self-reported locations where users prefer PFCI, along with other related preferences from 545 respondents. The results were analysed using a mixed-method approach including exploratory factor analysis (EFA), Geographic Information System (GIS), two-step cluster analysis, and qualitative analysis. The results indicate that expanding PFCI is critical for increasing EV uptake, with 81.4% of potential EV adopters showing a significant impact on uptake. Major deterrents identified include insufficient availability of public fast chargers followed by high charging costs. The qualitative analysis highlights existing EV users’ demand for improved end-user experiences, such as unified payment systems. Most preferred locations for PFCI installation include travel routes, followed by shopping centres/ supermarkets. Results also indicate that a reliable second-hand market would be key to increasing EV uptake. The findings from this study can serve policymakers in effectively rolling out PFCI, while the methodology employed can be replicated in other comparable study areas.

Diagnosing skin neglected tropical diseases with the aid of digital health tools: A scoping review.

Delays in diagnosis and detection of skin neglected tropical diseases (NTDs) pose obstacles to prompt treatment, which is crucial in preventing disability. Recent developments in digital health have given rise to approaches that could increase access to diagnosis in resource-poor areas affected by skin NTDs. This scoping review provides an overview of current digital health approaches that aim to aid in the diagnosis of skin NTDs and provides an insight into the diverse functionalities of current digital health tools, their feasibility, usability, and the current gaps in research around these digital health approaches. This scoping review included a comprehensive literature search on PubMed, EMBASE and SCOPUS, following the PRISMA guidelines. Eleven studies were included in the review and were analysed using a descriptive thematic approach. Most digital tools were found to be mobile-phone based, such as mobile Health (mHealth) apps, store-and-forward tele-dermatology, and Short Messaging Service (SMS) text-messaging. Other digital approaches were based on computer software, such as tele-dermatopathology, computer-based telemedicine, and real-time tele-dermatology. Digital health tools commonly facilitated provider-provider interactions, which helped support diagnoses of skin NTDs at the community level. Articles which focused on end-user user experience reported that users appreciated the usefulness and convenience of these digital tools. However, the results emphasized the existing lack of data regarding the diagnostic precision of these tools, and highlighted various hurdles to their effective implementation, including insufficient infrastructure, data security issues and low adherence to the routine use of digital health tools. Digital health tools can help ascertain diagnosis of skin NTDs through remote review or consultations with patients, and support health providers in the diagnostic process. However, further research is required to address the data security issues associated with digital health tools. Developers should consider adapting digital health tools to diverse socio-cultural and technical environments, where skin NTDs are endemic. Researchers are encouraged to assess the diagnostic accuracy of digital health tools and conduct further qualitative studies to inform end-user experience. Overall, future studies should consider expanding the geographical and disease scope of research on digital health tools which aid the diagnosis of skin NTDs.

End user experience of a widely used artificial intelligence based sepsis system.

Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences. Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh's and Salwei and Carayon's frameworks, supplemented by inductive coding for emerging themes. Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system's seamless electronic health record integration and identifying design gaps. This study offers insights into clinicians' experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction. The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.

Minimizing Business Disruption During Enterprise End-Point OS Upgrade while Maintaining a Robust Security Posture

The deployment of a major end-point operating system (OS) upgrade presents significant challenges for enterprise organizations, requiring a comprehensive strategy to minimize business disruption and maintain a robust security posture. Beyond the operational complexities of managing user productivity and application compatibility, organizations must also proactively address the critical security risks associated with such large-scale technology transitions. Cybercriminals often target organizations undergoing major IT changes, seeking to exploit vulnerabilities that may arise due to shifts in the OS architecture, user workflows, and security controls. This paper outlines practical strategies for executing successful OS upgrade projects that balance operational resilience and cyber risk management. Key focus areas include proactive management of end-point security, comprehensive contingency planning and rollback procedures, strategies to sustain productivity and business continuity, effective end-user training and communication, and valuable insights into the next- generation OS deployment. By focusing on these critical aspects, organizations can navigate major OS upgrade initiatives while preserving vigorous business operations, enhancing end-user experience, and safeguarding sensitive data and systems from costly security incidents.

