HUMAN COMPUTER INTERACTION AND DESIGN: BLUETRACK APP

Name of the Student
Name of the University
Author Note:

Introduction
The marine debris and pollution is rather critical worldwide issues which are threatening the marine ecosystems, livelihoods and the human health of the coastal communities. While several environmental organizations and the governmental bodies are actively monitoring the issue of pollution, the data from the direct observers, like the sailors, divers, fishers and swimmers is still remaining entirely underutilized. These personnel are within the direct contact with the entire marine environment as well as therefore, suitable contributors to the citizen initiatives of science which are aimed towards tracking and mitigating the ocean pollution.
Annotated bibliography
The paper is exploring how the non-expert members are contributing to the scientific collection of data, focusing on the motivation and the engagement in the citizen science projects. The source has been selected because it would provide the foundation for the understanding the method by which the BlueTrack App could easily leverage the community engagement (Barricelli and Fogli 2024). The analysis about the importance of community engagement through app is interesting and it has been applied in designing the community management section of the app. One key insight applied in the design was necessity of motivation mechanisms. The authors have emphasized that the citizen science tools success when the users feel that their contributions are mattered.
The paper is examining how the eco-feedback platforms are influencing the behavior through making the environmental impact highly visible to the users. The source has been selected because the BlueTrack App would incorporate the eco-feedback through showing the users with the visual summaries of the contributions to the cleaner oceans (Zytko, Im and Zong 2022). The implementation of these aspects has been done in the designing of real-time eco-feedback loop in the app, when the users would submit the debris sightings, interface would immediately update the shared community metrics as well as the personal progress of the users would be refreshed. Idea of the immediate visual confirmation from the paper has informed addition of the high-contrasting confirmation screens, progress waves and the animated checkmarks.
The author is discussing how the awareness technologies could easily improve the collaboration among the distributed groups. The research informed the social interaction design of BlueTrack App, specifically the function allowing the users into sharing various reports with the local environmental groups and the adjacent water sport fanatics (Kant and Adula 2024). The implementation of the simplified interface has been done based on this article which shows that nearby current reports using color cues and minimal icons. This is avoiding the cognitive overload while being still enabling the users into feeling connected to the activity of local environment. The idea of the authors is directly shaped Community Sightings Map.
The author is briefly synthesizing the psychological principles associated with the digital design. The paper has been selected for strengthening the understanding of overall perpetual as well as the cognitive factors of design. The principle of decreasing the cognitive load guided the simplicity of the interaction (Nazar et al. 2021). This influenced the design in using high contrasting and large icons for the debris categories, rather than depending on the heavy text menus. The book is emphasizing on decreasing the cognitive load, that informed the decision of streamlining overall reporting workflow and placing the button of Report Debris at center of home screen. These psychological principles have been specifically beneficial for the designing app aimed at being used by the divers as well as sailors who could not focus on the complicated interfaces.
The principles of Norman of visibility, affordance, constraints, and the feedback are serving as the foundational guidelines of design. The source has been selected for the direct association with the section of the interaction principles. The reflection in this article is that the emphasis of Normal on the natural mapping as well as the immediate feedback is aligning with the design goal of the app (Kivijrvi and Prnnen 2023). Inspired by the emphasis of Norman on the clear signifiers, the raised button shapes, bold outlines and shadows have been applied for indicating the tappable features on wearable and the mobile interfaces. The use of principle of constraints have shaped overall structured reporting options rather than open text fields, with ensuring that the users would submit the accurate, and standardized categories of debris.
The author is providing the systematic guidance on the user-centered design as well as the prototyping processes. The source is informing the iterative development procedure of the BlueTrack App, specifically the method of low to the mid fidelity prototyping (Ball and Richardson 2022). This inspired inclusion of the Reflection Journal features in the app, where the users could easily record their notes regarding marine conditions or even the personal reflections regarding stewardship. The idea which interfaces could encouraging deeper thinking being influenced the decision of creating gentle, and minimal designs in various sections of app.
Product design
Design
The overall conceptual design of the BlueTrack App is centered on the enabling of seamless, the low-effort reporting of the marine debris and the pollution incidents through the integrated smartwatch and mobile application. The intended target users are the divers, the swimmers and the fishers. As several types of users would be interacting with the system in the various dynamic outdoor environment, the design is focusing on simplicity, rapid and swift interaction with minimal design elements. The primary assumption is that the users might be needed to report debris or hazard quickly using only one hand. Therefore, this interface is prioritizing the large buttons, clean layout and the high contrasting visuals which would reduce any necessary complexity in the navigation. The main tasks of the users would be logging the debris sightings, browsing the recent report on the live map, joining the initiatives of community cleanup, and monitoring personal contributions (Preece n.d). The types of interactions would include input using touch, optional voice input and automated location tagging. The users could perform four major tasks:
Log debris detections: Through capturing the photos and tagging the location coordinates. This is the most critical and the first task which would be done by the users. It enables the users with documenting the environmental issues properly by easily capturing the photographs as well as automatically attaching the GPS coordinates. The users would sign in into the app using personal credentials and then view reporting data. After viewing debris, the users would click on Report Debris and save the data about debris.

