## 3 Re-Usable Re-Constructible Sensor Device
### Software
#### 3.3.1 Intelligent Calibration
This approach aligns with the design's friendliness philosophy, as it minimizes the impact on users' daily activities while enabling advanced computational capabilities. Rather than burdening local systems with heavy processing tasks, data are securely transmitted to the cloud, where powerful computing resources can efficiently analyze it. One key advantage of cloud-based data processing is the ability to apply the latest AI advancements to enhance data quality. By leveraging AI-driven algorithms, the system can intelligently filter out noise from raw sensor inputs, improving accuracy without requiring additional manual calibration. This results in more reliable insights while maintaining a lightweight, unobtrusive user experience. Furthermore, by operating within existing cloud security frameworks, the device ensures compliance with industry standards, reducing the complexity of implementation for organizations. With encrypted data transmission and stringent access controls, users can trust that their information is handled responsibly without compromising performance. ## 6 Conclusions

We have shown that circular design strategies can influence a user's emotional connection to a product. The combination of surveys and observations offered clear insights into how the participants engaged with the product. For future research, a much larger group of participants would improve the validity of the testing results. Furthermore, the success of the product design shows that there may be other applications for the design language developed by this project, such as webcams or other small, stationary devices. Further development and refinement of the internal component may reduce the difficulty and eliminate the need for this part to be pre-assembled and given to the user. This research clearly demonstrates the need for products designed with repair in mind and presents evidence of how this can be achieved in an affordable and accessible manner. The accuracy of the final products from user-built devices will undoubtedly vary. Additionally, over time, the placement and orientation of such devices may be altered due to improper installations. These issues can be effectively mitigated by utilizing deep learning and other AI methods to identify calibration-related problems quickly. Conceptualization, A.H., A.M., V.W. and S.J.P. ; methodology, A.H., A.M. and V.W. ; software, A.M.; validation, A.M., A.H., V.W. and S.J.P. ; formal analysis, A.M., A.H. and V.W. ; investigation, A.M., A.H. and V.W. ; resources, A.M., A.H. and V.W. ; data curation, A.M. and A.H.; writing--original draft preparation, A.M. and A.H.; writing--review and editing, A.M., A.H., V.W., A.Y. and S.J.P. ; visualization, A.H. and V.W. ; supervision, A.M., V.W. and S.J.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and was approved by the Tasmanian Ethics Committee at the University of Tasmania (protocol code 28408, approved on 29 September 2024). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

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