Prototyping a Hyperspectral Camera for Fungal Infection Detection

In our design thinking subject, I started an interesting exploration to develop a low-fidelity technology, a hyperspectral camera that can be used to diagnose infections caused by fungi on human skin. This process enabled me to learn about creativity, problem-solving, and teamwork. In this reflection, I will provide a factual account of the experiences, analyze the observations made, assess the impact, and consider how this learning may be useful in the future.

Objective Description: The Prototyping Process

While developing the prototypes, I focused on learning all the details about hyperspectral imaging. For the first time, I witnessed raw data being converted into spectral signatures, and I was able to view skin features that were not easily discernible to the human eye. We devised several scenarios based on medical imaging and environmental monitoring with my team members. We drew ideas on paper, played with parts, and experimented with basic arrangements. Watching our low-fidelity prototype come to life was exciting and somewhat shocking to the system.

Insights and Connections

The more I researched, the more I associated our work with other learning processes. As with data science, hyperspectral imaging follows three processes: feature extraction, classification, and pattern recognition. This hypothesis that fungal infections have distinctive spectral patterns influenced our design. It raised curiosity, joy, and slight anxiety within me at the same time. Can our prototype reliably distinguish between various infections and healthy skin? This was the link to my computer application and data science courses, the synergy of theoretical learning and practical paradigm.

Effectiveness and Value

There were drawbacks to our prototype, including low resolution, noise, and difficulties in calibration. Nevertheless, it was helpful in generating discussions. I realized that having prototypes similar yet unique from one another was inspiring to see among my teammates. Their views on the same concept were different. The value was not just in the solution delivered at the end but in the process – the mistakes, the refinements, and the learning.

Benefiting My Course, Career, and Life

1. Course Enhancement

• These hyperspectral principles will be useful in data preprocessing, feature extraction, and anomaly detection when it comes to software development and data science.

• Knowledge about low-fidelity prototyping influences Agile approaches and user-centric design.

1. Career Growth

• Other fields linked to hyperspectral imaging are remote sensing, agriculture, and health. This knowledge opens up opportunities for me in the career aspect.

• Partnership and flexibility, which are at the core of prototyping, are portable skills.

1. Life Beyond Academia

• These hyperspectral insights can be applied not only in coursework but in other ways as well. I will engage in interdisciplinary work, participate in open-source communities, and remain perpetually inquisitive.

Conclusion

The development of a hyperspectral camera prototype was not just about crafting a model; it was about changing a culture. When applying the acquired knowledge to future projects, I will keep in mind that innovation does not only reside in a clean, perfect solution but in the process that precedes it.