Blocked force characterization and transposition at secondary connections of a compressor

Applications in sustainable energy systems, like heat pumps, are encountering emerging challenges, among which acoustic comfort stands out as a critical factor in shaping the end-user experience. Early integration of vibroacoustic into each industrial design phase ensures the prevention and prediction of potential noise issues, thus avoid costly design modification loops. BDR Thermea and CETIM, in a collaborative project, have implemented new methodologies for designing acoustically optimized products, highlighting the importance of considering vibroacoustic design at each design stage of new products developments. In this study, structure-borne noise characterization methods are applied to a heat pump compressor using the ISO 21955 and ISO 20270 standards. The forces transmitted at its suction and discharge ports were studied through component based TPA for the intrinsic characterization of the compressor. Once obtained, these forces were transposed to a new receiving structure. The results allowed a comparison of the outcomes from both approaches and highlighted the importance of considering the influence of the stiffness of the connections at the compressor ports.

Impact of Wireless Network Packet Loss on Real-Time Video Streaming Application: A Comparative Study of H.265 and H.266 Codecs

The transmission of real-time videos over wireless networks is prone to the negative consequences of packet loss and delay, which can have a potential effect on the video quality during streaming. These impairments can lead to interruptions, buffering, and degradation of visual and auditory elements, resulting in an unsatisfactory user experience. In this paper, we aim to address the challenges associated with packet loss and delay parameters in wireless networks, and propose an approach to alleviate their impact on real-time video transmission. The proposed approach involves utilizing the H.265/H.266 video coding standards. For Versatile Video Coding (VVC), a patch support for VVdeC and VVenC to FFmpeg (Fast Forward Moving Picture Expert Group) is added. As a result, FFmpeg is used to encode, stream and decode all videos. Raw videos of 2K qualities are encoded based on the adaptive quantization (QP) for the above-mentioned codecs. By selecting optimal transmission data based on various network conditions, this approach enhances the Quality of Experience (QoE) for end-users while minimizing resource usage in the wireless network. Furthermore, the proposed approach selects the codec standards according to their bitrates and frame rates. Simulation results indicate that the proposed approach has a significant improvement for real-time video streaming over wireless networks to satisfy the end user experience. The approach also outperforms other related work by gaining a PSNR of +12 dB for H.265 and +13 dB for H.266 when the network packet loss is 1%.

Physicians' Experiences of EHR Technical Quality: Results from Four Large Cross-Sectional Surveys in 2010-2021.

Unexpected downtime and long response times of electronic health record (EHR) systems not only impact user satisfaction and clinicians' work efficiency but also bring about potential harm for patients. Despite improvements in the performance of EHR systems' architecture, hardware, and networks, technical challenges continue to cause problems. We explored the end-user experiences of EHR technical functionality and quality from four large national cross-sectional surveys conducted among Finnish physicians in 2010-21. The results were analyzed by healthcare sector/specialty groups. In most groups, the experiences of stability and reaction speed became worse in 2010-17, which is readily explained by the implementation of the national patient data repository services, but improvements were seen in 2021, suggesting that EHR vendors have solved at least some of the slowness problems. The proportion of physicians reporting having experienced faulty system function with potential or actualized harm for the patient had decreased in operative and medical specialties and in the private sector but remained stable in other groups. Our findings underline the importance of continuing to develop technical qualities - including the implementations of national integrations.

Healthcare workers’ perceptions and user experiences of biometric technology in the selected healthcare facilities in Zambia

BackgroundBiometric recognition, using traits like fingerprints, faces, and irises, has advanced significantly, enabling accurate identification. Despite progress, implementation in Zambian healthcare is limited. This study explored healthcare workers’ and patients' perceptions and experiences regarding biometric technology in Zambian healthcare settings, offering insights for informed decision-making and product design.MethodsThis phenomenological study explored the perceptions and experiences of healthcare workers and end-users (patients) concerning biometric technology in four healthcare facilities in Lusaka Province: Chilenje, Chawama, Kanyama (first-level hospitals), and Railways Urban Clinic. These facilities were purposefully selected for their active use of biometric services and involvement with the National Health Insurance Management Authority (NHIMA). The study included 20 healthcare workers and 16 patients. Face-to-face interviews with healthcare workers and patients, and focus group discussions with users, lasting 35 to 65 min, were conducted. Interviews were audio-recorded, transcribed verbatim, coded with Nvivo version 12, and analyzed using inductive thematic analysis.ResultsFacilitators included efficient system integration, investment in biometric equipment, workflow transformation, and patient recognition of benefits. Barriers included infrastructure limitations, technical challenges, patient resistance and misconceptions, and accessibility challenges for individuals with disabilities.ConclusionThis study highlights the transformative potential of biometric identification systems in healthcare, emphasizing benefits in efficiency and data integrity. Successful integration requires addressing facilitators like education and infrastructure and mitigating barriers such as cultural resistance and technological limitations. Stakeholders, including policymakers and healthcare providers, can use these insights to align efforts with Sustainable Development Goals for improved healthcare access and reduced inequalities. Considering user perspectives is crucial for effective implementation, and continuous improvement can enhance healthcare delivery and patient outcomes.