Figure 1: Documenting the environmental issues

Figure 2: Providing confirmation for the debris reporting
Alerting the authorities: Automatically through the categorized submissions. This is the second task that has been integrated into entire reporting flow. When any user would classify any sighting, system would trigger the notifications which are pre-configured to associated maritime or even the environmental authorities. The users would send their sightings data from the report section to the appropriate authorities.

Figure 3: Reporting debris
Viewing the community data: Through charts and maps presenting recent reports. This is presented through the interactive maps as well as the visual analytics. The users could easily explore the recent sightings, filter the reports by the type as well as review the trends. After clicking Contribute button, the users would be able to input details of debris sightings and contribute towards betterment of environment.

Figure 4: Engaging in community
Collaborating and reflecting: Through the community feed as well as the personal journal. Finally, users could collaborate as well as reflect through community feed as well as the customized impact journal. The community feed would allow sharing proper experiences, clean-up events, photos and various discussions and foster the sense of the collective responsibility.

Figure 5: Checking personal impact
The interaction design of the app uses the hybrid model, where the touch-based interactions for the detailed input, and the voice interactions in the smartwatch. For instance, the divers could easily press one single button on the smartwatch for marking their GPS coordinates of the debris deprived of using any mobile interface.
The design development is followed by the Generating the Prototypes process being described articles. The initial storyboard is able to map the user journeys in various environmental contexts, being followed by the low fidelity wireframes being tested with the simulated aquatic glare as well as the motion (Kosch et al. 2023). The feedback easily led to the adjustments, like the higher font size and the simplified menus, prior developing the interactive prototype of mid-fidelity level in Axure RP.
Design principles
Visibility: The key controls like Map View and Report are rather prominently displayed along with the higher contrast color. This principle is maintained by ensuring app interface would use the bright icons rather visible under the sunlight or even the underwater glare (Ball and Richardson 2022).
Example:
Figure 6: Using bright icons

Constraints: This principle is maintained by ensuring that the users are being guided into selecting the predefined debris categories instead of entering the free-text, with reducing the ambiguity in the data collection (Liao and Vaughan 2023).
Example:
Figure 7: Entering hands-free text

Consistency: Both the smartwatch and the mobile versions shares the consistent iconography as well as the terminology. This principle is maintained by ensuring the color codes for the debris categories is remaining uniform across the screens, decreasing the confusion (Sadeghi Milani et al. 2024).
Example:

Figure 8: consistent iconography as well as the terminology
Affordance: This principle is maintained by ensuring the interactive elements are being designed with the clear signifiers, for example, the buttons are being raised as well as shadowed for implying the tap-ability, while the sliders as well as the toggles are being used for swift input when being gloved (Ball and Richardson 2022).
Example:
Figure 9: shadowed for implying the tap-ability

