That is true. We did follow the design control process for capstone where we first wrote up the customer requirements, design requirements, test plans, and evaluation of the test plans. These are basically the Project Proposal, Design Development Plan(DDP), Design Input Document(DID), and Design Specification Document(DSD) that is within the DHF file for the medical device. Back then, I did not really understand why we had to write up all these documents because developing and making the project itself was already hard enough. But now I understand that we were following the proper documentation required from the FDA and by adhering to these procedures, we can make a well-organized and put together DHF file for our capstone project(which in turn was our final report). Thus, we were demonstrating the regulatory compliance requirements which is as important as designing and developing the project into a working product. 

@atk27njit-edu To add detail (we were in the same Capstone Group) the project focused on developing an upper extremity spasticity simulator to help educate clinicians and reduce inter-rater variability in the Modified Ashworth Scale (MAS). The one thing within documentation was really how subdivisions can form within your team. It came to us logically, but looking back we all focused on different practices, some did more on our NSF-I corps program for market research's, some of us did test planning and requirement analysis, some of us focused on larger Gantt chart aspects with weekly deliverables, etc... Comparing of how actual companies perform and what and why we did before is relatable to what your saying. Obviously we didn't have a in depth application but I will call the overall experience of capstone as a success purely due to exposure and front line usage in documentation, in our conversation and of course our development. 

Looking back there are so many ways to improve the device, from scaling out of the MAS limitation to employ corrective measures based on torque feedback to correspond to true spastic behavior instead of linear feedback to even things as simple as a machined structure to offset it deteriorating a couple months down the line. Therefore I do believe a market can be sustained but not with our ending product, but as a guideline to what comes next. 

My senior year's capstone or design project was the development of a computational pipeline to help sort and categorize different cell types into its biological classifications. Our market research involved defining a target market, which in this case would be researchers in both academia and private industry. Because our 'product' was not a physical tangible device but rather a software tool, it is harder to define an SOP for its usage other than to produce code that works. Progress of its development was documented to provide milestones for the different functionalities that we wanted our code to have, as well as timelines for periodically evaluating and squashing any bugs or issues that may occur when adding different functions or code changes. Our timeline was relatively flexible in the sense that we could afford to have delays in our development given the comfortability of our team's skills in code. Even so, the distribution of our product is relatively simple given it is digital.

For my capstone project, my team and I are currently developing a syringe mixer and pumper designed to evenly distribute microbubbles—specifically, mono-lipid bubbles containing treatment—within a saline solution. The primary challenge we have encountered has been related to motor issues, which have stalled our progress in achieving the desired functionality. We are actively working to resolve these issues to ensure that the device can operate efficiently.

If we successfully overcome these technical challenges, our device would fill a unique niche in the market, as there currently isn't anything similar available. This opens up the possibility of patenting our design, which could provide a competitive edge if there is a demand for it. However, I remain uncertain about the market potential at this stage, especially since the targeted application of drug delivery via IV is still being explored.

Nonetheless, if our device proves effective, there could be opportunities for commercialization, particularly as interest in advanced drug delivery systems grows. With the right marketing strategy and increased demand, I believe there is potential for profitability in bringing this innovative solution to market.

For my capstone project, my team and I designed and developed a mechano-myography (MMG) armband that was able to track the mechanical vibrations of the upper arm while also measuring the changes in circumference during a muscle contraction. The armband consisted of 10 accelerometers, that were utilized to capture the mechanical vibrations in the X, Y, and Z direction, and a flexible rubber sheet to measure the change in circumference of the upper arm. A microprocessor, which was situated on the shoulder of the patient, controlled the sensors and relayed the collected data to a computer for further analysis. The primary objective of our project was to be able to use the MMG Armband to estimate the force output of the patients arm through a predictive model. This model was constructed by using the data from the 10 accelerometers, flexible rubber sensor, and torque measurements from a Biodex system during bicep contractions. By integrating these diverse datasets, the predictive model was able to provide accurate estimation of force output. 

The biggest struggle that we faced as a group was getting the flexible sensor to function. It had been very difficult to get the flexible sensor to stretch during each contraction because it didn't exactly wrap perfectly around the arm. We had to design the casing for each accelerometer to hold the flexible sensor on the bottom while also keeping each ends of the sensor stable and not moving, so that the middle could stretch.

Lastly, this product has never actually been on the market, therefore there wasn't any true competition for our device other than EMG Armbands, which use electromyography to measure the electrical activity of a muscle. As a result, I do believe that we could have made some form of money if we were to continue with our design because it was the first of its design. 

My capstone project focuses on creating a chitosan based sulfated polysaccharide hydrogel for wound healing, specifically aimed at larger cutaneous wounds, including the potential for diabetic foot ulcers. There is a gap in the market regarding diabetic wound care, as the need for faster, more comprehensive wound healing is needed as these wounds a re very difficult to treat. Our capstone project provides a formulation that we assert will accelerate healing, have antibacterial properties, and also provide an oxygen rich environment. The key components of this project relate to different requirements in healing, such as oxygenation. Oxygenation of a healing wound is particularly important as it helps the wound progress through the stages of wound healing. How are we going to test this? Our team designed a fibroblast adhesion test as well as adhesion test to see how the cells interact with the product. Unfortunately, the project faced issues in achieving structural integrity for the hydrogel, which affected its usability. Our team had to continually adjust ratios for the formulation in order to find the best consistency appropriate for our healing hydrogel. We ended up increasing citric acid as a crosslinker and controlled evaporation were explored for solutions, but these improvements were minimal. Our project underlined the significance of testing materials and constantly improving the design. That being said, improving Sulf-Aid's potential success in the market would require focusing on the structural integrity and production expenses. Incorporating material scientists into collaborations could be advantageous for upcoming advancements.

My capstone project focused on using machine learning to detect defects in roll bearings. We trained models on a variety of datasets to identify early signs of bearing wear and tear. One challenge we faced was balancing model complexity with real-time performance since some models took too long to process data for practical use. Despite this, the project showed potential for industrial applications, especially with predictive maintenance becoming more popular. With further optimization, I believe this could be a viable tool in the market.