Barriers and facilitators to effective electronic health record-based sepsis screening in the pediatric intensive care unit.

The Pediatric Surviving Sepsis Campaign supports the implementation of automated tools for early sepsis recognition. In 2019 the C.S. Mott Children's Hospital Pediatric Intensive Care Unit deployed an electronic medical record (EMR)-based screening for early recognition and treatment of sepsis. We analyzed all automated primary sepsis alerts, secondary screens, and bedside huddles from November 2019 to January 2020 (Cohort 1) and from November 2020 to January 2021 (Cohort 2) to identify barriers and facilitators for the use of this tool. We distributed surveys to frontline providers to gather feedback on end-user experience. In Cohort 1, 895 primary alerts were triggered, yielding 503 completed secondary screens and 40 bedside huddles. In Cohort 2, 925 primary alerts were triggered, yielding 532 completed secondary screens and 12 bedside huddles. Surveys assessing end-user experience identified the following facilitators: (1) 73% of nurses endorsed the bedside huddle as value added; (2) 74% of medical providers agreed the bedside huddle increased the likelihood of interventions. The greatest barriers to successful implementation included the (1) overall large number of primary alerts from the automated tool and (2) rate of false alerts, many due to routine respiratory therapy interventions. Our data suggests that the successful implementation of EMR-based sepsis screening tools requires countermeasures focusing on 3 key drivers for change: education, technology, and patient safety. While both medical providers and bedside nurses found merit in our EMR-based sepsis early recognition system, continued refinement is necessary to avoid sepsis alert fatigue.

Blockchain-Based Security Framework for VNF Package Protection in 5G Network Slicing Services

Abstract The demand for scalable, open and granular networks has enabled mobile networks such as 5G to adopt new concepts including NFV (Network Function Virtualization). NFV separates the dependency of network functions from the hardware component, allowing them to be deployed flexibly and dynamically across network slices. In this way, operators can deliver personalized services and optimize the use of network resources, contributing to greater operational efficiency and an enhanced end-user experience. However, there are several issues to be addressed, such as the security of VNFs and the way in which service provider customers instantiate NSSIs (Network Slice Subnet Instance). In this article, we present a new approach based on blockchain 2.0, which guarantees the immutability of VNFs and templates, as well as highly secure instantiation with access management and support for replay attacks.

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

The Internet of Things (IoT) has become an integral part of our daily lives, growing exponentially from a facility to a necessity. IoT has been utilized extensively through cloud computing and has proven an excellent technology for deploying in various fields. The data generated by the IoT devices gets transmitted to the cloud for processing and storage. However, with this approach, there are specific issues like latency, energy, computation resources availability, bandwidth, heterogeneity, storage, and network failure. To overcome these obstacles, fog computing is utilized as a middle tier. Fog computing gathers and processes the generated data closer to the user end before transmitting it to the cloud. This paper aims to conduct a structured review of the current state of fog computing and its architectures deployed across multiple industries. This paper also focuses on the implementation and critical parameters for introducing fog computing in IoT-cloud architecture. A detailed comparative analysis has been carried out for 5 different architectures considering various crucial parameters to identify how the quality of service and quality of experience for end users can be optimized. Finally, this paper looks at the multiple challenges that fog computing faces in a structured six-level approach. These challenges will also lead the way for future research in resource management, green computing, and security.