Prototype

Figure 10: Homescreen

Figure 11: Debris reporting

Figure 12: Confirmation of debris

Figure 13: Recent sightings

Figure 14: Community details

Figure 15: My impact page
https://wxcw4a.axshare.com
The overall mid-fidelity prototype of the BlueTrack App has been developed utilizing the Axure RP and is incorporating the realistic interactivity across the smartwatch and the mobile screens. The primary interface involves:
? Centralized report debris button
? Live map of the recent sightings
? Community tab for the shared posts as well as the clean-up events
? Dashboard of My impact summarizing the user contributions
One of the significant iterations included the simplification of the report workflow from the four stages to mainly two, decreasing the user effort as well as probable frustration.
Establishing Accessibility and Inclusiveness.
One can refer to accessibility and inclusivity as the key concepts to be pursued when creating the BlueTrack App since in such a manner, the application may be tailored by the largest number of people irrespective of their abilities and in the context in which they are employed (Zytko, Im and Zong 2022). The latter was used in the whole design since the target market included divers, swimmers and fishers and individuals who possessed varying degrees of experience in the application of technology.
Visual Accessibility:
The greatest challenge that the design team of the BlueTrack App encountered was that it is too noticeable during the day when the sun is too bright or at the sea where the visual objects cannot be easily identified. As an attempt to contain this, color schemes have been contrasted and more interactive features such as the use of buttons have been enlarged so that a user can still use the app in winter when putting gloves on or when it is light. Further, the application supports custom font size, which provides the ability to use the application by the visually challenged (Sadeghi Milani et al. 2024).
Motor Accessibility:
The users may be covered with gloves (divers or fishermen) with physical environment; therefore, it is impossible to touch it properly. The application is also more interactive similar to having buttons and sliders that are easy to tap or swipe using gloves. There is also the presence of voice commands and this enables the users to use their applications without necessarily touching their gadgets thereby rendering the applications more convenient in water areas or when the user is using with machines (Nazar et al. 2021).
Cognitive Accessibility:
Blue Track App is concerned with simplified and simplified navigation since it is geared towards lessening the amount of work required on the mind. The complex processes were reduced to the bare minimum to ensure that the application was as user friendly as possible even to the non-technical people. Proper identification has been done with the help of icons and minimum amount of texts to recognize the application interface. It also has contextual support, including the brief tooltips, the help tutorial and pop-up, and instructs the users in the process of reporting the trash (Barricelli and Fogli 2024).
Ethics and Privacy of Data
The most important and especially regarding the ecological surveillance tools such as the BlueTrack App, when developing applications where personalized information is being gathered the important consideration is the ethical values and privacy concerns which, in this case, the user interface has to be user-friendly. As this information will be deposited by the users who will be sensitive data on the marine environment and pollution, the question of respectful treatment of such data will be one of the primary questions (Barricelli and Fogli 2024).
User Consent and Openness:
BlueTrack App is highly concerned with the issue of informed consent. A consent form is offered to the users in which they are notified of the way that their information would be shared, used and stored. This would be transparent and open which would assure the users that they would be aware of the type of data they would be providing and how the provided data would be used in the greater environmental monitoring process (Zytko, Im and Zong 2022). The other option available in the application is the aspect of opt-in, in which the users are granted the alternative of either entering the information about the personal or not.
Data Privacy and Security:
Since the app will be interconnected with the GPS positioning, photo pictures, and, most probably, personal information, the privacy of users will be demanded. BlueTrack App has a built-in end to end encryption of the data transmission and thus the personal and location data is not lost. Data information stored under the user is obtained according to GDPR (General Data Protection Regulation) or domestic law i.e. the policy of spending data is apparent, and the user is free to manage his or her data.
Moreover, the application will reduce the amount of data that the application will be collecting to expose minimal information as required in its core processes so that the user is not bombarded with data gathering requests. Basing on the analogy that it is possible to leave pictures of rubbish; the user is not asked to provide any information that is not recognizable to him or her unless the user makes a choice to join the application that is a community like clean-up events or discussion groups.
Ethical Use of User Data:
The information that will be received through the app will be utilized mainly in the process of monitoring and prevention of marine pollution. Nonetheless, ethical application of this data does not commence and end at the gathering of such data but extends to sharing of such data. The information will then be made available to the environmental agencies and the concerned government through the BlueTrack App in a manner that would not be violating the privacy of the users. In addition, it lacked a business practice of user information that could be performed without their express agreement that would guarantee that personal information is managed ethically.
Scalability and Future Improvement
The technology advancement must also be used in the BlueTrack applications. Though the existing version of the app proves to be a strong tool, the means of providing conservation to the marine environment, there are a number of ways how the tool can be improved to make the app a more efficient and convenient tool in the future.
Increased Interaction of AI and Machine Learning:
The upcoming generations of the BlueTrack can also include machine learning (ML) to be trained and to classify the type of debris that people are reporting to them. Alternative, instead of leaving the user to the task of sorting out the rubbish himself/herself, the app can apply the image recognition technology to find the general type of rubbish based on the images uploaded. This would not only ease the burden of thought of the users but would also make the reports much more precise.
Implementation of IoT and Wearables:
The new versions may be connected with the devices of the Internet of Things (IoT) or other wearables to increase the range of the app. The environment sensors are one of the application features as an example that spies the quality of the water and the amount of rubbish or other pollutants in the environment. These sensors would also offer valuable and up-to-date information to the users that would not only arouse the user activity, but would also present a more objective set of information to observe the environment.
Scalability and Internationalization:
After BlueTrack App becomes more sophisticated, it will also get a chance to cover a broader area than the original market targeted by marine divers, and cover a broader range of individuals living in the coastal neighborhood, environmental scientists, and even visitors. It would also have to localize the app to the other areas by localizing the app to the other areas which it will be operating by offering the app local languages, offering local reporting and offering local provision of local specific eco-feedback systems that it would be capturing the local cultures and local issues (Ho and Vuong 2025).
The prototype is demonstrating the realistic interactions, involving the geolocation capture, the category selection as well as the notifications of confirmation. The smartwatch prototype would enable the one-touch logging as well as the voice notes, specifically designed for the aquatic accessibility. The intention of the prototype is mainly for validating the assumptions regarding the reporting usability as well as the motivational influence of the eco-feedback on the consistent participation (Lyu 2024).
Research study
The aim of this study is assessing whether design choices inspired by the HCI theory, specifically visibility is successfully supporting the correct and the low-effort debris reporting for the users operating in time-sensitive and unstable marine conditions. The objective is aligned with vast design intention of making application dependable for the use in open-water, restricted visibility, where the distractions and the environmental stressors could influence the user behavior.
The research question being considered is:
Is it possible for divers to report debris by wearing diving suit with gloves and full uniform?
The hypothesis being considered is that:
The users are easily able to operate the app and the smartwatch by wearing gloves and complete uniform because the app allows also voice assisted instructions.
The participants being considered are:
Forty active marine divers would be recruited using various local clubs and the online forums. The sample of 40 divers would allow the inclusion of participants with varied levels of marine knowledge, like recreational snorkelers, professional divers, fishers and sailors which would help in ensuring the ecological validity. Any smaller sample might overrepresent one particular group. The inclusion of the SUS scores, accuracy measures and task completion times, each needing statistical analysis, is benefited from the sample of minimum 30 for achieving acceptable power.
The method being employed for conducting the research study is:
Participants would randomly be assigned into two main groups like:
? Experimental group utilizing the eco-feedback type of the BlueTrack App
? The control group utilizing the simplified version deprived of eco-feedback.
? The participants would simulate the reporting of debris during the two-hour marine activities utilizing the waterproof smartwatches and the mobile devices.
The materials being used for conducting the research study are:
? Proper consent form that is outlining the ethical considerations
? Pre-study questionnaire for capturing the demographics as well as the environmental attitudes.
? Interview conducted post-study properly assessing the perceived usability, motivation as well as the satisfaction
? Observation checklist to be used by the researchers.
? Divers would be given the prototype alongside the questionnaire.
Data analysis
Data analysis would be structured across the qualitative and quantitative dimensions for producing the complete picture of the usability performance.
The quantitative analysis would include the following:
? Task completion time
? Descriptive statistics of survey results
? One sample t-test examining whether the error rates are different by the task type of the various interface tasks
? Reporting accuracy
? Comparison of the participant chosen debris categories to correct pre-determined categories
? Calculation of accuracy % per task and complete
? System Usability Scale
? SUS scoring converted to 0-100 scale
? Comparison with the industry benchmarks
? Correlational Analysis
? Helps in determining whether the expertise is biasing the outcome
? Correlation among the experience levels.
Conclusion
One of the most crucial insights I have gained is how the context is shaping interaction. Before this project, I mainly tended in viewing usability primarily through the screen design and the interface layout. However, after studying HCI theory as well as analyzing the academic literature showed how drastically the environmental constraints would affect what is plausible and highly safe for the users. This understanding has strongly influenced my decisions of design, pushing me into making the interactions clearer, shorter as well as highly resilient in distraction. Working with the concepts like affordances, visibility, constraints and feedback helped me into moving from theoretical knowledge to the functional design practices. I have become highly aware of responsibility held by the designers when creating tools which should function reliably in the unpredictable exterior environment. This reflection has made me prioritize safety and clarity over the decorative complexity as well as assisted me into completely appreciating the value of the iterative prototyping. One more crucial insight was role of the trust and motivation in the science-citizen applications. The literature has highlighted that the users participate highly actively when they would feel that the contribution matters. This shaped my choice of including the confirmation messages, transparent process of reporting and the community features aligned with the environmental authorities. Overall, this entire coursework has strengthened my overall ability of connecting research with the design practice. It has also made me increasingly attentive to the user context, evidence backed decision-making and the ethical responsibility, the skills which I would carry into any future projects including technologies of real-world.
References
Ball, L.J. and Richardson, B.H., 2022. Eye movement in user experience and humancomputer interaction research. In Eye Tracking: Background, Methods, and Applications (pp. 165-183). New York, NY: Springer US.
Barricelli, B.R. and Fogli, D., 2024. Digital twins in human-computer interaction: A systematic review. International Journal of HumanComputer Interaction, 40(2), pp.79-97.
Ho, M.T. and Vuong, Q.H., 2025. Five premises to understand humancomputer interactions as AI is changing the world.AI & SOCIETY,40(2), pp.1161-1162.
Kant, S. and Adula, M., 2024. Human-Machine Interaction in the Metaverse in the Context of Ethiopia. In Impact and Potential of Machine Learning in the Metaverse (pp. 196-212). IGI Global.
Kivijrvi, H. and Prnnen, K., 2023. Instrumental usability and effective user experience: Interwoven drivers and outcomes of Human-Computer interaction. International Journal of HumanComputer Interaction, 39(1), pp.34-51.
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A. and Wo?niak, P.W., 2023. A survey on measuring cognitive workload in human-computer interaction.ACM Computing Surveys,55(13s), pp.1-39.
Liao, Q.V. and Vaughan, J.W., 2023. Ai transparency in the age of llms: A human-centered research roadmap.arXiv preprint arXiv:2306.01941.
Lyu, Z., 2024. State-of-the-art human-computer-interaction in metaverse.International Journal of HumanComputer Interaction,40(21), pp.6690-6708.
MacKenzie, I.S., 2024. Human-computer interaction: An empirical research perspective.
Nazar, M., Alam, M.M., Yafi, E. and Suud, M.M., 2021. A systematic review of humancomputer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9, pp.153316-153348.
Preece, Y. R. H. S. J. (n.d.). INTERACTION DESIGN: beyond human-computer interaction, 3rd Edition. OReilly Online Learning. https://www.oreilly.com/library/view/interaction-design-beyond/9780470665763/
Sadeghi Milani, A., Cecil-Xavier, A., Gupta, A., Cecil, J. and Kennison, S., 2024. A systematic review of humancomputer interaction (HCI) research in medical and other engineering fields.International Journal of HumanComputer Interaction,40(3), pp.515-536.
Zytko, D., Im, J. and Zong, J., 2022, November. Consent: A research and design lens for human-computer interaction. In Companion Publication of the 2022 Conference on Computer Supported Cooperative Work and Social Computing (pp. 205-208).