Development of a machine learning model to classify mineral deposits using sphalerite chemistry and mineral assemblages

Sphalerite is a commonly occurring mineral in natural systems and a prominent indicator mineral used in resource exploration. Its chemistry and associated mineral assemblages are both controlled by the physicochemical conditions of the local environment, such as temperature and sulfur fugacity. Accordingly, the chemistry of sphalerite and the nature of the associated minerals provide valuable clues about the classification of mineral deposits and their environment of formation. Nevertheless, exclusive reliance on the trace-element chemistry of sphalerite for deposit classification has its limitations given the multitude of factors (For example, deposit type, temperature, pressure, and background concentrations of elements) that affect its chemistry. To address this challenge, we develop machine learning models using the SHapley Additive exPlanations (SHAP) method to assess the importance of sphalerite trace-element chemistry and mineral assemblage information to distinguishing between five petrogenetically distinct mineral deposit types. This contribution demonstrates that a composite model that incorporates both data types markedly improve the accuracy of deposit type discrimination. The composite model is composed of two sub-models, Random Forest (RF) and Extra Random Trees (ERT), which are specifically employed for processing trace-element data and mineral assemblage data, respectively, due to their superior performance in handling these two distinct types of datasets. To simplify the end-user experience, we provide an executable file of the machine learning-based classifier, allowing it to be readily applied as an exploration tool using simple inputs. In summary, this work substantially enhances the confidence of Pb-Zn deposit type classification using sphalerite and introduces an innovative perspective on the application of machine learning to resolving complex geological problems.

Sustainable business models in SMEs: The customer’s perspective


 
 
 The main purpose of the study is to identify the relationship between sustainability goals and measures of the degree of sustainable policy implementation, as well as the customer perspective as determined by the business model (BM) template of A. Osterwalder and Y. Pigneur. In the conducted research, the customer perspective takes into account five elements of the BM and each element was examined by several characteristics. The elements of the BM from the customer perspective are: customer segment, value proposition, distribution channels, customer relationships, and revenue. The study used the authors’ survey questionnaire, in which respondents, who were enterprises in the SME sector, answered questions about their own BM divided into three layers – business, social, and environmental. The results were statistically analysed using SPSS STATISTIC 28 software and a logit model was developed to verify the significance of the relationship between the binary variables studied. 303 enterprises participated in the survey, 59% of which were enterprises guided by sustainability goals (SGs) in their BMs. However, only 27% of those surveyed had measures of the degree of sustainable policy implementation.
 
 
 
 The conducted research confirmed that acting in accordance with SGs is strongly related to customer-oriented elements of the business model, such as (1) customer segregation by sex or by level of environmental awareness, (2) value proposition – an environmental orientation, (3) distri- bution channels – direct sales online, (4) customer relationships through dedicated personal assistance or automated service based on personal customer profiles, (5) revenue – sales of products and services. Slightly different results were obtained for using measures of the degree of the SD policy implementation, where the highest correlation occurred for: (1) main customers – manufacturing entrepreneurs, (2) customer segregation by end-user experience or by level of involvement in socially important issues, (3) value proposition – strengthening democracy, (4) distribution channels – wholesale stores are run by partners, (5) customer relationships through self-service or automated service based on personal customer profiles or involving customers in the product development process, (6) revenue – rental/lease/leasing or licenses.
 
 
 
 
 

Reengineering Indoor Air Quality Monitoring Systems to Improve End-User Experience.

This paper presents an indoor air quality (IAQ) monitoring system designed for a better end-user experience. The monitoring system consists of elements, from the monitoring sensor to the monitoring interface, designed and implemented by the research team, especially for the proposed monitoring system. The monitoring solution is intended for users who live in houses without automatic ventilation systems. The air quality sensor is designed at a minimum cost and complexity to allow multi-zone implementation without significant effort. The user interface uses a spatial graphic representation that facilitates understanding areas with different air quality levels. Presentation of the outdoor air quality level supports the user's decision to ventilate a space. An innovative element of the proposed monitoring interface is the real-time forecast of air quality evolution in each monitored space. The paper describes the implementation of an original monitoring solution (monitoring device, Edge/Cloud management system, innovative user monitoring interface) and presents the results of testing this system in a relevant environment. The research conclusions show the proposed solution's benefits in improving the end-user experience, justified both by the technical results obtained and by the opinion of the users who tested the monitoring system.