Appendix
Search strategy details
The search procedure followed the systematic as well as the iterative approach:
Original scoping search: Executed on Google Scholar for identifying the broad themes as well as confirming the availability of the literature
Focused database search: Searches have been performed utilizing various refined Boolean terms across the IEE, ACM
Screening the titles and the abstracts: The articles have been screened for the relevance on the basis of the methodological and the conceptual relevance

Questinnarire:
? How easy was it to complete the debris reporting task?
Scale: 1 = Very Difficult, 5 = Very Easy
Sample Answer: 4

? How quickly were you able to locate the Report Debris button on the main screen?
Scale: 1 = Very Slow, 5 = Very Fast
Sample Answer: 5

? The app interface was visually clear and easy to understand.
Scale: 1 = Strongly Disagree, 5 = Strongly Agree
Sample Answer: 5

? The number of steps required to submit a report felt manageable.
Scale: 1 = Strongly Disagree (too many steps),
5 = Strongly Agree (minimal steps)
Sample Answer: 4

? The eco-feedback (visual progress, confirmation messages, community metrics) motivated me to submit more reports.
Scale: 1 = Not Motivating, 5 = Highly Motivating
Sample Answer: 5

? How accurate did you feel your debris category selections were when using the app?
Scale: 1 = Not Accurate, 5 = Very Accurate
Sample Answer: 4

? The app responded quickly and smoothly during the reporting tasks.
Scale: 1 = Very Slow, 5 = Very Fast
Sample Answer: 5

? I felt confident using the app even in outdoor or water-related environments.
Scale: 1 = Not Confident, 5 = Very Confident
Sample Answer: 4

? How likely are you to continue using this app to report debris in future?
Scale: 1 = Very Unlikely, 5 = Very Likely
Sample Answer: 5

? Overall, how satisfied are you with the usability of the BlueTrack App?
Scale: 1 = Very Unsatisfied, 5 = Very Satisfied
Sample Answer: 5